Dynamic HRA for Surgery applications: development of … · Figure 2: List of GTT developed for...

POLITECNICO DI MILANO

School of Industrial and Information Engineering

Laurea Magistrale in Mechanical Engineering – Production

Systems

Dynamic HRA for Surgery applications: development of

a Dynamic Event Tree simulation tool for Robotic

Radical Prostatectomy

Supervisor:

Prof. Paolo TRUCCO

Co-supervisor:

Eng. Rossella ONOFRIO

Master Thesis of:

Eleonora Paola TOFFOLO

Matr. 837522

Academic Year 2016/2017

TABLE OF CONTENTS

TABLE OF CONTENTS .......................................................................................I

TABLE OF CONTENT: FIGURES ................................................................... IV

TABLE OF CONTENT: TABLES ..................................................................... VI

ABSTRACT .......................................................................................................... 1

EXECUTIVE SUMMARY ................................................................................... 3

INTRODUCTION ............................................................................................... 17

CHAPTER 1: HUMAN RELIABILITY AND RECOVERY ANALYSIS IN

INDUSTRIAL AND HEALTHCARE SECTORS ............................................. 22

1.1 Human Reliability Analysis: from industrial to healthcare sector ....... 22

1.1.1 What is Human Reliability Analysis about? ...................................... 22

1.1.2 Role of human cognition in HRA ...................................................... 23

1.1.3 The definition of Performance Shaping Factors ................................ 25

1.1.4 Surgical environment peculiarities and current state of HRA application

..................................................................................................................... 26

1.1.5 Strengths and flaws of HEART thechnique ....................................... 28

1.2 Recovery analysis as a development of HRA second generation ............. 34

1.2.1 The concept of Recovery in System Safety Engineering ................... 34

1.2.2 How to model recovery: IFs and Dependency ................................... 36

1.2.3 The relevance of recovery paths in Surgery ....................................... 39

1.2.4 Applications of recovery analysis in literature ................................... 40

1.2.5 Current gaps in literature .................................................................... 44

1.2.6 Further developments in the Healthcare sector .................................. 45

CHAPTER 2: DYNAMIC RISK ASSESSMENT AND DYNAMIC EVENT

TREES ................................................................................................................. 47

2.1 Dynamic generation HRA ......................................................................... 47

2.1.1 From static to dynamic analysis ......................................................... 47

2.1.2 Historical Evolution of dynamic HRA in Industry ............................ 48

2.1.3 Simulation tools: benefits and challenges .......................................... 53

2.2 The crucial role of PSFs: properties and behaviour over time .................. 57

2.3 Dynamic Event Trees as a tool to formalize system/procedure evolution 60

2.3.1 Introduction ........................................................................................ 60

2.3.2 The five characteristics of DET ......................................................... 60

2.3.3 Industrial applications of DET ........................................................... 62

2.3.4 Gaps in literature ................................................................................ 67

2.3.5 Further developments in the Healthcare sector .................................. 68

2.4 Study objectives ........................................................................................ 72

CHAPTER 3: THE EMPIRICAL SETTING ..................................................... 74

3.1 Introduction ............................................................................................... 74

3.2 Minimally Invasive Surgery ..................................................................... 75

3.3.1 DaVinci Robot ................................................................................... 78

3.3 Robotic Surgery ........................................................................................ 82

3.1.1 Benefits and limitations ................................................................ 84

3.3.3 Robot applications ............................................................................. 86

CHAPTER 4: STUDY METHODOLOGY ........................................................ 91

4.1 Introduction ............................................................................................... 91

4.2 Dynamic Risk Assessment - preliminary phases ...................................... 92

4.2.1 Task flow diagram and recovery paths .............................................. 93

4.2.2 IFs and IFs’ impact definition ............................................................ 93

4.2.3 Modified HEART and integration with the DET framework .......... 103

4.3 Dynamic risk assessment implementation .............................................. 113

4.3.1 DET as a tool to integrate nominal probabilities procedures and paths

................................................................................................................... 113

4.3.3 Critical tasks identification .............................................................. 115

4.4 Illustration of the simulation procedure .................................................. 116

4.5 Factor Analysis ........................................................................................ 118

CHAPTER 5: CASE STUDY ........................................................................... 120

5.1 Introduction ............................................................................................. 120

5.2 Surgical Technique ............................................................................. 121

5.3 Application of the proposed Dynamic HEART Methodology ........... 124

5.3.1 Application of HEART technique ............................................... 126

CHAPTER 6: RESULTS .................................................................................. 128

6.1 Numerical analysis of the simulation results ........................................... 129

6.2 Probability Density Functions of Patient Grade Outcomes .................... 140

CHAPTER 7: CONCLUSIONS........................................................................ 145

7.1 Theoretical implications and future research .......................................... 147

7.2 Implications and relevance for practitioners ........................................... 150

REFERENCES .................................................................................................. 153

WEBSITE REFERENCES ............................................................................... 157

ACKNOWLEDGEMENTS ....................... Errore. Il segnalibro non è definito.

APPENDIX 1: Tools used for RARP procedure .............................................. 158

APPENDIX 2: Validated Task Analysis of BA-RARP procedure ................... 160

APPENDIX 3: Validated Task Analysis-Parallelism between tasks performed at

console and those at the table ............................................................................ 163

APPENDIX 4: Contributing factor classifications in the human factors

classification framework for patient safety (Mitchell et al. 2016) .................... 168

APPENDIX 5: Simulation Tool’s Script (Matlab®) ........................................ 172

APPENDIX 6: Matlab® functions ..................................................................... 178

APPENDIX 7: Questionnaire Results ............................................................... 180

TABLE OF CONTENT: FIGURES

Figure 1: Ranking by yearly death (Makary et al., 2016) ................................... 17

Figure 2: List of GTT developed for NARA ...................................................... 31

Figure 3: List of EPC developed for NARA ....................................................... 32

Figure 4: List of quantified GTTs developed for NARA ................................... 32

Figure 5: List of EPC developed for CARA ....................................................... 33

Figure 6: The uses of simulation and modelling in HRA ................................... 55

Figure 7: Proportion of use of MIS, Robotics and Open procedure in different

setting .................................................................................................................. 78

Figure 8: Typical set-up of robot system in operating room (a) sketch (b) real-life

............................................................................................................................. 80

Figure 9: International increase of DaVinci surgical procedures ....................... 87

Figure 10: Increase of DaVinci speciality surgeries in recent years ................... 89

Figure 11: Plots of the triangular pdf of IFs in surgery ...................................... 97

Figure 12: Flowchart representing main steps of traditional HEART methodology

........................................................................................................................... 104

Figure 13: Pdf distributions for the "homogenous" case .................................. 114

Figure 14: Phases for the Critical task identification ........................................ 116

Figure 15: Sequence of the procedure simulated by the tool ............................ 125

Figure 16:The probability of a Grade 0 outcome for the 0.95 percentile of patients

........................................................................................................................... 133

Figure 17:The probability of a Grade 3 outcome for the .95 percentile of patients

........................................................................................................................... 134


........................................................................................................................... 135

Figure 19: Grades’ PDF for the complete set of simulation runs ..................... 141

Figure 20: Grades’ PDF for the "only IF 1" set simulation run ........................ 141




Figure 24: Grades’ PDF for the "only IF 10" set simulation run ...................... 144

Figure 25: Grades’ PDF for the "NO IF " set simulation run............................ 144

TABLE OF CONTENT: TABLES

Table 1: Taxonomy for the IFs in Surgery- high technology content (Onofrio et

al. 2015) .............................................................................................................. 27

Table 2: Recovery influencing factors (RIFs) (Subotic et al. 2007) ................... 38

Table 3 Literature review of dynamic HRA applications ................................... 49

Table 4: DaVinci surgical procedures ................................................................. 88

Table 5 : Validated surgical taxonomy of Influencing Factors ........................... 94

Table 6: Comparison between HEART, NARA, and CARA multipliers ........... 98

Table 7: Comparison between modified HEART multipliers and new ones .... 102

Table 8: Generic Task Types (GTTs) and relative Nominal Human Unreliability

(NHU) ............................................................................................................... 105

Table 9: HEART 38- Error-Producing Conditions (Williams, 1986) ............... 106

Table 10: Flowchart representing main steps of traditional HEART methodology

........................................................................................................................... 106

Table 11 Comparison between IFs’ taxonomy and traditional EPC one .......... 107

Table 12: Benefits of robotic prostatectomy over open and laparoscopic surgery

(http://roboticprostatesurgeryindia.com/) ......................................................... 123

Table 13: Outcomes following robotic radical prostatectomy in the select reported

studies ............................................................................................................... 123

Table 14: EMs’ probability range definition .................................................... 130

Table 15: EMs' grade range definition .............................................................. 130

Table 16: Probability of having the 95% of patients respectively with the

minimum and maximum grade possible ........................................................... 131

Table 19: Analysis of IF clusters' impact: probability of Grade 0 for the 0.95

percentile of patients and of Grade 3 for the 0.05 percentile of patients .......... 137

Table 17: Clavien-Dindo grading system for the classification of surgical

complications .................................................................................................... 181

1

ABSTRACT

La sicurezza del paziente e la prevenzione di danni dovuti ad errori medici,

diagnostici o terapeutici, è da sempre uno i temi prioritari in ambito sanitario; il

fenomeno è oggi ancora più accentuato dal crescente livello di informazione e

presa di coscienza dei pazienti che chiedono, con sempre maggior forza, più tutela

e certezze.

I dati riportati da diversi studi, tra cui il più recente sviluppato dalla Johns Hopkins

University of Medicine (Makary et al., 2016), confermano che la morte a causa di

errori medici è al terzo porto nella classifica delle cause di decesso negli Stati

Uniti, e si ha ragione di pensare che questo risultato sia facilmente trasponibile su

scala mondiale ad altri paesi avanzati.

Il concetto di “errore medico” ha subito diverse interpretazioni nel corso dei secoli

e si può definire come “un trattamento medico che sposta il livello di rischio al di

fuori dei margini di accettabilità di insuccesso suggeriti dalla pratica medica,

provocando danni al paziente”.

In un’ottica di continua evoluzione per migliorare le cure e la sicurezza in sanità,

si conferma la necessità di applicare tecniche di analisi del rischio, ed in

particolare di valutazione del rischio legato alla componente umana (Human

Reliability Analysis, HRA), al fine di poter implementare azioni correttive e/o

preventive, e di ridurre la vulnerabilità del processo clinico affrontando la gestione

del rischio ad esso correlato con l’adozione di un approccio sistemico.

Il presente studio si propone di sviluppare e testare uno strumento di simulazione

dell’affidabilità umana specificatamente progettato per applicazioni mediche, e in

particolar modo per procedure chirurgiche.

L’integrazione della tecnica quantitativa HEART, propriamente modificata per

applicazioni mediche, e della struttura del Dynamic Event Tree (DET), ci hanno

consentito di sviluppare uno strumento di simulazione dinamica di una procedura

chirurgica, soggetta a possibilità di errore da parte del chirurgo, da cui ottenere

stime di probabilità per diversi livelli di esito sul paziente (outcome). Il metodo e

lo strumento predisposti sono stati testati in un contesto di chirurgia robotica per

l’esecuzione di una specifica procedura chirurgica, la BA-RARP. Lo studio ha

consentito di trarre rilevanti conclusioni riguardanti i fattori maggiormente

influenzanti il suo buon esito.

L’analisi quantitativa ha dimostrato che la condizione che più di tutte peggiora la

prestazione del chirurgo in sala operatoria è il rumore di sottofondo dovuto ad

interazioni tra il personale, o tra quest’ultimo e la strumentazione stessa, non

2

inerenti con la procedura in esecuzione. Mentre, dall’analisi condotta per

categorie di fattori l’aspetto più critico (tra quello Personale, di Team e

Organizzativo) è risultato essere quello inerente alle dinamiche di equipe,

ponendo così un accento sulle abilità di coordinamento, cooperazione e

comunicazione dello staff coinvolto.

Questo lavoro ha contribuito a ridurre il gap osservato in letteratura circa la

diffusione di tecniche di analisi di affidabilità umana nel contesto sanitario,

confermando in particolare le potenzialità della tecnica HEART nell’applicazione

in aree differenti da quella industriale; si auspica infine che questo studio possa

essere di supporto alla evoluzione della formazione dei futuri chirurghi robotici,

alla progettazione di procedure chirurgiche più sicure, così come di checklist e

scenari di simulazione per l’apprendimento.

In conclusione al nostro lavoro, a completamento dell’analisi svolta, sono riportati

ulteriori approfondimenti sui risultati ottenuti ed alcune proposte per il

miglioramento dell’organizzazione del lavoro e l’ottimizzazione delle risorse in

ambito medico; non di meno, le possibilità di sviluppo del filone di studio a cui

abbiamo fatto riferimento sono illustrate insieme a diversi suggerimenti per futuri

approfondimenti.

3

EXECUTIVE SUMMARY

I. Introduction

The entire line of study regarding Human Error Probability is based on the quote

by Alexander Pope “To err is human”. This statement encloses the main pillars of

Human Reliability Analysis and Safety Engineering in general: the harmfulness

and futility of blame culture, since errors are inevitable; and the need to relate

human errors with the mental processes laying behind them.

The importance of the role of humans is easily recognised in the design,

implementation, control, and maintenance of any safety-critical system; and

complex systems, like modern hospitals, rise major safety concerns because of

their potential for accidents with fatal consequences.

It is from these key points that the need for a systematic approach to the analysis

of human actions and to the assessment of human reliability is of growing interest

in many sectors, including healthcare. Several formal Human Reliability Analysis

(HRA) methods have been proposed in the last 40 years, with several applications

in Nuclear, Transport and Process industry.

Everyone who has ever dealt with Safety Engineering knows that the most

dreadful scenario, in terms of event severity, is the one involving human loss, so

it is straightforward to think about Healthcare, and specifically Surgery, as a

proper field of applications for this kind of analysis.

Trying to transfer HRA techniques to the Healthcare sector, we must consider all

the customizable aspects of such techniques in order to select the one that better

fits our case and to calibrate the variants according to the application under study.

To achieve a quantitative estimate of the HEPs, many HRA methods utilize

Performance Shaping Factors (PSFs), which characterize significant facets of

human error and provide a numerical basis for modifying default or nominal HEP

levels (Boring 2006). Consequently, after having selected the most suitable HRA

technique, it was fundamental to determine the set of PSFs to be involved in this

kind of environment through the definition of an ad hoc taxonomy, which required

a deep investigation of pre-existing literature, starting from industry to medical

4

and surgical related one, and a validation oriented work by means of surgeons’

interviews and judgements evaluation.

This line of research started at Politecnico di Milano a couple of years ago, and

some preliminary work was already faced in previous studies.

Two specific studies have already been produced on the topic of HRA adaptation

for Surgery. The first one (Onofrio et al. 2015) was more related to the taxonomic

aspect of the problem, while the second one (Trucco et al. 2017) proposed an

empirical application of a quantitative technique derived from an adjusted version

of HEART, together with the task analysis development and the taxonomy

validation for the specific case.

The scope of this work was to make a step forward introducing the possibility of

quantifying recovery probabilities and paths, so we had to further alter the

approach presented in previous studies, introducing this concept through the

support of experts for validating recovery paths, hypothesis, and data coming into

play, and for calculating the related probabilities.

Searching for new developments of HEART, we got into two main updates of this

technique having the objective of re-actualizing and specializing the general, and

in some sense obsolete, tool for different fields of application, such as Nuclear

Power Plant (NPP) and Air Traffic Management (ATC).

In fact, whilst this technique has served well, it was developed many years ago

and it has remained principally the same technique, based on the same original

data set (Kirwan et al. 2016). It was therefore felt that a redefinition of the Error

Producing Conditions (EPCs) involved, and of the relative multipliers, could be

developed based on more recent and relevant data; since these are the guidelines

for future researches, we opt for the adoption of their more recent taxonomies and

GTT definitions, readapted for Surgery application.

Reliability and performance management look at HRA database and techniques,

almost exclusively, as tools to prevent human errors and failures. However, if we

take a closer look and think of what we exactly want to prevent, they are the

consequences of a failure rather than the occurrence of the failure itself (Jang,

Jung, et al. 2016a). Coherently, the recovery of human errors is as important as

5

the prevention of human errors and failures. This consideration actually paves the

way to a complementary field of study concerning the fostering and the

investigation of recovery processes.

The integration of time dimension in human behaviour analysis is the logical

consequence of the investigation of human mental processes, and of the fact that

many of the so-called influencing factors are implicitly related to the timeline of

the process/system they describe. In this sense, dynamic risk assessment allows

more detailed analyses and in deep mapping of performance measures.

Nowadays, it is recognized that a number of Dynamic Event Trees and direct

simulation software packages for treating operator cognition and plant behaviour

during accident scenarios are being developed or are already available (National

& Falls 1996); in particular, simulation-based HRA techniques differ from their

antecedents in that they are dynamic modelling systems that reproduce human

decisions and actions as the basis for performance estimation.

The possibility to use simulation tools to run an unlimited number of scenarios

(virtually without actual humans once the configuration is initiated), and to obtain

almost instantaneous results, dramatically reduces the costs. Hence, the

opportunity to perform, and analyse, a wider spectrum of scenarios in a generally

easier and more cost-effective way is the principal benefits of this type of

technique.

When an individual encounters an abnormal event, the natural reaction often

includes physical, cognitive, and emotional responses (Chang & Mosleh 2007).

These three types of response also influence each other; and there is ample

evidence that they also affect problem-solving behaviour; so, it is evident that also

in the dynamic analysis case PSFs cover a crucial role in the estimation of human

behaviour and error probability.

In addition to these internal PIFs influencing cognitive processes and decision-

making attitude, there are external PIFs (e.g., organizational factors) affecting

individuals’ behaviour both directly and indirectly; and each of these, both related

to personal and environmental domains, can potentially evolve over time.

6

To cope with this new focus, different types of PSFs adjustments were proposed

and analysed; but, in order to take proper decisions and to make the result

compatible also with different application, it is important to understand the

fundamental role that the scenario involved has on the process.

This point is backed by numerous studies confirming the fact that the first step in

a Dynamic Risk Assessment is to identify the accident scenarios where it appears;

indeed, the interface and the interaction between the plant and its operators is

obviously described by a critical dynamic process together with crew cognition.

For what regards our study we will limit ourselves to the implementation of

already validated taxonomy considering the impact of the factors as constant, but

changing the IFs considered depending on the task involved: allowing discrete

change of IFs over time.

Between the dynamic HRA tools encountered during our literature review,

Dynamic Event Trees resulted of particular interest to us due to their extreme

flexibility and their ability to analyse scenario dynamics under the combined

effects of stochastic events.

A Dynamic Event Tree is defined by five key characteristics:

1. The branching set (level of detail);

2. The set of variables defining the system state;

3. The branching rules (to determine when a branching should take place);

4. The sequence expansion rules (to limit the tree expansion);

5. The quantification tools to compute the deterministic state variables (e.g.,

process variables).

And for each branching point, the quantification process involves four steps:

1. Evaluation of crew's cognitive state and of the nature/quality of the

information regarding the plant available to the team;

2. Qualitative evaluation of the conditional likelihood of each branch;

3. Initial determination of the conditional probability for each branch;

4. Comparison of the conditional probabilities for similar situations in

different parts of the tree, and adjustment.

7

When it comes to construct an event tree, at each branch of the tree a probability

value must be determined. This value can derive from expert judgments, as in our

case, or from data collected in databases, adaptable to the situation of interest.

Clearly, this kind of technique has the same drawbacks attributed to all studies

making extensive use of expert judgments. However, the last step of the ones

mentioned above (i.e. Comparing similar branches) greatly facilitates the

assessment because it enables the analyst to use information concerning the

relative likelihoods of scenarios and to perform a double check on the results

obtained and proposed.

II. Study methodology and results

In the chapter illustrating the study methodology adopted, scientific evidences and

illustration of the various boundary conditions involved in our work, and of the

methodology through which we quantitatively evaluated our model are provided.

The scope of the chapter mentioned before is to prove the consistency of our

methodology; indeed, the most important aspect of this part of the work was the

adoption of a systematic approach; that we have applied in tackling every aspect

of the case study.

The steps we addressed in order to justify our analysis were:

- The estimation of the Proportion of Affect (PoA) of the Influencing

Factors (IF);

- The individuation of the Error Modes (Ems) and the estimation of their

relative probabilities;

- The individuation of the Generic Task Type (GTT) involved in the

procedure according to HEART;

- The development of the algorithm for the calculation of the DET;

- The definition of the Patient Outcome classification.

Of course, for starting our work we had to undergo several preliminary phases

since the elements needed to implement a study like the one we are approaching

to are numerous; and the major issue we encountered in dealing with the

Healthcare sector was the lack of reliable data; so, making the extensive use of

experts’ estimate an only choice.

8

The modified version of HEART proposed here is the result of a series of

considerations and adaptation of the original version in order to make it more

suitable for Surgery application; and, as mentioned before, the innovative aspect

of this work, with respect to the versions presented in previous studies, consisted

in the introduction of 9 the Error Modes (Ems) stemming from the most critical

tasks (already individuated as “Isolation of lateral peduncles and of posterior

prostate surface”; “Santorini detachment from the anterior surface of the

prostate”; and “Anastomosis”), together with the association of Patient outcome

grades, according to the Clavien-Dindo classification for Patient outcome (the

most widely accredited classification in the surgical sector), to each of the

recovery branches considered.

In order to identify the 9 most relevant recovery paths associated to these tasks we

collected the opinion of three surgeons through standardized and ad-hoc

interviews. Experts’ judgements were also employed for the evaluation of PoA;

for which we made reference to the IFs’ triangular distributions brought up by the

work of the PhD of the Politecnico di Milano, Rossella Onofrio, specifically

oriented in the direction of creating a statistical ground for the definition of

HEART’s weights in Healthcare.

As said before, in our study, as in (Trucco et al. 2017) one, surgeons were

responsible for those steps more “judgmental and structured”: selecting the

appropriate Nominal Human Unreliability (NHU) category, selecting the

Influencing Factors (IFs) from the surgical validated taxonomy and their

corresponding Assessed Proportion of Affect (PoA), plus the definition of the

Error Modes possible for each critical task and their relative probabilities (alpha).

Since the results of a survey significantly depend on the assessor’s knowledge of

the task and his personal opinion, the three surgeons involved in the study were

all well experienced, well trained, and aware of the steps and order of the

procedure.

Integrating the set of formulas of the modified HEART technique for Surgery with

a DET structure, a tool able to randomly generate probable paths for the procedure

was set up; and the Matlab® code resulting can be ideally divided in three main

parts:

9

- Initialization of data;

- Quantitative evaluation of paths (iterative part);

- Grade’s probability distribution evaluation.

The surgical procedure to which we have applied our model is the BA-RARP, a

revolutionary version of the traditional Robot-Assisted Radical Prostatectomy

(RARP), which has its only point of access through Douglas, so without opening

the anterior compartment and the endopelvic fascia, and without the need to

dissect the Santorini plexus (Galfano et al., 2010).

The following graph represent the structure of the DET we actually worked with

and highlights the sequence of the procedure together with the final Patient

Outcome grade associated to each deviation.

In the quantitative phase of the work, surgeon’s unreliability for the sequence of

Critical Tasks has been estimated by applying the modified dynamic HEART

10

technique in the evaluation of the DET’s nodes. Specifically, the following issues

have been addressed:

- Initialization of the Assessed Proportion of Affect, which gives a measure

of each EPC/IF effect magnitude;

- Initialization of the Assessed Nominal Likelihood of Unreliability

(ANLU) for the Critical Tasks “Isolation of lateral peduncles and of

posterior prostate surface”;” Santorini detachment from the anterior

surface of the prostate”, and “Anastomosis”;

- Identification of the Error Modes (Ems) undergone for each simulation,

i.e. paths, and evaluation of the branches’ probabilities through the

adoption of a linear additive model and the modified HEART’s set of

formulas;

- Identification of the final Patient Grade Outcome, according to Clavien-

Dindo classification;

- Calculation of the probability distribution of each Patient Outcome Grade

for the selected procedure, holding the Central Limit Theorem.

Once obtained the probabilities for the different grades, we performed a factor

analysis to investigate the effect of the various IFs considered in the calculation

on the probability of success of the surgery, and in particular on the health and

recovery of the patient. Through the simulation tool it was possible to select all

the variables, and so paths, in a completely independent and random manner for

20,000 iterations so that, holding the CLT, the resulting probabilities have global

validity.

According to the questionnaires collected, the worst possible scenario for a patient

undergoing this type of surgery (i.e. BA-RARP) is the Grade 3 outcome (i.e.

“Requiring surgical, endoscopic or radiological intervention”); and the results

obtained for the evaluation of the quantiles (q=0.95) of the optimum outcome, i.e.

no deviation from standard procedure (Grade 0), ant the maximum expected

degradation of patient outcome one (Grade 3) show that the more impacting factor

on the performance of the surgeon in the operating theatre is, by far, IF 1 (i.e.

Noise and ambient talk); followed by IF 5 (i.e. Poor management of errors and

threats to patient safety) tied to IF 10 (i.e. Poor or lacking coordination), IF 7 (i.e.

11

Rude talk and disrespectful behaviours), and IF 9 (i.e. Unclear or failed

communication).

We should have expected IF 1 to be the factor more heavily impacting on

surgeon’s performance in terms of Grade 0 quantile (-3.54%), since it has been

considered to describe all the three Critical Task under exam. Even though, it is

well known that background noise is a very relevant disturbing factor, the effect

produced from IF 1 on Grade 0 is also stressed by the way in which the software

evaluates the final grade of the procedure; in fact, in order to get the no deviation

case, we need to undergo a no deviation case for all the tasks involved, otherwise,

the highest grade encountered will be selected as the resulting one.

The same considerations can be done, on a different scale since they are taken into

account just in CT 1 and 3, for IFs 5 and 10, which share the same order value

(around 99.0%); and to IF 7 and IF 9, considered only in one of the three tasks

(aroung 99.4%). In the graph below the results regarding the probability of a

Grade 0 outcome for the 0.95 percentile of patients is displayed.

Probability of a Grade 0 outcome for the 0.95 percentile of patients

Analysing the probability of a Grade 3 outcome for the 0.95 percentile of patients,

the a priori consideration we made was that the only task presenting the possibility

of ending with this severity level is task 1; hence, only those factors affecting the

first CT (IF 1, IF 5, and IF 10) were supposed to have an impact on this KPI.

This was confirmed by the simulation results from which we could appreciate the

fact that considering only those factors not involved in CT1 evaluation (IF 7 and

99,71 99,48 99,39 99,03 98,72

96,46

93,47

90

91

92

93

94

95

96

97

98

99

100

101

No IF IF 7 IF 9 IF 10 IF 5 IF1 Complete

Grade 0 (q=0.95)

12

9) we ended up with a Grade 3 probability around 0.001% (i.e. the same resulting

from the NO IF case), while we had very similar results for IF 1, 5 and 10, all

involved in CT1 evaluation.

In order to provide clearer and sounder figures, we also decided to evaluate the

probability of a Grade 3 outcome for the 0.05 percentile of the patients, so the

probability for the 5% of the patients to end up with a Grade 3 outcome and the

results obtained from this investigation demonstrated that this probability is

around the 0.03%; the relative results are shown in the histogram below..

Probability of a Grade 3 outcome for the .05 percentile of patients

Finally, the scenario analysis was developed in order to make some reasoning

about the relative importance on BA-RARP of different categories of Influencing

Factors, namely: Team, Organizational, and Personal factors.

Analysis of IF clusters' impact: probability of Grade 0 for the 0.95 and Grade 3 for the 0.05

percentile of the patients

Patient

Outcome Complete

Team

(IF 1, 7, 9, 10)

Organisational

(IF 5)

Personal

(IF 9, 10)

Grade 0 93.47 % 94.58 % 98.72% 98.38 %

Grade 3 0.0324 % 0. 0221 % 0.0196 % 0. 0109%

0,003 0,0031 0,00420,0056

0,00730

0,0196

0,0324

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

NO IF IF 7 IF 9 IF 10 IF 5 IF1 Complete

Grade 3 (q=0.05)

13

As we can appreciate from the diagram above, resulting from the scenario

analysis, the category most impacting on the end result of the surgery is the one

related to Team and Teamwork conditions; secondly the Organizational one; and

finally, the one concerning Personal factors. Another interesting point is made by

the fact that the “Complete” scenario is much more similar to the “Team” one than

to the “Organizational” and “Personal” ones; which means that the first category

is the one better describing, and so more heavily influencing, the outcome of the

realistic case.

II. Conclusions

This study allowed the development, testing and validation of a simulation tool

based on Dynamic Event Tree theory and structure adopting a modified HEART

methodology for application in the Healthcare sector; and, through the running of

the simulation of the procedure’s simplified version, we have been able to validate

the correct behaviour of the tool designed.

The attention was directed to the analysis of surgeon’s unreliability in robotic

surgery, since it is an innovative sector where Minimally Invasive Surgery enables

optimizing precision, speeding up recovery, and potentially reducing human

errors. Still, since for now and the near future the robot does not replace the

surgeon but only supports him in close cooperation and interaction, the analysis

and management of human error and the application of HRA techniques are

fundamental and necessary.

The state of art review underscored firstly the importance made by HRA

techniques in the few surgery applications developed and secondly the need to

reduce the gap of applicability between Industrial and Healthcare sectors.

Even though, the first baby steps have been done in this sense, the majority of the

efforts in the socio-technical complex system of healthcare organizations is

characterized by reactive approaches, strongly focused on the retrospective

analysis of adverse events, such as incident data analysis; while, it would be for

sure more interesting to develop that branch of HRA discipline concerning

anticipatory analyses, which would represent a new twist in Healthcare helping in

14

the prediction, and hopefully elimination, of system’s vulnerabilities without the

necessity of occurrence of the failures itself.

The introduction of a DET structure allowed the inclusion of a procedural

timeline, still not considering the influence of the passage of time; while the

update of the multipliers used in Healthcare specifically designed HEART

methodology, defined a step forward in terms of database, and so results’

accuracy.

There is still much work to do in order to get specific and wide ranging database

directly produced by experts and experiences coming from the Healthcare sector;

nevertheless, through specific assumptions we manage to benefit from the

developments gained by more advanced, for what regards safety studies, contexts.

For developing and improving this study, it is important that other procedures and

surgical settings could experience this modified methodology and proactive

simulation approach, enhancing its diffusion, so that this work does not remain a

mere exercise of study.

This investigation represents a first step for the inclusion of dynamics in HRA

techniques for surgery applications and a few suggestions for future developments

could be:

• The description of the evolution over time of the Influencing Factors

involved;

• The dependences existing between the tasks composing the sequence of

the procedures and the IF/ECP themselves;

• The investigation of the cognitive models underlying surgeons’ behaviour

in order to develop high-performance simulating tools;

• The investigation of the recovery paths and of factors specifically designed

for recovery scenarios peculiarity.

A better modelling of the aspects mentioned before would constitute a valuable

consolidation of our study. In this way, quantitative consideration of goodness for

recovery strategies could be formulated, so to refine educational tools and

packages, and the whole Hospital system would benefit from this line of research.

15

The introduction of MIS has marked the beginning of a proper revolution in the

Surgical sector; and we hope that this work will support future training of robotic

surgeons and the design of new procedures and checklists; but most of all, that the

immediacy of use of simulation tools will foster the evolution of operating room’s

environment and organization.

The turning point represented by the kind of technology we issued consists in the

possibility of manipulating those factors actively, or passively, influencing human

behaviour and putting them in relation with the probability of success of the

surgery and its probable outcomes.

Still, the most hampering factor in the development of HRA techniques in

Healthcare is the lack of reliable data; we expect that the continuous theoretical

development, and the increasing ease of use and effectiveness of this kind of tools

will get the attention of surgical, and in general medical, world.

The study highlights the major factors, or class of factors influencing surgeons’

performance. Therefore, it is important to take that information into account and

to try to reduce their effect by raising surgeons’ awareness about errors promoting

conditions and implementing improvement actions.

This work represents a useful contribution to technology providers, paving the

way to the introduction of dependencies and recovery paths’ evaluation for HRA

applications in surgery. Thanks to the tool developed and tested in the present

study, performing a reliable and efficient simulation is more than ever affordable,

and the refinement and enlargement of the data involved would provide even more

precise and effective analyses, facilitating the optimization and improvement of

the operating room environment.

What is more fascinating of HEART and DET technique is their flexibility of

application to the most disparate fields of interest, and their adaptation from NPP

to Surgery environment is the prove that nowadays Safety Engineering is a

transversally valuable discipline for maximizing systems’ performances; which,

in the end, results in an improvement of work’s quality both from the point of

view of the worker and of the client/patient

16

For what specifically regards Robotic Surgery, it has not yet expressed its full

potential, and we expect future studies to introduce all those elements and

strategies already experimented in the industrial sectors (e.g. NPP, ATC)

producing a more comprehensive description of the phenomena occurring along

the procedure and a more accurate analysis of probabilities; with the hope of

seeing the spreading of the utilization for these methodologies and the increase in

awareness among potential users.

17

INTRODUCTION

Human error is the main cause of adverse events in Healthcare as demonstrated

by several epidemiological studies in the last two decades (Wilson, 1995; Schioler

et al., 2001; Vincent, 2001; Kable, 2002; Davis, 2002; Baker, 2004; Aranaz-

Andrés et al., 2008; Soop et al., 2009).

As shown in the picture below, Johns Hopkins University researchers have

estimated that medical error is now the third leading cause of death in the United

States after heart disease and cancer (Makary et al., 2016), and it reasonable to

think that the same trend remains valid on a global scale.

Human performance involves a complex interaction of factors, including the

inseparable tie between individuals, their equipment, and their general working

environment (Van Beuzekom et al. 2010).

Figure 1: Ranking by yearly death (Makary et al., 2016)

18

This criticality is emphasized in applications as Surgery due to the socio-technical

complexity involved in this field. Talking about complexity the operating room

environment is for sure the most challenging ambient in Healthcare because of the

increasing difficulty of the procedures; the highly interdependent multi-

professional staff; the sophisticated and technological equipment; the time

constraints; the stressing environment; and the occurrence of unexpected

situations due to the unstable and critical conditions of the patients.

Operating rooms are one of the most complex areas in healthcare where adverse

events are frequently seen with the rate of 47.7% to50.3%; this conclusion is

supported by numerous statistics showing higher number of adverse events

reported in surgery with respect to other clinical specialties (Brennan, 1991;

Anderson et al., 2013) and by the pace of recent developments, which suggests

that the practice is becoming both more complex and more tightly coupled

(Perrow, 1999). The study conducted in 2011 at the Acibadem University School

of Health Sciences, in Turkey, which involved OR staff (including physicians,

nurses, anaesthesia technicians and perfusion technicians) reported that the 65.2%

of health professionals witness condition of patient safety threatening event in the

OR throughout their professional life (Ugur et al. 2016).

Although the best measure for safety performance in Healthcare is still not

defined, except for patient outcomes, it is clear that, given the extreme complexity

of the problem, it is impossible to express a safety score as a single figure (Van

Beuzekom et al. 2010).

Influencing factors analysis has been considered as one of the most important

dimension of HRA in Surgery (Joice et al., 1998). In the last decades, many efforts

have been spent in the detection and analysis of the factors influencing tasks’

outcomes and incidents’ occurrence by performing systematic analyses of

databases and by comparing different organizational assets; first and foremost,

within the industrial framework but lately many investigations have also been

carried out in Healthcare settings. On the other hand, the attempt to combine one,

or more, of the many HRA techniques developed in industry to a not automated,

but at the same time extremely technical application, as Surgery is can be very

demanding and cumbersome because of the operative discrepancies between the

19

two worlds and the need for a confrontation between experts with different

backgrounds.

The first step for integrating HRA techniques in the new field of application

consisted in determining ad-hoc taxonomy for Safety in Surgery, whose

development represented a turning point for specialized studies providing solid

basis from which more practical investigations had the opportunity to stem.

After this initial effort, other important steps forward have been made, for

instance, in the determination of distribution curves for PSFs’ effect estimation by

expert judgments, in order to consider the variability of influencing factors’

perceived impact, and so paving the way for quantitative and simulation based

analysis, which are nowadays the new front line of risk analysis’ innovation.

HRA techniques are responsible for the identification of the weak points of a

system and of their classification and ranking in order to allow a proper

distribution of efforts and resources; and the last generation of techniques for this

discipline, the so called third generation HRA, proposes to integrate traditional

and/or new techniques with simulation tools to better model and express the

interaction between the system, the procedures, and the operators involved.

In general, this kind of techniques is more complex and, since it aims at capturing

and modelling as many peculiarities of the systems as possible, it tends to be less

flexible; and this lack of flexibility arises the need for specific development of the

methodology for each application.

For what concerns the Healthcare sector itself, we are forced to operate in

conditions very far from ideal ones. The majority of the problems arises from the

paucity of past data which makes the outcomes’ comparison between old and new

techniques impossible. But this is just one of the drawbacks of the blame culture,

still very strong in this kind of environment, which has generally lead to a dearth

of comprehensive and transparent accidents’ reports.

Anyway, it is noteworthy that in recent years the process of implementation of the

‘‘just’’ culture has started. For example, in the ATC sector air traffic controllers

are not punished for actions, omissions or decisions that lead to any safety-

relevant occurrence as long as these occurrences are reported via an appropriate

20

occurrence reporting scheme; and, hopefully, this initiative will create a more

trustworthy environment in which controllers are willing to report any safety-

related incident without fear of any disciplinary action (Subotic et al. 2007).

It is easy to understand that the lack of proper data hampers the optimization

process, making the foundations of the model and of the consequent calculations

fragile; but it is obvious that changing the mindset of a significant portion of the

population is beyond the scope of this work and will require years, so we will

focus on how to improve the methodological approach to the matter.

To this end, it was necessary to get around the problem figuring out new strategies

to gather experts’ opinion, and thus, finding a way to make those judgments, and

their sum, as much objective as possible. In particular, our idea was to question a

group of surgeons about safety, safety procedure, and affecting factors in a precise

and standardised manner (which will be presented in the Study Methodology

Chapter) and then translate their opinions numerically through a well established

procedure.

As mentioned above, literature review showed a recent and growing interest for

Safety Engineering methodologies, and the fact that HRA is gaining more and

more credibility and relevance in the medical setting is due to the fact that the

historical revolution it would bring to this field is becoming undeniable.

Nowadays performing HRA has become a mandatory requirement and a

fundamental element for a continuous improvement of safety in hospitals; it is

adopted worldwide whilst many details change basing on the country legislation

and on the department of pertinence.

The benefits of transferring and applying to Healthcare services the most

important proactive risk analysis methods already implemented in industry are

fully recognized in patient safety literature (Vincent, 2001; Lyons, 2004; Verbano

et al., 2010, Cagliano et al., 2011). On the other hand, as mentioned before the

higher variability, and sometimes complexity, of Healthcare operations in

comparison to industrial ones represents a big obstacle for the implementation of

HRA techniques in Healthcare.

21

The issue of applicability of HRA in Healthcare is largely discussed in literature,

and the large majority of the studies tries to modify and adapt existing techniques

to the clinical setting of interest by producing specific templates, procedure and

flow charts; but the biggest challenge in this sense is to make the methodologies

as much system-based as possible in order to make users more sympathetic to use

them.

As we expected, nothing was found in literature concerning quantitative dynamic

probability analysis for Surgery or Healthcare applications, so the objective of the

study was to put together all the information and results achieved up to now, and

to propose a simulation tool able to integrate the structure of a Dynamic Event

Tree with the flexibility of a quantitative tool such as HEART; so not losing fit

with the specific application but, at the same time, introducing more sophisticated

analysis’ methods.

The manuscript is divided into 7 chapters, as follows. In Chapter 1 we have

provided and introductory view of the area of investigation and its relevance from

both practical and scientific perspectives. Chapter 2 deals with the illustration of

the main findings in Dynamic HRA literature, not only concerning this study but

also looking at future developments. Chapter 3 describes the empirical setting we

will operate with, providing an overview of Robotic Surgery and technology and

applications. Chapter 4 introduces the study methodology we have adopted for

our work: the sequence of steps we went through, the assumptions made, the tools

and the classifications employed, and the backbone of the quantitative device

developed. Chapter 5 illustrates the process of customization of the resolving

structure, described in the preceding chapter, to the specific case of study:

initializing the variables and the quantitative data the evaluation will be based on.

In Chapter 6 the results obtained from the running of the simulation tool are

illustrated and commented.

Finally, in Chapter 7 the main conclusions are drawn along with suggestions for

future research endeavours.

22

CHAPTER 1: HUMAN RELIABILITY AND

RECOVERY ANALYSIS IN INDUSTRIAL

AND HEALTHCARE SECTORS

1.1 Human Reliability Analysis: from industrial to healthcare sector

1.1.1 What is Human Reliability Analysis about?

The study of safety as an attribute of system is a relatively new interest. It started

roughly in the 80’s, and this is no coincidence since it was in those years that, due

to the increasing complexity of systems and to the furious rate of innovation, the

most destructive accidents took place.

Disasters like the one of the Three-Mile Islands (TMI) on March 28 1979 made

the need for a structural role of safety in industry more evident than ever, fostering

the development of standards and risk evaluation techniques until then confined

to the military field.

From that moment on, Safety Engineering became a field of study itself

particularly fostered by Nuclear and Oil & Gas industry, where the complexity of

the systems, both from a physical and a technical point of view, greatly affects

performances.

Specifically, we can distinguish two main types of failures having utterly different

roots and so requiring very different treatments: those due to random breakage of

the instrumentation and those due to human errors.

For what regards the first type we can adopt different strategies concerning

maintenance, redundancy of specific critical nodes, quality improvement of the

single components, and many others.

The kind of strategy to be adopted has to be justified through a proper risk

assessment, according to specific standards and documentation. But this is the

easy part, in fact, the most challenging aspect of risk assessment regards the errors

23

arising from human-system interaction, in fact, in order to be comprehensive a

safety assessment must take into account all the elements of a system, including

human factors, and the corresponding failure probabilities (Subotic et al. 2007).

The entire line of study regarding Human Error Probability is based on the quote

by Alexander Pope “To err is human”. This statement encloses the main pillars of

Human Reliability Analysis and Safety Engineering in general: the harmfulness

and futility of blame culture, since errors are inevitable; and the need to relate

human errors with the mental processes laying behind them.

The importance of the role of humans is easily recognised in the design,

implementation, control, and maintenance of any safety-critical system; and

complex systems, like modern hospitals, present major safety concerns because

of their potential for accidents with fatal consequences.

It is from these key points that the need for a methodical and systematic approach

able to describe and analyse human actions and to individuate behavioural patterns

and specific calculation tools arises.

Applying a scientific approach to a considerable amount of data and observations

has produced more and more sophisticated instruments able to predict

probabilities, or at least provide a measure, of human failures performing a certain

series of tasks in a certain environment.

1.1.2 Role of human cognition in HRA

Patel et al. (2004) showed that in medicine experts tend to follow a top-down

reasoning strategy which seemed anomalous when compared to other domains,

wherein experts tend to first gather data and then assemble hypothesis; this is an

important finding from the perspective of studying errors related to this sector

since it gives us a taste of how the problem is approached, and so of the cognitive

procedures underlying errors and recovery.

Some HRA techniques try to adopt a cognitive approach taking into consideration

the operator, the system and their interactions. The use of the resulting cognitive

models can help in studying human mental processes leading to errors and thus

24

increases the possibility of successfully coping with the underlying causes of the

final outcome.

Unfortunately, cognitive approaches are often tailored on the specific applications

they refer to and a proper analysis of the cognitive process is a very demanding

job that would require a thesis itself. This is the reason why we will not focus on

the cognitive models involved making some strong, but reasonable, assumption

in order to be able to concentrate on the main topic of our work: proposing a risk

assessment technique able to integrate the dynamics of the procedure with

personal and environmental factors affecting the different recovery paths’

probabilities and outcomes.

In order to understand procedural anomalies, it is first of all crucial to have in

mind the standardised main procedure and the relative recovery paths; but also in

this case there is not much in literature, which makes us conclude that when a

failure occurs experience is the only resource a surgeon can rely on.

Basing on experience it is possible to predict the probable outcome of an action

enabling them to provide preventative or supportive inputs; which means that

more expert surgeons have higher probability of success with respect to beginners.

The aim of our HRA application is to predict human erroneous actions in a given

context and to provide, basing on statistical grounds, guidelines regarding the

safer choices to be made when a specific deviation from the standard procedure

occurs.

Although training has resulted to be the most effective way to counteract failures,

many cognitive errors can be counteracted also by system design aimed at

reducing complexity or by proper communication at any level, as will be shown

later on.

Therefore, we can say that our goal is to reduce as much as possible the variance

of failure probability between different performers by generating well-known

alternative, and/or recovery, procedures.

25

1.1.3 The definition of Performance Shaping Factors

Numerous formal HRA methods exist able to identify potential sources of human

error, incorporate them into overall risk models, and quantify the corresponding

Human Error Probabilities (HEPs). To achieve a quantitative estimate of the

HEPs, many HRA methods utilize Performance Shaping Factors (PSFs), which

characterize significant facets of human error and provide a numerical basis for

modifying default or nominal HEP levels (Boring 2006).

Nowadays, the need to adopt an exhaustive, meaningful and hierarchical

classification and taxonomy for all the factors influencing and shaping our

behaviour and the relative outcome is more than ever clear, since in many

circumstances the study of factors contributing to active failures is hampered by

the lack of consistent terminology.

In this sense, the development of Human Factors Classification Framework

(HFCF) for patient safety presented by Mitchell et al., (2016) is for sure a big

improvement in healthcare taxonomy thanks to the cognitive approach adopted,

the list of Contributing factors in HFCF for patient safety is provided in

APPENDIX 4: Contributing factor classifications in the human factors

classification framework for patient safety (Mitchell et al. 2016).

The particularity of this framework is that it provides a hierarchical classification

system that is able to identify multiple causation factors involved in the

occurrence of adverse clinical incidents; and allows temporal relationship between

factors (Mitchell et al. 2016).

The HFCF for patient safety is able to identify patterns of causation for clinical

incidents, and to highlight the need for targeted preventive approaches based on

understanding how and why incidents occur (Mitchell et al. 2016).

Taxonomy is required to diagnose why accidents are occurring and to support

prioritization of remedial actions. The choice of a particular taxonomic structure

(e.g. job-related and cognitive) is driven by the need to capture all types of

potential causes together with the need to identify where remedial actions can be

26

put in place; this is the reason why it must be closely related to the field of

relevance.

One of the developments that we want to experiment is the validation of the

grouping of influencing factors already proposed in previous studies. By doing

this we will be able to reason about behavioural patterns and PSFs’ classes of

importance, which will help the research in outlining a more detailed and complete

scenario.

1.1.4 Surgical environment peculiarities and current state of HRA

application

When it comes to link an extremely quantitative and little explored world as

human mind and a likely complex world as Surgery thousands of possible

considerations could be done.

Trying to transpose HRA techniques to the Healthcare sector we must consider all

the customizable aspects of such techniques in order to select the one that better

fits our case and to calibrate the variants according to the application under study.

First of all, it is fundamental to determine the set of PSFs involved in this kind of

environment through the definition of an ad hoc taxonomy. This requires a deep

investigation of pre-existing literature starting from industry to medical and

surgical related one; then, a validation oriented work by means of surgeons’

interviews and judgements evaluation must be carried out.

This line of research is nothing new for the Politecnico di Milano; in fact, this

study started a couple of years ago and two other theses have already been

produced on the topic of HRA adaptation for Surgery. The first one (Onofrio et

al. 2015) was more related to the taxonomic aspect of the problem, while the

second one (Trucco et al. 2017) proposed an empirical application of a

quantitative technique derived from an adjusted version of HEART; together with

the task analysis development and the taxonomy validation for the specific case.

27

Table 1: Taxonomy for the IFs in Surgery- high technology content (Onofrio et al. 2015)

Influencing Factors

Standardization & Formalization

Training

Equipment & HMI

Distractions

Lighting

Safety Climate

Safety Culture

Staffing

Temperature & Humidity

Space Design

Workload

Cyrcadian Rhythm & Sleep Loss

Communication

Cooperation

Coordination

Experience & Knowledge

Fatigue

Leadership

Physical characteristics & Health

Soft Skills

Stability & Familiarity among team members

28

As said earlier, the most important aspect in terms of customization of the

techniques concerns PSFs. According to several studies, communication errors

are key factors in medical settings. Lingard demonstrated that the 36% of errors

occurring in the operating room are mainly caused by communication issues

provoking waste of resources, inefficiency, list delays, patient inconvenience, and

an increased rate of procedural errors (Lingard et al., 2002); in fact, the

communication is a crucial aspect of modern medical practice and an

organizational issue.

Another peculiar aspect of Surgery applications deducted from previous studies’

results is that surgeons must be considered as “ideal” performers since they are

supposed to have a deep knowledge of the subject and good training for

procedures; in this sense the factors involved assume a crucial role since all

failures are mainly related to non-technical skills.

Hence, when dealing with medical personnel, the knowledge background, except

for experience, must be assumed homogeneous and of high level, so the decision-

making procedure can be standardised and, in general, can be presumed to be the

best possible under selected conditions.

1.1.5 Strengths and flaws of HEART thechnique

From previous studies, it was concluded that the best thing to do, in order to

evaluate the tasks involved in a surgery, is to develop a modified version of

HEART technique suitable for Healthcare application, which has implied the

adoption of the taxonomy presented in the previous paragraph together with the

relative weights attributed through experts’ judgements.

Since our scope is to make a step forward introducing the possibility of

quantifying recovery probabilities and paths, we have to farther alter the approach

presented in previous studies introducing this concept through the support of

experts for validating recovery paths, hypothesis, data coming into play and for

calculating the related probabilities.

Since no other study was carried out on the topic, except for the Politecnico di

Milano ones, there is no evidence of the existence of techniques more suitable

29

than HEART to the Healthcare framework. Aside of that, preserving the HEART-

like approach proposed in previous theses on the subject we can give continuity

to the work developed for this application and make use of the results obtained;

this choice will be better explained and justified in the Study Methodology

Chapter.

Searching for new developments of HEART we got into two main updates of the

technique having the objective of re-actualizing and specializing the general, and

in some sense obsolete, tool for different fields of application, such as Nuclear

Power Plant (NPP) and Air Traffic Management (ATC).

The need for these new tools stem from the fact that the most popular technique

for the quantification of human interactions in the UK, HEART, was developed

many years ago (Williams, 1986), and remained in use without any significant

modification (while HEP database, i.e. CORE-DATA (Computerised Operator

Reliability and Error Database (Taylor-Adams et al., 1995; Gibson et al, 1999) has been under development since 1992), and without any customization for the

different sectors analysed.

Despite the recognition of HEART as a flexible and resource efficient tool, its

extensive usage has also revealed several areas for improvement, including

(Kirwan et al. 2016):

- Under-pinning of the tool by more recent data;

- A clearer understanding of how the data are used to generate the GTTs and

EPC factors;

- Improvements in consistency of usage of HEART;

- Guidance on usage of GTTs, EPCs and APoAs;

- More focusing on NPP human error and recovery contexts;

- Provision of explicit examples or benchmarks for NPP HRA assessors.

The net result of these findings was the evidence that a new approach was

desirable; and the existence of a human error database made such a new approach

possible.

The new tool referring to NPP applications was called NARA (Nuclear Action

Reliability Assessment); and it was basically developed along the same lines as

30

HEART, but based on more recent and relevant data, and tailored to the needs of

UK NPP PSAs and HRAs (Kirwan et al. 2016).

The first step in its development consisted in a contextual adaptation of the tool

producing new list of GTTs (Generic Task Types), and EPCs. For what regards

the first one, the final outcome resulted to be partly a sub-set of the original

HEART GTTs, and partly a further refinement of GTTs’ definition to more

accurately encompass the actions being considered in the PSAs; the new list of

GTTs was then used as the basis for reviewing the current HEP data available

prior to GTT re-quantification (Kirwan et al. 2016).

For the EPCs’ selection instead, many set of EPCs used in the UK NPP PSAs were

reviewed in order to identify overlaps and mismatches, while other EPCs were

generated taking a cue from contemporary human error identification approaches.

On the other hand, to quantify human performance in the context of ATM the

Controller Action Reliability Assessment (CARA) was developed on the wake of

the results obtained adapting HEART to different domains such as in the Railway

(Cullen et al., 2004; Kim et al., 2006) and Nuclear ones (Kirwan et al. 2016). Also

in this case, as for the NARA one, the key modifications applied to the original

technique concern the GTT definition (a new set of GTTs have been developed

for CARA which are specific to the ATM environment), and the set of EPCs to

be involved in the investigation; the same considerations as NARA, regarding the

use of database and the validation of the final sets, were done. The CARA and

NARA GTTs are illustrated respectively in Figure 2 and 3, while the two lists of

EPCs are presented together with NARA ones in Figure 4 and 5 (for the CARA

case those EPCs shaded grey are the ones whose maximum values are supported

by weak validation and therefore should be treated with caution).

31

Figure 2: List of GTT developed for NARA

32

Figure 4: List of quantified GTTs developed for NARA Figure 2: List of quantified GTT developed for CARA

Figure 3: List of EPC developed for NARA

33

In the paper “Application of the CARA HRA tool to Air Traffic Management

safety cases” (Kirwan 2017) we also found a short review of the differences in

applying HEART and CARA to three safety cases in ATC; in particular, the three

cases were related to:

1. Aircraft landing guidance system (www.eurocontrol.fr);

2. A position/identity display for the air traffic control (ATC) aerodrome

environment (EUROCONTROL, 2005);

3. An aerodrome procedure for low visibility conditions using future ATC

systems;

The main findings related to the application of CARA with respect to HEART

were:

- The effectiveness of the GTTs’ redefinition which allowed to include

many more facets (e.g. we passed from including two to six GTTs), and

implied that fewer EPCs were required for CARA;

Figure 5: List of EPC developed for CARA

34

- The fact that CARA’s application led to new insights concerning display

features and their impacts on human reliability (e.g. via provision of a

dedicated audible and visual alarm).

Such insights were based on sensitivity to human factors not previously evident

in the analysis, and would enable the system design team to determine precisely

how to maximise human reliability and controller response to an alarm in the

control tower. In particular, this result shows that CARA can be useful not only

for quantification in safety cases, but also for determining how to improve Human

Factors in a safety-critical system.

As said before, the two HEART’s development presented before respond to the

immediate need for a technique allowing human factors and human reliability to

be considered within the specific safety case area. Indeed, there is a pragmatic

requirement for human factors to enter into the safety case dialogue, and for that

dialogue to be meaningful it is required to be in a quantified and well-defined

context.

This represents the future for HRA techniques and, to our little, with this work we

hope to foster the development of techniques specifically designed and validated

for Surgery, and in general Healthcare, applications.

1.2 Recovery analysis as a development of HRA second generation

1.2.1 The concept of Recovery in System Safety Engineering

Reliability and performance management look at HRA database and techniques,

almost exclusively, as tools to prevent human errors and failures; but if we take a

closer look and think of what exactly we want to prevent: they are the

consequences of a failure rather than the occurrence of the failure itself (Jang,

Jung, et al. 2016a).

This conclusion, that recovery of human errors is as important as the prevention

of human errors and failures, actually paves the way to a complementary field of

35

study concerning the fostering and the investigation of recovery processes

functioning.

Generally, recovery promotion involves the entire sequence from error detection

to the actual recovery; many studies have categorized the recovery process into

three phases; the detection of the problem and its situation, the explanation of the

causes of the problem or countermeasures against the problem, and the end of

recovery empirically (Bagnara et al., 1988; Bove and Andersen, 2001; Francis, 1998;

Frese et al., 1990; Frese, 1991; Johannson, 1988; Kontogiannis, 1997, 1999; Rizzo et al.,

1995; Van der Schaaf, 1995; Zapf and Reason, 1994). Due to the fact that the focus of

recovery promotion up to now has been on categorizing recovery phases and

modelling recovery process, the researches related to the recovery failure

probabilities of human operators are very few and so cannot constitute a reliable

source of data. This is proven by the fact that in our literature research we found

just one line of research coping with recovery probabilities.

The first part of the said study was published in 2014 (Jang et al. 2014) and treated

basic Human Error Probability; the second part was released in 2015 (Jang, Ryum,

et al. 2016) and was related to Recovery Failure Probability; while the third and,

for now, last part (Jang, Jung, et al. 2016a), reported the results gathered from new

experiments aiming at determining Nominal Human Error Probability and

Recovery Failure Probability; the content of the studies will be discussed in detail

in Section 1.2.4.

As mentioned before, the understanding of human cognition and human cognitive

modes represents a crucial node of HRA especially when dealing with complex

procedures and environments, because it strongly affects the way a series of tasks

is performed and of course the ability of recovering errors if any takes place.

Indeed, considering full procedures we cannot neglect the fact that the operator,

specifically the surgeon, can adopt lots of recovery strategies to cope with an

occurring failure; thus, the recovery analysis will cover most part of this

discussion being a not yet investigated topic.

According to several researches, in order to obtain a proper recovery modelling

and evaluation, the recovery process necessitates three main steps:

36

1. Detection;

2. Extrapolation of causes and formulation of possible

countermeasures;

3. Empirical recovery.

By the way, there is another phase which is fundamental in order to promote

effective recovery: the iterative check of the outcome. In fact, it is not sufficient

to implement recovery measures when necessary, but also, the operator always

has to check the effectiveness of his choices and actions, in an iterative fashion

till an acceptable outcome is reached.

What permeates the whole concept of recovery, and is so indispensable to produce

a meaningful study, is the need for a deep understanding of the influencing factors

acting on recovery and of the evolution of human performance and critical

judgement along the chain of events composing the process; so this will be the

focus of the following paragraphs.

1.2.2 How to model recovery: IFs and Dependency

One of the most popular shortcomings adopted while modelling the influence of

Performance Shaping Factors (PSFs) in conventional HRA methods is to

implicitly assume them independent; but this is definitely not true neither for ideal

nor for real cases.

There are two types of dependences that should be taken into account in order to

effectively model phenomena: dependences between tasks and dependences

between PSFs. While the first type has been largely investigated in literature, as

an aspect heavily influencing precision of HEP quantification and proper

understanding of the event itself; the second kind of dependency has been little

treated; so it would be an interesting topic for further HRA studies and

developments.

It is quite straightforward that, when dependences are taken into account, a

significant modification of the influence of PSFs over the operator performance

takes place; especially for complex systems.

37

In fact, the more complex the system is the easier getting links and synergies

between sequential actions is, due to the increasing interconnections of the

influencing factors involved.

If we think about the dependencies between tasks we can easily conclude that it is

not the task itself that influences the outcome of the following tasks, but the fact

that these tasks, or better their relative end result, affect the Global Influencing

Factors; e.g. triggering a steep increase in stress level, so modifying the scenario

in which the process evolves.

Therefore, still being different concepts, the dependency of tasks and the one of

PSFs are strictly intertwined, given the fact that those tasks related to the same

factors are more likely to happen, and so have a stronger reciprocal dependency

than others.

The case study carried out for Air Traffic Control domain using PROCOS by De

Ambroggi (2010) demonstrated a significant modification of PSFs’ influence over

operator’s performance when dependences are taken into account; highlighting

the importance of considering the mutual dependencies existing between PSFs

when it comes to analyse human performance, especially in complex systems.

Over the years, HRA researchers have shown the importance of the role played

by the context in which human errors take place, and so of contextual factors; this

is true also for recovery probability; but, assuming the factors influencing error

probability to be the same affecting recovery probability, and with the same

impact, may lead to misleading results.

Talking about “alternative/recovery paths” we should consider the fact that there

could be a change in the impact of the different factors on the probabilities, and

maybe, a change in the influencing factors themselves with respect to the error

producing conditions; anyway, this step would require a study itself so, as we will

see in the next sections, we will preserve the already validated taxonomy instead

of disproportionately refining the taxonomy issue.

The only article identified during the literature review phase specifically covering

the issue of recovery influencing conditions was the one of Subotic et al. (2007),

where ad-hoc recovery taxonomy was proposed.

38

In particular, in this document the authors validated a list of the relevant contextual

factors affecting the process of controller recovery from equipment failures in

ATC (Air Traffic Control); and suggest a definition for recovery: “recovery

factors are those factors aiming at preventing or reducing the negative

consequences of error or failure”.

Since an important aspect of the recovery process is to have a deep understanding

of the influencing factors; the first step in this direction was to review all

contextual factors identified in the most relevant current HRA methodologies;

concluding that the various techniques identify and emphasise several, and

sometime different, groups of contextual factors.

This study showed not only the crucial relevance of investigating the factors

influencing recovery, and the differences existing between the latter and the

simple failure related ones, but also the vastness of facets captured by the different

methodologies; promoting future researches in this sense.

As final result of this research, the following ad hoc taxonomy for Recovery

Influencing Factors (RIFs), with relative description, was proposed and then

validated through experts’ interviews.

Table 2: Recovery influencing factors (RIFs) (Subotic et al. 2007)

39

1.2.3 The relevance of recovery paths in Surgery

The discussion of the last paragraph emphasises the need for specific terminology

and taxonomy depending on the field of application we want to operate in, Robotic

Surgery in our case.

Since we want to consider also non-conventional paths, i.e. recovery ones, at first

we have to define and properly describe which are the paths of interest, generally

the more probable, and their result, i.e. the final condition they will lead to.

Starting from the assumption that we are dealing with knowledge based cognitive

processing we could say that before choosing the most suitable action the surgeon

has identified all possible recovery paths and evaluated them all; but this

deduction would imply that all recovery paths will end in a “complete recovery

state”, which is not always the case due to the numerous and wide ranging

evaluation he/she would be supposed to do in a few seconds.

Since in Surgery literature only the standard procedures are illustrated it is first

necessary to individuate and formalise the most successful, or at least the most

frequently adopted, strategies that surgeons implement in order to cope with

errors’ occurrence. Every time a failure takes place many variables are affected

and many influencing factors come into play impacting on the time required to

select a countermeasure and also on the subjectivity and quality of the choice

itself.

In order to maximize the efficiency and effectiveness of the procedures it is

essential to model and study how this kind of mechanisms, which are unavoidable

since “to err is human”, develops and works. To do this we have to investigate

those strategies and the relative influencing factors (both personal and contextual)

leading to sub-optimal performances, to produce a simulating tool able to generate

IFs-related probability, and to eventually propose measures to reduce the

probability of fatality.

40

1.2.4 Applications of recovery analysis in literature

A literature research regarding error recovery, paying particular attention to

medical applications, was conducted in order to catch up with the latest results in

this field.

The keywords used to filter the material from Scopus, Web of Science, and

Pubmed platforms were «Recovery Procedures»; «Error Recovery» AND

Surgery; «Error Recovery» AND HRA.

Most of the literature found adopting these research filters proposes modelling

techniques for human recovery process, since it is the first step for integrating this

concept in HRA methodologies; and many highlight the importance of the link

existing between error, recovery success, and cognitive models.

In this regard, there are also studies addressing the issue of how cognitive types

can influence the recovery process; trying to understand and formalise the steps

behind the procedures, even though, as mentioned before, in our case study the

assumption that all the surgeons think, medically speaking, in the same way is not

that far from reality since the theoretical background is homogeneous, or at least

is supposed to be.

The conclusion extrapolated from the analysis of several retrospective recovery

studies is that the error recovery process involves both stages of execution and

evaluation, and that this cyclical process can be modelled using Norman’s model

of interaction (Patel et al. 2011).

The Norman’s model has been intensely used to analyse the process of error

recovery, and, on account of its generic nature, has also been used to model a

broad range of cognitive interactions in which interpretation and action are tightly

connected.

This process incorporates the stages of triggering, diagnosis and correction; and

presents them as part of a decision and action cycle including additional aspects

of clinical decision making, as risk mitigation and cultural barriers to the detection

and correction of errors.

41

The evaluation of the impact of several factors -such as: recovery easiness,

severity, and detectability of the error- on recovery probability is suggested in (Su

et al. 2000); even though also in (Patel et al. 2011) some influencing factors were

identified; e.g.: expertise, complexity of the paths chosen, and completeness of

the available information.

In Kontogiannis' work (2011) a proposal for modelling recovery steps from the

cognitive point of view, and a scheme for state transition representation is

provided. This study presents a research framework in terms of error recovery

strategies providing hypotheses for empirical research and, also in this case,

pointing out several influencing factors (e.g.: conflicting goals, cooperation and

communication of team’s members).

We can say that the conclusions and results derived from the literature research

are pretty coherent, even when the field of interest is different, which is for sure a

meaningful achievement as well as a good starting point; but, aside from this

introductory investigation, we accuse a scarcity of quantitative validation.

In fact, modelling human recovery process is not sufficient to apply HRA since

its implementation also requires human error and recovery probabilities, i.e. a

quantitative approach.

As mentioned before, Jang’s studies are the only ones, found in our research,

practically evaluating recovery paths’ probability.

The work was developed studying a digital Human System Interface (HSI) in

Nuclear Power Plants (NPP) and was structured in three blocks: Task analysis and

human error modes identification through SHERPA; Analysis and modelling of

dependencies between error modes through THERP model; and Statistical

analysis of the experiment results through a Bayesian analysis.

An adequate taxonomy for human errors was defined during the process; in

particular it was made up of eight categories: Operation selection omission;

Operation execution omission; Wrong screen selection; Wrong device selection;

Wrong operation; Mode confusion; Inadequate operation, and Delayed operation.

In order to simplify the discussion, it was decided to take a shortcut in treating the

main factors affecting human error and recovery; in fact, recovery failure

42

probabilities were considered static, and the scenario was characterised by

constant parameters: no time urgency, no supervision, and high level of human

machine interface throughout the procedure.

Setting the values for these three external factors, the authors are somehow

defining a static scenario which dramatically affects the validity of failure and

recovery probabilities; leading to highly constrained results and without covering

the magnitude of variables involved.

One or more error types were associated to each task/subtask and the simulations

of the accident scenario, which required cognitive action, was performed.

A statistical analysis was then carried out on the results in order to obtain human

error and failure recovery probabilities (the statistical method based on Bayesian

analysis was used adopting 5% and 95% quantiles).

Further investigations on this application showed that such influencing factors as

task dependency are not negligible (Jang, Jung, et al. 2016b); indeed, the failure

or success of one subtask may affect the failure or success of the next subtask if

the two are not mutually independent.

Hence, the reason why dependency among subtasks should be considered is that

the HEP of a task could be overestimated or underestimated as the number of

subtasks, i.e. the level of detail of our model, increases (Jang, Jung, et al. 2016b).

To assess the degree of dependency, in this study, THERP dependency model was

adopted; this choice was justified by the lack of data on conditional probabilities

and the validity of the development process of the model (Jang, Jung, et al.

2016b).

Pr [failure dep. step] =

= Pr [failure initial step] *Pr [failure dependent step| initial step failure]

43

The formula above represents the basic definition for a subtask failure probability

depending on the one preceding it, given the fact that the latter failed. According

to THERP this concept can be also expressed through the following formula:

Pr [B|A] = a + b * Pr [B]

Where A and B represent the subtasks; while, a and b are positive numbers

obtained from judgments, and not from data, according to the following chart:

The THERP approach to dependency assessment uses several parameters to

determine the level of dependency between events, including same or different

crew, time, location, and cues (Swain and Guttmann, 1983); the general guidelines

in assessing the levels of dependence are provided by THERP handbook.

In this particular case, the different classes of dependency were identified by

considering different factors: Similarity of control devices, Separation between

control devices (closeness), Repeated action steps, and Group soft control.

Finally, a tree diagram was developed with binary exit (Y/N) for each of the

mentioned criteria, so that the final twelve branches were divided between the five

levels of dependency: Complete (all Y), High, Moderate, Low and Zero (all N).

Adopting this classification, it is possible to formulate, according to THERP, the

probability of occurrence of an event B given an event A.

Also the evaluation of HEPs was performed using THERP; but, it is important to

notice, that neither RIFs nor PSFs were taken into account since their weights

were considered unitary and constant.

The procedure for the quantitative evaluation of HEPs was:

1. Deduce error and recovery probabilities (E and R) from

experiments results;

44

2. Obtain dependency level estimates (k) from experts’ interviews;

3. Calculate HEPs related to each task/subtask adopting the following

expression:

HEPi=1-((1-R0E0)*∏ (1 + 𝑘1

1+𝑘(1 − ∑ 𝑅i𝐸i)𝑖≠0𝑖 )

So, the values obtained for the various probabilities, both failure and recovery

ones, were achieved through an empirical approach, observing the outcomes of a

certain number of experiments under fixed boundary conditions and focusing only

on discrete tasks selected in advance.

1.2.5 Current gaps in literature

The findings regarding recovery failure probabilities for the Industrial sector

cannot be imposed directly to Surgery applications since the context in which

error recovery is supposed to take place and the factors involved are, or at least

could be, totally different.

As mentioned in the influencing factors’ paragraph, the specificity of taxonomy

is a crucial aspect for conducting reliable and meaningful investigations. Also, the

validation of such terminology is essential to preserve a scientific approach to the

subject; and this aspect is completely missing in previous works.

Moreover, even in those few studies where influencing factors (PSFs/RIFs) are

discussed, as in Jang ones, they are only described in a static and reductive

fashion.

It is pretty easy to understand that considering the effect of time urgency, no

supervision, and a high level of human machine interface as constant the authors

made a strong approximation which prevented to get a reliable and complete

evaluation of the system; which is even less acceptable considering that the

challenge for the future is to develop a dynamic analysis.

For what regards dependency, even though the issue is treated in literature a

standardised and universal definition for the concept of dependency and

dependency level is missing.

45

Finally, talking about quantitative methodologies for studying recovery

probabilities only the statistical approach has been adopted up to now; and this

gap is a huge obstacle for the transposition of this kind of analysis in Healthcare

due to the lack of available and reliable data.

1.2.6 Further developments in the Healthcare sector

For what regards the Medical sector no technical and in deep studies has been

carried out to systematically analyse and integrate factors and procedure with

human risk assessment techniques.

This wide gap in research is due to the fact that the two scientific fields,

Engineering and Medicine, have few commonalities and so it is difficult to instil

the idea that, adapting industrial safety strategies, is a way to improve the

performance and optimize the resources in non-industrial environments.

Starting from the latest developments in HRA for Surgery, most of which are not

validated yet, it would be interesting to invest more and more effort in the creation

of specifically modified versions of standard tools, presented in literature and

designed for general industrial or nuclear applications, in order to produce

powerful techniques able to greatly impact the quality of life.

Previous studies (Trucco et al. 2017; Onofrio et al. 2015) have produced specific

taxonomies for Surgery and task flow analysis for the BA-RARP surgery which,

together with the modified HEART developed for the surgery environment,

constitute the basis for our discussion.

A first objective for future studies could be to propose and validate taxonomy for

Recovery Influencing Factors (RIFs). This could be different from the one

developed for Error Producing Conditions as, to initiate a recovery path, a failure

must have already taken place; so, the effect of circumstantial factors as high

stress, lack of time or difficult communication can be different from the one

relative to fault free situations; moreover, even completely new aspects may rise

from this mismatch. By a proper definition of RIFs it would be also possible to

investigate those factors influencing the detectability of errors and the judgement

capacity of the professionals involved in the procedures under analysis, so giving

46

an insight to the cognition model of the operator without introducing complicated

models in the quantitative simulation tools.

Beside the terminology aspect, it is fundamental to both validate and improve the

understanding and the description of functional, spatial and time relations

involved in the given setting (i.e. operating room layout, procedural sequence,

personnel and equipment involved, management policies, etc.) in order to justify

the dependencies considered and to evaluate their impact.

Another key aspect together with the definition of the various correlations is to

define the ideal level of detail to adopt, or better, the level of detail that is relevant

and suitable for investigating Surgery applications; in fact, in order to optimize

the effort, it is desirable to consider only those elements/tasks whose impact on

the procedure outcome is tangible and not negligible. This “resolution aspect” will

transpire from the recovery modelling phase, and an interesting objective for

future studies and developments could be to create and validate recovery models

taking into account different degrees of resolution to investigate the properties of

the various combinations for “any” situation.

47

CHAPTER 2: DYNAMIC RISK

ASSESSMENT AND DYNAMIC EVENT

TREES

2.1 Dynamic generation HRA

2.1.1 From static to dynamic analysis

Most HRA models are designed to capture human performance at a particular

point in time. These models can be considered static HRA models, in that they do

not explicate how a change of one PSF affects other PSFs and the event

progression downstream. On the other side, most HRA methods do account for

dependency, which is the effect of related events on HEP calculation (Boring

2006)

Dependency, however, is typically based on overall HEPs and does not

systematically model the progression of PSF levels across events, while dynamic

HRA needs to account for the evolution of PSFs and their consequences to the

outcome of events (Boring 2006).

The need to integrate the time dimension in human behaviour analysis is the

logical consequence of the investigation of human mental processes, and of the

fact that many of the so-called influencing factors are implicitly related to the

timeline of the process/system they describe. In this sense, dynamic risk

assessment allows more detailed analysis and in deep mapping of performance

measures.

Going back to the basics, we can individuate three main families of HRA: first

generation, second generation, and dynamic HRA.

The main defects of I generation methods were identified as: lack of distinction

and identification of omission and commission errors; production of statistical

results due to too little database; insufficient structure (which makes it

48

unrepeatable); and absence of a causal picture (which prevents the development

and the implementation of effective countermeasures).

The crucial difference between these first two generations of methods consists in

the fact that while the first one largely fails to consider the context in which human

failures occur, not considering human cognitive processes, the second generation

carefully considers and models the influence of context on human behaviour and

on failure occurrence.

Finally, what distinguishes the so called third HRA generation from the previous

two is the fact that it provides a dynamic framework for HRA modelling and

quantification. In order to get the feeling of the direction that scientific innovation

is taking, we now focus our discussion on those literature research findings

specifically dealing with this last category of HRA methods, which objective is to

deliver tools able to simulate the real-time behaviour of a system.

2.1.2 Historical Evolution of dynamic HRA in Industry

Nowadays, it is recognized that a number of Dynamic Event Trees and direct

simulation software packages for treating operator cognition and plant behaviour

during accident scenarios are being developed or are already available (National

& Falls 1996).

Simulation-based HRA techniques differ from their antecedents in that they are

dynamic modelling systems that reproduce human decisions and actions as the

basis for performance estimation.

To conclude this introduction about dynamic HRA, and its evolution over the

years, a summary table of the applications found in literature is provided. It is

evident that almost all of the applications refer to the nuclear sector (NPP), and

none of them makes reference to the surgical one, which will make our job even

more challenging.

49

Table 3 Literature review of dynamic HRA applications

Authors Title Objectives

Field of

application

Results

(National

& Falls

1996)

Representing

context,

cognition, and

crew

performance in

a shutdown risk

assessment

Demonstrate

how the DET

analysis method

(DETAM) can

be used in a

realistic analysis

to treat context,

cognition, and

crew

performance.

NPP

It demonstrates

how quantitative

risk predictions are

affected by the

treatment of

dynamics.

(Boring

2006)

Modelling

Human

Reliability

Analysis Using

MIDAS

Point out the key

considerations

for creating

dynamic HRA

framework

(including event

dependency and

granularity).

Nuclear

power plant

(NPP)

control

room

operations.

Division of the

eight starting

factors into the

three types of

PSFs’

modifications

50

(Trucco

& Leva

2007)

A probabilistic

cognitive

simulator for

HRA studies

(PROCOS)

Develop a

simulator

(PROCOS) for

approaching

human errors in

complex

operational

frameworks.

Air Traffic

Control

(ATC)

The comparison

between the results

of the proposed

approach and those

of traditional HRA

methods shows the

capability of the

simulator to

provide coherent

and accurate

analysis.

(Chang

&

Mosleh

2007)

Cognitive

modelling and

dynamic

probabilistic

simulation of

operating

crew response

to complex

system

accidents

Discuss the

information,

decision and

action in crew

context (IDAC)

model for HRA.

NPP An overview of the

IDAC architecture

and principles of

implementation as

a HRA model.

(Rao et

al. 2009)

Dynamic fault

tree analysis

using Monte

Carlo

simulation in

probabilistic

safety

assessment

Validation of

Monte Carlo

simulation to

solve dynamic

gates

NPP

regulation

system

Monte Carlo has

proven to be a

reliable tool to

solve DFT.

51

(Gil et al.

2011)

A code for

simulation of

human failure

events in

nuclear power

plants:

SIMPROC

Demonstrate the

Demonstrate the

validity of

SIMPROC tool.

NPP SIMPROC is an

adequate tool to

incorporate in the

simulation of

Plant dynamics the

effects of actions

performed by

operators while

following the

operating

procedures.

(Ge et al.

2015)

Quantitative

analysis of

dynamic fault

trees using

improved

Sequential

Binary

Decision

Diagrams

Confirm the

applicability and

merits of SBDD

for generating

DFTs.

Highly

coupled

DFTs of

non-

repairable

mechanical

systems

Compared with

Markov methods

SBDD overcomes

the notorious

problem of “state

space explosion”

and is also

applicable for

DFTs modelling

systems with

arbitrary time-to-

failure distributed

components.

52

(Rao et

al. 2015)

A Dynamic

Event Tree

informed

approach to

probabilistic

accident

sequence

modelling:

Dynamics and

variabilities in

medium LOCA

Develop

alternative

Dynamic Event

Trees and

quantify damage

frequency.

NPP Risk sensitivity to

numerous

assumptions and

the benefits that

DETs provide in

terms of

characterizing

scenario dynamics

were pointed out.

(Gyung

et al.

2016)

Development

of a systematic

sequence tree

model for feed-

and-bleed

operation under

a combined

accident

Validate the

adoption of

sequence trees to

systematically

analyse accident

sequences and

plant conditions.

NPP Eleven possible

accident sequences

under a combined

Accident (TLOFW

accident) were

identified and

systematically

categorized.

The literature review has pointed out several approaches to the dynamics’ topic;

one of the presented methodologies is the Dynamic Fault Tree one (DFT), which

opens the doors to the computational issue that comes together with simulation

tools.

Dynamic Fault Trees extend traditional Fault Trees by defining additional gates

called dynamic gates to model complex interactions; which are generally solvable

through Markov models deployment. However, when the number of gate inputs

increases state space becomes too large for calculation with Markov models.

Moreover, Markov model is applicable only for exponential failure and repair

distributions; so, to address these difficulties, Monte Carlo simulation-based

approach is commonly adopted.

53

Monte Carlo (MC) simulation methods have been broadly used to evaluate

complex modelling industrial systems with arbitrary distributed components, and

they are often regarded as benchmarks in the validation of new proposed

approaches.

This simulation method treats the problem as a series of real experiments

conducted in a simulated time, and it estimates probability, as well as other

indices, by counting the number of times an event occurs during the simulated

time (Rao et al. 2009). It represents one of the latest improvements in the field of

complex systems’ simulations.

2.1.3 Simulation tools: benefits and challenges

Cacciabue and Hollnagel (1995) have the credit of having been the firsts providing

a formal and comprehensive definition of cognitive simulation:

“The simulation of cognition can be defined as the replication, by means

of computer programs, of the performance of a person (or a group of

persons) in a selected set of situations. The simulation must stipulate, in

a pre-defined mode of representation, the way in which the person (or

persons) will respond to given events. The minimum requirement to the

simulation is that it produces the response the person would give. In

addition, the simulation way may also produce a trace of the changing

internal mental states of the person”.

Simulation-based HRA methods provide a new direction for the development of

advanced methodologies in order to study the effect of operators’ actions during

procedures.

Human performance simulation tools utilise virtual scenarios, virtual

environments, and virtual humans to mimic the performance of humans in actual

scenarios and environments.

Simulations may be used to produce estimates of Performance Shaping Factors

(PSFs) and to quantify Human Error Probabilities (HEPs) in dynamic frameworks,

so better representing reality. Hence the main challenge of such an approach is to

replicate the stream of consciousness of the human component of the system,

54

considering both influencing factors and cognitive type, in order to provide a

dependable analysis not only of risks and outcomes in general, but also of their

root causes.

We can find an example of performance measures mapping in (Boring, 2006). In

his work, Boring wanted to demonstrate that through task/procedure iterations it

is possible to systematically explore the range of human performance, and obtain

an estimate of failure (or success) frequency, which can finally be used as a

frequentist approximation of HEP.

We can say simulation tools address the dynamic nature of human performance

in a way that has not been found in other HRA methods. This kind of tools

represents one of the pillars of dynamic HRA’s evolution; in fact, functioning as

data sources and serving as a privileged observatory at the same time, it constitutes

the basis for this approach.

The possibility of dealing with a virtual and ideal reality drastically reduces the

complexities of the dynamic environment, fostering the creation of new ad hoc

techniques and the adaptation of old ones to new requirements (e.g.: integrating

the dynamic progression of human behaviour throughout the task and the failure

itself).

The paper “Dynamic Human reliability analysis: benefits and challenges of

simulating human performance” (Boring 2007) reviews the differences between

first, second, and dynamic generation HRA, outlining potential benefits and

challenges of this last approach.

As suggested before, simulation-based HRA differs from its antecedents in that it

is a dynamic modelling system that reproduces human decisions and actions in

order to constitute a sample on which basing performance estimations; so,

providing the fundamental grounding for dynamic HRA modelling.

In particular, the article points out the fact that a generic simulation tool might be

used in several ways, as we can see represented in the picture below.

55

Figure 6: The uses of simulation and modelling in HRA

As we mentioned before, the most interesting uptake of dynamic methods from

our point of view is the generation of PSFs’ estimates which can consequently be

used for the quantification of the HEPs through specific HRA techniques.

Simulation-based HRA may also augment previous HRA methods by

dynamically computing PSF levels, and so being able to derive punctual HEPs for

any given point in time.

This could be done by integrating the procedures with the simulation of the plants,

and adding operators’ actions as boundary conditions. Adopting a frequentist

approach for calculating HEPs (where a variety of human behaviours is modelled

through a series of Monte Carlo style replications) would then enable the

production of an error rate over a denominator of repeated trials.

In this regard, Gil et al. (2011) presented a work where the SIMPROC tool was

implemented to simulate human failure events in a Nuclear Power Plant (NPP).

This tool generates a Dynamic Event Trees (DET) stemming from given initiating

events which, taking into account the different factors related with the tasks

involved, efficiently simulates all the branches which may affect the dynamic

plant behaviour in each sequence.

It is now important to specify the fact that there is a key distinction between

simulation and simulator data. Indeed, while simulations utilize virtual

environments and virtual performers to model the tasks of interest; simulators

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utilize virtual environments with human performers (Bye et al., 2006). Given the

above, using actual people, simulators can capture the full spectrum of human

PSFs for a given task, whereas simulations must rely on those PSFs for which a

virtual modelling is possible.

Nonetheless, the possibility to use simulation tools to run an unlimited number of

scenarios (virtually without actual humans once the configuration is initiated), and

to obtain almost instantaneous results, dramatically reducing the costs, is the

principal benefits of this type of technique. Hence, the opportunity to perform,

and analyse, a wider spectrum of scenarios, in a generally easier and more cost-

effective way makes simulations technology the most commonly used between

the two.

On the other hand, one of the arguments raised against the use of these tools is

that the predictive ability of simulation is hampered by epistemic and random

uncertainty, and by the mismatching and shortcoming attributable to the lack of

full understanding of the modelling parameters and random variance respectively.

Most of the HRA methods follow general task analysis guidelines for event

decomposition, but there is significant variability in the level of decomposition

adopted across analyses and analysts. While one analysis may focus on a detailed

step-by-step breakdown of human actions and intentions, another may cluster

human actions at a higher level according to resultant errors; and this

inconsistency is particularly problematic in making headway on dynamic HRA

(Boring 2007).

In conclusion, human performance simulations surely have revealed important

new data sources and possibilities for exploring human reliability; though, there

are still significant challenges to be addressed, particularly with regard to the

dynamic nature of HRA vs. the mostly static nature of conventional first and

second generation HRA methods (Boring 2007).

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2.2 The crucial role of PSFs: properties and behaviour over time

When an individual encounters an abnormal event, the natural reaction often

includes physical, cognitive, and emotional responses (Chang & Mosleh 2007).

These three types of response also influence each other; and there is ample

evidence that they also affect problem-solving behaviour. In addition to these

internal PIFs, there are external PIFs (e.g., organizational factors) affecting

individuals’ behaviour both directly and indirectly.

Different types of PSFs adjustments were proposed and analysed; but, in order to

take proper decisions and to make the result compatible also with different

application, it is important to understand the fundamental role that the scenario

involved has on the process.

This point is backed by numerous studies confirming the fact that the first step in

a dynamic risk assessment is to identify the accident scenarios where it appears;

indeed, the interface and the interaction between the plant and its operators is

obviously described by a critical dynamic process together with crew cognition.

For what regards the present study, it was limited to the implementation of an

already validated taxonomy, considering the impact of the factors as constant, but

changing the set of relevant IFs depending on the task involved. In other words,

the study only considered discrete changes of IFs over time, as a first attempt to

introducing HRA simulation tools in healthcare. The possibility of investigating

the evolution in PSFs’ estimation and impact will be one of the suggestions for

future studies; nonetheless, we want to provide a quick insight to the topic.

When addressing a dynamic framework, not only it is important to analyse the

behaviour and the performances of the simulated operators, but also to capture

and manipulate the PSFs that have an effect on those outcomes.

A realistic simulation is comprised not only of the normal random span of human

behaviour for a given situation, but also of the range of PSFs that influences the

result of the simulation and their evolution throughout the procedure.

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In particular, Boring (2007) defined three types of possible modifications to PSFs:

4. Static Condition;

5. Dynamic Progression;

6. Dynamic Initiator.

In “Static Condition”, the PSFs remain constant across the events or tasks in a

scenario (e.g. we can consider static the educational background of a surgeon

during a surgery). With “Dynamic Progression” we describe both positive and

negative evolution of PSFs’ impact on the performance (e.g. the stress level can

affect both positively or negatively a surgery outcome). Finally, a “Dynamic

Initiator” is defined as a sudden change in scenario, which badly affects the

general outcome.

An important aspect of the transition from a static to a dynamic tool stands in the

need for a coherent transposition of the PSFs in the second domain. This is the

topic investigated by Boring in its work “Modelling Human Reliability Analysis

Using MIDAS International Workshop on Future Control Station Designs and

Human”; where the initial effort to model the SPAR-H PSFs in MIDAS is

addressed.

MIDAS is a simulation tool permitting Monte Carlo style multiple runs of

scenarios; and enabling the adoption of a frequentist approach to HEP calculation,

through which simulated errors may be mapped back to the PSF states at the time

the error occurred. In this way, it is also possible, to calculate HRA dynamically

across scenarios (Boring 2006).

The three types of PSFs modification listed above were considered by Boring, and

it was agreed that the simulation tool must (Boring, 2006):

- Include the nominal effects of a PSF for static conditions;

- Feature the full range of PSF effects, from performance enhancing to

performance decreasing effects;

- Incorporate the natural cause-and-effect relationship of one task on

another in terms of the PSF progressions;

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- Consider PSFs over time, in terms of diminishing effects (i.e., the natural

decay of an effect) and effect proliferation (i.e., the natural increase of a

PSF over time, even if it begins as a latent effect)

- Reconfigure PSFs in the face of changing scenarios while retaining PSF

latency and momentum states from the scenario forerunner for a suitable

refractory period.

The model developed in the work of De Ambroggi (2010) allows to distinguish

two components of PSF influence: Direct Influence; i.e. the influence that the

considered PSF is able to express by itself notwithstanding the presence of other

PSFs; and Indirect Influence; i.e. the incremental influence of the considered PSF

due to its dependence on other PSFs.

The results of this study showed the relevance of considering both direct and

indirect influence of the nine selected factors and the predominance of the

acquired component in modifying the weights of the PSFs; thus not considering

the latter leads to a biased estimation. (De Ambroggi 2010).

Another interesting point regarding Performance Influencing Factors was made in

(Chang & Mosleh 2007) where, in modelling PSF groups entering a simulation

process (the fifty PIFs were classified into eleven hierarchically structured

groups), also interdependencies between the factors were taken into account so

improving the result’s accuracy achieved by means of the IDAC model.

This paper provides detailed discussions of two important modelling elements of

IDAC: firstly, the identification of the set of relevant PIFs; and secondly, the

important topic of PIFs’ interdependencies providing a diagram of PIFs influence

linking externally observable inputs and outputs to internal PIFs (i.e.: PSFs’

grouping).

Also, a complementary discussion is presented in the paper about the methods for

assessing the states or values of the single PIFs; and, in order to facilitate the use

of these models in a dynamic PRA framework, the qualitative PIF dependencies

were transformed resulting in an explicit and quantitative causal model. This

would set a foundation for the integration of further evidence and an orderly

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improvement of the accuracy and completeness of the causal model (Chang &

Mosleh 2007).

2.3 Dynamic Event Trees as a tool to formalize system/procedure

evolution

2.3.1 Introduction

In the previous paragraphs, we have introduced Dynamic Fault Trees (DFTs) as

powerful tools for modelling systems with sequence and function dependent

failure behaviours; anyway, although DFTs are very effective in introducing the

dynamic behaviour of systems, their quantitative analyses are pretty much

troublesome, especially for large scale and complex DFTs.

Even though Monte Carlo (MC) simulation methods have been broadly used to

evaluate complex DFTs modelling industrial systems with arbitrary distributed

components, and are often adopted as benchmarks to validate new proposed

approaches, another appealing alternative to DFTs is represented by Dynamic

Event Trees (DET); which basically consists in Event Trees for which branching

is allowed to occur at discrete points in time, and where the definition of system

states is left to the analyst.

We can say that DET framework is extremely flexible (National & Falls 1996),

and aside that, it provides a means to analyse the scenario dynamics under the

combined effects of stochastic events, so, it is of particular interest to us.

2.3.2 The five characteristics of DET

A Dynamic Event Tree is defined by five key characteristics:

5. The branching set (level of detail);

6. The set of variables defining the system state;

7. The branching rules (to determine when a branching should take place);

8. The sequence expansion rules (to limit the tree expansion);

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9. The quantification tools to compute the deterministic state variables (e.g.,

process variables).

The branching set is probably the key characteristic of the DET technology, since

it determines the scope and level of detail treated by the Dynamic Event Tree

(National & Falls 1996).

Most HRA follows general task analysis guidelines for event decomposition, but

the level of decomposition adopted is significantly variable due to its dependency

on the operator modelling it, and this generates problem in the quantitative

evaluation phase; in particular (Boring 2006):

- Most simulation systems offer a highly-detailed level of task

decomposition that may be incompatible with certain HRA approaches;

- Adjustments to HEPs for dependency based on human action clusters may

be artificially inflated when used with a highly-detailed level of task

decomposition, because there is no granularity adjustment on dependency

calculations;

- No current HRA method offers guidance on the treatment of continuous

time-sliced HEP calculation as is afforded by dynamic HRA.

This variability is due to the degree of freedom left by the possibility to arbitrary

discretize the different variables (e.g. hardware state, crew diagnosis state, crew

quality state, and crew planning state); indeed, the larger number of state for

system variables are allowed, the more detailed the event tree will be.

For what regards the branching rules, even if branching is allowed at discrete time

intervals, it is not performed for each time interval due to the size that the Dynamic

Event Tree would have; instead, branching is performed only when at least one of

these two conditions arises:

- A hardware system is demanded to function (e.g. alarm);

- A critical time point is reached in the scenario (e.g. failure occurring).

Talking about quantification tools, two types are typically used in a Dynamic

Event Tree analysis; the first type includes the tools used to predict the dynamic

behaviour of plant process variables for each scenario in the tree; while the second

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type includes the ones necessary to develop the conditional branching (National

& Falls 1996).

For each branching point, the quantification process involves four steps:

- Evaluation of crew's cognitive state and of the nature/quality of the

information regarding the plant available to the team;

- Qualitative evaluation of the conditional likelihood of each branch;

- Initial determination of the conditional probability for each branch;

- Comparison of the conditional probabilities for similar situations in

different parts of the tree, and adjustment.

When it comes to construct an event tree at each branch of the tree a probability

value must be determined. This value can derive from expert judgments or from

data collected in databases adaptable to the situation of interest; but, as long as the

mental processes followed in the decision-making process or in the actual

performance of the task are not considered, important sources of information may

be lost.

Clearly, this kind of analysis has the same drawbacks attributed to all studies

making extensive use of expert judgments. However, the last step of the ones

mentioned above (i.e. Comparing similar branches) greatly facilitates the

assessment because it enables the analyst to use information concerning the

relative likelihoods of scenarios and to perform a double check on the results

obtained and proposed.

2.3.3 Industrial applications of DET

The applications of Dynamic Event Trees have spread largely after the birth and

improvement of simulation tools taking advantage of the various peculiarities of

this technology, such as flexibility and completeness.

One of the first studies in this direction was performed by Gertman in 1996, when

he demonstrated that the DET analysis method, specifically DETAM (Dynamic

Event Tree Analysis Method), can be used in a realistic analysis to treat context,

cognition, and crew performance. This work is particularly interesting for us

because it introduces the Dynamic Event Tree’s property that allows to bypass the

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problem of resource limitation, issue that we may instead encounter in Cognitive

Event Trees.

This last category of Event Trees is useful for presenting potential crew decisions

and actions, and is quantified in the same way as THERP Event Trees; without

explicitly representing current plant conditions, and modelling potential stochastic

variations in certain PSFs (e.g., stress) (National & Falls 1996).

One common argument against the use of DETAM, or related methods, concerns

the complexity of the approach; that is why, one of the scope of the mentioned

paper was to demonstrate that this method could be practically applied to a

realistic accident scenario.

This approach, in which the dynamic evolution of possible scenarios is modelled

explicitly, allows the treatment of various crew states and their interaction with

the different plant; and also the treatment of various Performance Shaping Factors

within the physical, cognitive, and psychological context of the evolving scenario

(National & Falls 1996).

The study concluded that DETAM is a useful technique for realistically modelling

crew/plant interactions during complex accident scenarios; enabling the treatment

of cognitively based errors of commission and omission; and also to deal with

situations where cognition is not a key factor; still being less efficient than

conventional action-based models for such applications.

Finally, although DETAM manual implementation is possible, a software

implementation was suggested in order to improve the efficiency of the analysis,

and have improvements in terms of both reliability and consistency.

The paper “A probabilistic cognitive simulator for HRA studies (PROCOS)”

(Trucco & Leva 2007) described the development of a simulator for approaching

human errors in complex operational framework integrating the quantification

capabilities of the so-called ‘first-generation’ human reliability assessment

methods with a cognitive evaluation of the operator.

Also, a scenario analysis was performed setting the PSFs values through

commissioning personnel judgments regarding three selected standard situations:

optimal case scenario; nominal conditions scenario; and worst case scenario.

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The simulator proposed (PROCOS) allowed analysing both error prevention and

error recovery; and the comparison of the results obtained through the proposed

approach and those of traditional HRA techniques pointed out the capability of

the simulator to provide coherent and accurate analysis.

As we mentioned before in our discussion, there is a fundamental difference

between simulation and simulator data. In particular, we want to recall the fact

that simulators utilize virtual environments with human performers (Bye et al.,

2006); so, since simulators employ real humans, they are able to capture the full

spectrum of human PSFs for a given task, whereas simulations must rely on those

PSFs for which a virtual modelling is possible.

Specifically speaking about PROCOS’s structure, the components this simulator

is made up of are the following (Trucco & Leva 2007):

- The operator module, which implies the cognitive flow charts for

action execution and recovery phase, plus the error types/error modes

matrix. The critical underlying feature of this module is the

mathematical model for decision block criteria of the flow charts;

- The task execution module, based on the event tree referred to the

procedure that has to be simulated;

- The human–machine interface module, made up of tables regarding

the hardware state and its connection with the operator actions (task

executed or error modes committed).

While the inputs required for the simulation process are:

- Set of PSFs affecting the task to be simulated;

- Hardware involved in the execution of the task and its possible states;

- Steps of the task (task analysis);

- Set of error modes to be considered.

Only a few years after this study Universidad Politécnica de Madrid in

collaboration with the Consejo de Seguridad Nuclear developed the so-called

SIMulator of PROCedures (SIMPROC).

This tool aims at simulating events related with human actions and is able to

interact with simulation model of plants; moreover, SIMPROC helps the analyst

65

quantifying the importance of human actions in the final plant state (Gil et al.

2011).

This software tool was coupled with a software package (SCAIS), a simulation

system developed to support the practical application of Integrated Safety

Analysis (ISA) methodologies able to generate Dynamic Event Trees stemming

from an initiating event, based on a technique able to efficiently simulate all

branches taking into account different factors related with headers which may

affect the dynamic plant behaviour in each sequence (Gil et al. 2011)

In this paper, also a methodology to computerize an Emergency Operator

Procedure (EOP) is proposed:

1. Obtain a task flow diagram from the procedure, considering the procedure

instruction of interest for the simulation;

2. Identify, one by one, each task action with a computerized instruction and

define the variables needed to manage the information related with the

instructions, identify the plant systems, components or physical

parameters;

3. Computerization.

The computerization phase can be carried out through the following steps:

- Specify the modelling detail level for plant systems and components;

- Clarify the plant physical parameters relevant to the procedure;

- Conduct the computerization of actions over components, to control

physical parameters within a range.

The computerized procedure obtained should have the same functionality as the

hardcopy procedure; this can be checked by comparing the original task flow

diagram with the computerized task diagram (Expósito et al. 2008).

Ultimately, this work demonstrated that SIMPROC is an adequate tool to integrate

the simulation of the plant dynamics with the effects of actions performed by

operators while following the operating procedures.

During the literature review, another version of the DET for dealing with dynamic

modelling was presented: The Sequence Tree.

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A Sequence Tree is a type of branch model that categorizes the plant condition by

considering the plant dynamics. Using the Sequence Tree model, all possible

scenarios requiring a specific safety action to prevent core damage can be

highlighted, and success conditions of the safety actions performed during a

complicated situation, such as a combined accident, will be also identified. In

short, we can say that Sequence Trees are the qualitative version of DET; in fact,

if the initiating event frequency under a combined accident can be quantified, the

sequence tree model can translate into a Dynamic Event Tree model based on the

sampling analysis results (Gyung et al. 2016).

Finally, in the study it was demonstrated that through the utilisation of Sequence

Tree models all the theoretically possible undesirable sequences, under a specific

combined accident situation, were identified and systematically categorized

adopting the modelling tool suggested in the paper.

In “A Dynamic Event Tree informed approach to probabilistic accident sequence

modelling: Dynamics and variabilities in medium LOCA“, the discussion goes

back to the quantitative dimension. Here the Human Error Probabilities are

calculated as the summation of Diagnosis Error Probability and Execution Error

Probability; the Diagnosis Error Probability is multiplied by the basic HEP of a

diagnosis error and the relative weighting factor.

In their work Rao et al. (2015), the Dynamic Event Tree framework is applied in

order to support the definition of success criteria; the following PSFs were

considered: Man-machine interface (e.g. alarm), Decision burden, Procedure, and

Education/training.

In particular, the aim was to assess the impact of variabilities and scenario

dynamics on success criteria, and ultimately on the results of the PSA model of

the scenario, i.e. on the core damage frequency (CDF) and dominant contributors

(Rao et al. 2015). This was made possible by the fact that DET framework

constitutes a valuable means to analyse scenario dynamics under the combined

effects of stochastic events.

Adopting this kind of approach, it was evident that a few considerations regarding

the operators’ path choice are necessary, and that those assumptions will depend

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on safety systems’ state, on plant parameter values, and sometimes, they could

also be forced by the process dynamics.

This work highlighted not only the fact that the complexity of an accident scenario

dynamics arises from the interactions between the plant and operator responses,

but also that the variants of the scenario could have very different dynamics

depending on scenario’s characteristics; which, by the way, is in line with the

conclusions of the other papers examined.

Indeed, the analysis’ outcomes resulted to be consistent with the ones found in

literature and the ones reached adopting analytical approaches, so confirming the

validity of the method.

In order to conclude the discussion about dynamic tools, it is important to mention

that, recalling the fact that even though Markov models are generally adopted to

solve dynamic gates in Dynamic Fault Tree, with increasing system’s complexity

it is more suitable to opt for a Monte Carlo simulation; during the literature

research a validation of this statement, and of the goodness of the results deriving,

was found in “Quantitative analysis of dynamic fault trees using improved

Sequential Binary Decision Diagrams” (Rao et al. 2009).

2.3.4 Gaps in literature

The review of the extant literature revealed that no specific simulation software

tools have been developed for Dynamic HRA in healthcare so far; so, the basic

guidelines found in literature were taken in order to properly model an ad hoc tool

for our application.

Moreover, models and values documented in literature to support different phases

of a Dynamic HRA were assumed as a starting point, but required a critical

evaluation and selection in order to be transferred in healthcare from their domain

of origin. For example, existing knowledge on recovery failure probabilities,

obtained from industrial applications, could not be adopted directly, since the

context of error recovery is totally different.

Another aspect missing in previous documented studies is also a dynamic

quantification of recovery probabilities; in fact, even in those studies where

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recovery was taken into consideration, i.e. (Jang et al. 2014; Jang, Jung, et al.

2016c; Jang, Jung, et al. 2016a), and analysed through PSFs/RIFs (as Time

urgency, No supervision, and High level of human-machine interface), the latter

were considered only statically, hindering reliable, and most of all, complete

evaluations of the evolving scenario.

Furthermore, the aforementioned studies’ results strongly depend on the statistical

basis on which the whole discussion was built on; this requirement strongly

hampers the implementation of these techniques, especially when it comes to

introduce additional data regarding recovery, since the production of reliable

database always requires time and, in some cases, is an out-of-reach requirement.

2.3.5 Further developments in the Healthcare sector

According to National & Falls (1996) and Gil et al. (2011), the step-wise

procedure to perform a dynamic risk analysis is:

1. Identify the scenario (mental models, interfaces SHELL, IFs, …);

2. Generate a task flow diagram including recovery paths;

3. Define the tasks/subtasks and the variables involved (also

hardware, diagnosis, quality and planning state can be identified

for each node);

4. Chose the HRA method through which evaluating the HEP for

each task/subtask;

5. Carry out the simulation pointing out the level of detail of the

system.

Actually, for almost each of these points there is still a lot of work to do. For what

regards the first point it could be interesting to start the development of wide

ranging scenario templates available, together with the relative modelling and

computational software. The standardization, under proper hypothesis, of well-

defined scenarios would pave the way to wider scope tools, still preserving

flexibility. In fact, in this way we would privilege the customization of the

computational part of the structure (e.g. the DET ramification and task

descriptions), preserving the one of the generic tool. In this way it would be

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possible fostering the reduction, in number, of existing models, which will be wide

ranging (for what regards a single field of application), instead of following the

actual trend of generating ad-hoc solutions for each possible system/case.

For what regards the second point, not much can be done to automate the

generation of trees, but it would be of great benefit to step up efforts for creating

standardized trees for all those operations subject to this kind of studies, as we are

proposing for Surgery, in order to facilitate analysis. This would help in promoting

safe procedures and corresponding safety measures in a coherent and effective

way.

As mentioned before, in the paper «Modelling human reliability analysis using

MIDAS» (Boring, 2006) the initial efforts to translate HRA to human

performance simulation were investigated.

This process has required a re-thinking of several fundamentals of HRA, ranging

from dependency to PSFs’ characterization; but the key point to consider when

approaching dynamic HRA for sure concerns the role of PSFs.

While static HRA models do not imply the fact that a change in one PSF affects

other PSFs along with the event evolution, dynamic HRA must consider PSF

latency (i.e. once activated a PSF will retain some activation across tasks) and

momentum (i.e. the propensity of the antecedent PSF to change).

This whole topic has much to do with the third of the suggested steps for Dynamic

HRA. In fact, if we want to investigate the evolution over time of a process we

cannot neglect the way PSFs change over time. In this sense, it is fundamental to

proceed in a systematic manner and it would be also useful to standardise the types

of PSF updates to be considered: for instance, the three behaviours identified by

Boring (2006) and cited in previous paragraphs could be adopted: Static

Conditions, Dynamic Progression, and Dynamic Initiator.

The introduction of the time domain and of system’s dynamics in HRA is the main

challenge in terms of innovation of safety evaluation techniques. But there is still

a lot of work to do in this sense both in terms of dependency and links’

formalization between tasks and PSFs, and in terms of quantitative estimation, i.e.

readjustment of the algorithms involved in the different HRA techniques.

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The choice of the HRA technique is a very sensitive spot; obviously, there is not

just one technique prevailing over others in all respects, but each of them has its

own pros and cons.

An interesting development in the description of the tight coupling of events in

real life would be well represented by the massive investigation of dependencies

relating tasks and PSFs.

During our literature search four aspects were identified as relevant in order to

determine the level of dependency between tasks: Spatial closeness, Checking

systems’ similarity, Similarity of the gesture, and Time coupling.

The concept of “Spatial closeness” is easily understandable, generally refers to

the area were the robotic arms are operating and, we could say, to the relative level

of precision required in order not to affect the work already done or coming next.

The notion of “Similarity of the control systems” refers to the similarity of the

clinical, or not, parameters able to influence critical patient condition or the

occurrence of failure. When this kind of dependence is present it could be difficult

to individuate the failing subtask; and so, the causes of the failure if the error is

not immediately detected.

The “Similarity of the gestures” involved in two consequential subtasks can lead

to a double error; due to an increment in performance anxiety; or, eventually, to

slips; since having just performed the same action could induce the operator to an

easy-going approach.

The “Time coupling”, instead, covers the aspect of the length of the time interval

that can elapse between the two tasks. If the tasks are considered coupled it means

that no, or very short, delay is allowed between the end of the first and the

beginning of the second action; thereby, if we have this condition the failure of

one of the two is made more probable by the fact that they are in sequence.

After having validated the fact that these, and only these, are the meaningful

aspects to be considered in a dependency analysis for a surgical procedure,

experts’ could be asked, for each of these parameters, to express how the outcome

and the presence of a certain subtask can affect the probability of occurrence

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and/or failure of another subtask, so as to create a dependency tree and allow the

distinction between Complete, High, Moderate, and Low dependency.

Having in mind the specific meaning of the dependency factors and the Recovery

Influencing Factors (RIFs), we can think about reporting this result in the HEP

formulation by introducing a dependency coefficient “k” according to the model

adopted in THERP for treating dependency. This can be done by starting from the

discussion made by Jang et al. (2016).

In the following lines, we will present a set of formula combining THERP’s

approach to dependency (though the introduction of “k”) and the extremely

flexible and common HEART algorithm. This conjunction is of particular interest

from our point of view since the latter, i.e. HEART, is the methodology we

selected for our study, and in general for HRA application to surgery, as will be

illustrated later on; the following formulas are suggested in order to perform the

risk analysis:

RFP = (1-D0)*∏ [(1 + 𝑘)1

1+𝑘(1 − ∑ 𝑅i𝐸i)𝑖≠0𝑖 ] (1)

Ri=1 - ANLUi = 1 – (NHU ∗ ∑ 𝐴𝑠𝑠𝑒𝑠𝑠𝑒𝑑_𝑅𝐼𝐹_𝐴𝑓𝑓𝑒𝑐𝑡𝑛𝑖=1 i) (2)

Assessed_RIF_Affecti = [(RIFMultiplieri - 1) ∗ PoAi] + 1 (3)

Equation (1) refers to the calculation of the Recovery Path Probability (RPR),

where the subscript -i defines the task under evaluation, “R” and “E” the recovery

and the error probability associated to relative task respectively, while “D0“ is the

term representing the detectability of the failure from which the recovery path

starts in the first place. In particular, “E” can be evaluated through the Assessed

Nominal Likelihood of Unreliability (ANLU), as prescribed by HEART. Finally,

for what specifically regards the application of HRA techniques to Healthcare it

is evident from the literature research brought up that all previous aspects

remained unexplored; plus, there is still need for validating database, suitable

techniques, well-defined procedure, and taxonomy.

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2.4 Study objectives

The primary objective of this study was to create a prototype for a quantitative

risk assessment methodology able to take into account the main recovery paths

relative to a specific surgical procedure, trying to combine some of the

developments proposed both from a recovery analysis and a dynamic risk

assessment point of views.

Given the fact that in medical literature only standard procedures are available,

and that inexperienced surgeons only rely on good sense when a failure occurs in

the operating theatre, at first, we outlined the most efficient and frequent recovery

path through experts’ interviews, only for those tasks that previous studies already

identified as the most critical ones.

Even though it would be of great interest to have a DET considering the dynamics

of the scenario involved through dependencies and PSFs’ evolution for the

procedure under analysis, this work only addressed the evaluation of a traditional

DET due to the complications involved in the introduction of such characteristics.

The final probability is relative to the specific recovery path, and assessed through

an ad hoc modified HEART technique. The assumptions and the algorithm

involved is commented in the Study Methodology chapter and the full script is

available in Appendix 5.

Due to the high complexity and heterogeneity of the surgery process, we were

forced to proceed relying on qualitative and subjective judgements elicited by a

sample of expert surgeons, without having the possibility of validate these data

with statistical analysis of real observations, as suggested in literature.

The dynamic aspect of our investigation will be covered by a specific simulation

tools; in fact, we will provide a model able to reproduce the evolution of the

procedure along the recovery paths by launching Monte Carlo simulations and

making the PSFs and probabilities range between upper and lower limit value

according to triangular or rectangular distributions, and analysing their impact on

the recovery branches’ probability and outcome grade.

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Another interesting element was the inclusion of a path clustering step,

considering that each path is characterized by a certain «level of success», which

depends on the final patient’s condition (outcome). To this end, we asked experts

to identify the level of Patient Outcome according to the Clavien-Dindo

classification (Dindo et al., 2017; Mitropoulos et al., 2013).

To test the proposed methodology and tool in a real case, and demonstrate how

they should be implemented, they were applied to a specific Radical Robotic

Prostatectomy surgical procedure (BA-RARP) which could also serve as a

reference for future validations.

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CHAPTER 3: THE EMPIRICAL SETTING

3.1 Introduction

In the last decades the interest in patient care and safety has been growing in face

of the enormous progress of medicine and of the increasing awareness of the

possible drawbacks due to the mismanagement of preventable accident.

Together with the technological growth we have seen the birth of new surgery

techniques such as Minimally Invasive Surgery (MIS).

MIS is mainly characterized by the increasing support of technology for the

surgeon; fascinated by the promise of less invalidating surgeries, with all the

annex benefits, the scientific world has put many efforts in developing the

interface existing between equipment and surgeons in the most effective way.

This evolution not only concerns physical and software modifications but also

requires a change in the organisational aspect of the operating room: from equip

management to policies.

Modern surgery not only seeks to treat the patient, but at the same time tries to

minimize the possible consequences from both the patient and the hospital point

of view.

The main objective is to get minimum repercussions on the patients being the least

invasive, which also implies shorter hospital stay and less lawsuits, so resulting in

significant money saving.

From this last consideration, we see that the improvement of patients’ conditions

goes with the optimization of resources, and the results obtained in the last years

definitely justify the interest for the subject, even though there are conflicting

opinions on the size of the improvement.

In particular, MIS market has been catalysed by the DaVinci robotic system,

which is nowadays a worldwide accredited excellence in the Advanced Healthcare

Systems sector. Also Italy has ride the wake of robotic surgery, with considerably

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good results, being now one of the leading countries in Europe. In the next chapter

we will discuss in detail the aforementioned topics starting from the big picture

and ending with the specific application of our interest.

3.2 Minimally Invasive Surgery

Since the nineteenth century the technological evolution had an incredible impact

and a wide range of applications in the medical-surgical sector. From its birth,

Endoscopy has radically changed its role in the healthcare landscape, moving

from being a purely diagnostic to a fully-fledge surgery technique.

The advent of this technique represented a real revolution in the history of surgery

and, together with the huge evolution of instrumentation and anaesthetic, plus the

X-rays discovery, constituted a big step forward in terms of patient care but most

of all produced a drastic reduction of risk in Surgery.

As a consequence, the traditional open surgery has become an obsolete procedure

adopted only in very extreme conditions and not advisable in most cases, giving

the way to less invasive and safer techniques.

Minimally Invasive Surgery includes endoscopic, laparoscopic and more recently

robotic surgery, which requires a separate discussion.

The first application of Endoscopy as a surgical technique dates back to 1987,

when Lyon, Philippe Mouret performed the first successful cholecystectomy on

humans; from that moment on we can say that the evolution of MIS has proceeded

relentlessly, and what continues to foster its developments are the numerous

scientific evidences of the benefits of this discipline, especially in Oncology.

Laparoscopy is mainly used to work with those organs contained in the abdominal

and pelvic cavity; thoracoscopic, for organs contained in the thoracic, and those

interventions within hollow organs, such as transanal, transoesophageal and trans

gastric surgeries.

The clue of this kind of surgery is to minimize the interference between the

instrumentation and the organism: accessing the organs of interest through small

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incisions by means of specific instruments and video systems, thus minimising

the number and entity of surgical traumas on patient.

Anyway, the significant advantages supporting the spreading of this method are

not only related to minor surgical inference on the body, which results in quicker

and less painful postoperative course, minor exposure to infections and faster

rehabilitation of the patient; but also to the aesthetic aspect that is gaining more

and more importance in order to psychologically overcome the experience.

Talking about the costs of the operation, all things considered they are not much

of a constraint. In fact, generally speaking about MIS the expense, in terms of time

and money invested on the training necessary to use this kind of technologies, is

definitely offset by the considerable economic saving due to the shorter surgery

duration and hospital stay, and to the reduction of complications both after and

during the procedure.

Since Robotic Surgery also implies the purchase of a robot a more in-depth debate

will be proposed in the dedicated Section 3.3.

The general benefits of MIS can be so summarised as:

- Small incisions;

- Less mental and physical impact for the patient;

- Less risk of infection;

- Less wound surgery complications;

- Shorter hospital stays;

- Shorter surgery duration;

- Less trauma for the patient;

- Less pain;

- Less blood loss;

- Smaller scars.

However, Minimally Invasive Surgery is not a totally risks-free practice, it is

possible to have intraoperative complications, even very serious ones, mostly due

to surgeons’ initial lack of experience in using complex technology devices, poor

coordination between team members or inadequate equipment and ergonomics of

the workspace.

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The ergonomic aspect has proven to be very important for this technology , in fact,

several studies show that the majority of laparoscopic surgeons report neck, back,

shoulder or hand pain; and it has been reported that 87% of them regularly

experience musculoskeletal pain during or after laparoscopy (Zihni et al., 2014).

Not least, the use of minimally invasive instruments (e.g. trocars) denies surgeons

the tactile feature of the operating gesture (Cao & Rogers, 2006).

These limitations can be overcome through training activity, involving the use of

simulators to pc, box trainer, educational videos, etc. (Guzzo & Gonzalgo, 2009).

The development of these simulation supports has the aim to reduce, as much as

possible, the gap between more experienced surgeons and beginners, aside of

minimizing intra-operative complications associated to MIS surgery in general.

Anyway, often surgeons agree to define laparoscopic surgery, and in general MIS,

as more stressful than open surgery due to the visual and instrumental obstacles,

the higher level of concentration required and the demanding, as well as

necessary, training program (Guzzo & Gonzalgo, 2009; Berquer et al., 2002).

In conclusion, the most significant disadvantages of this technology can be

summarized by following aspects:

- The surgeon needs to move the instruments watching his gestures through

a monitor;

- Expensive and special equipment is required;

- Maximum hand-eye coordination is required, worsened by the fact that the

laparoscope is usually operated by an assistant;

- The coordination of the operating surgeon is incredibly disturbed by

external factors;

- The freedom of movement is strongly limited;

- The tactile sensation is gravely undermined or nullified;

- Ergonomics problems;

- MIS requires additional safety concerns and precision requirements

compared to traditional open surgery;

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- Only 2D visual feedback is available for Laparoscopic applications.

In view of those considerations, it is necessary that trainees and surgeons, before

approaching MIS for the first time, acquire skills in performing surgical

procedures involving a minimally invasive access and get familiar with handling

the dedicated instrumentation, which is totally different from the one used in

traditional open surgery (Hamad, 2010). In Figure 2 we can see that the proportion

between the rate of use of MIS, Robotics and Open procedure in different settings

can vary sensibly, and we can also appreciate the fact that Prostate Surgery is the

case for which Robotics has its wider field of application.

3.3.1 DaVinci Robot

The DaVinci Robot enables surgeons to operate with enhanced vision, precision,

dexterity and control with respect to any open, laparoscopic or endoscopic

surgery.

This is mostly due to its 3D high-definition vision system, with magnification up

to 15x and special wristed instruments that bend and rotate far greater than human

Figure 7: Proportion of use of MIS, Robotics and Open procedure in different setting

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wrist. This system incorporates the patented technology EndoWrist, which

reproduces the movement degrees of freedom of surgeon forearm and wrist during

the operation, providing up to 7 degrees of freedom (Ficarra et al., 2010).

The DaVinci System allows a great versatility of movements, providing access to

narrow and deep anatomical spaces (not always possible with laparoscopic) and it

gives highest surgical accuracy that cannot be compared with other techniques. In

addition, the 3D visualisation, freedom of instrument movement and intuitiveness

of the surgical motion are able to restore hand-eye coordination, which is usually

lost in laparoscopic surgery (Al-Naami et al., 2013).

The DaVinci robot is the most advanced Minimally Invasive Surgery system in

the market, and it is available in two versions:

- Da Vinci Si: arrived on the market in 1999 and considered the gold

standard for medium complexity procedures in urology, gynaecology and

general surgery in a single quadrant;

- Da Vinci Xi: an innovative system, introduced in Italy in 2014; it is the

ideal tool for highly complex surgery and multi-quadrant surgical fields,

allowing extreme freedom of movement. These features make it suitable

for operations in the field of urology, gynaecology and general complex

surgery, maximizing anatomical access and guaranteeing a 3D-HD vision.

The Da Vinci surgical robotic system is a master-slave remote-controlled system,

consisting in a console, where the operating surgeon (master) directs the robotic

surgical arms (slave) from a computer-video console (Ficarra et al., 2010).

One of the robotic arms holds the video scope, which provides binocular vision of

the operative field, while the others hold instrument adapters to which specialised

robotic instruments are attached. All instruments have articulated elbow and wrist

joints, enabling a range of movement which mimics the natural motions of open

surgery.

The surgeon directs the robotic arms using master handles which are locate below

the video console and transmit the exact motions of the surgeon’s hands to the

robotic arms, filtering hand/arm tremor and providing feedbacks.

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Additional videos can be positioned, inside the operation room, to facilitate the

work of the rest of the staff working at the operating table.

Figure 8: Typical set-up of robot system in operating room (a) sketch (b) real-life

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The Main assets of the Da Vinci robotic system are the following:

➢CONSOLE

The console provides the computer interface between the surgeon and the surgical

robotic arms. It is positioned outside the sterile field, and represents the control

centre of the system, where the surgeon controls the 3D endoscope, the EndoWrist

instrumentation by means of two manipulators (master) and the pedals.

As mentioned before, the surgeon’s hand movements are digitised and transmitted

to the robotic arms which perform the identical movements in the operative field,

for safety reason, the robotic arms are automatically deactivated whenever the

surgeon’s eyes are removed from the display. On the other side, pedals are used

to activate the electro cauterizer and the ultrasonic devices; and for relocate the

master handles when necessary.

The surgeon can also chose to switch between full-screen view to a multi-image

mode, which shows the 3D image of the surgical field together with two other

images (ultrasound and ECG), providing auxiliary inputs.

➢ROBOTIC ARM CART

The robotic arm cart is placed beside the patient on the operating table, holding

the robotic arms on a central tower; one arm holds the video scope and the others

are used as supports for the instrument which are applied to robotic arms’ ends

through reusable trocars.

The DaVinci system makes use of the remote centre technology, defining a fixed

point in space around which the robotic arms move (Tooher & Pham, 2014). This

technology allows the system to manipulate instruments and endoscopes within

the surgical site, while minimizing the force exerted on the body of the patient. It

is also possible to perform manual positioning, in terms of height (relative to the

base) and advancement and rotation of the group of arms up to a maximum of

270°.

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➢CART VIEW

It contains the central processing unit of images; which includes a 24-inch

touchscreen, an ERBE VIO dV electrosurgical for delivering unipolar and bipolar

energy, and adjustable shelves for optional auxiliary surgical equipment, such as

insufflators (DaVinci System Xi also includes a full HD video).

➢SURGICAL INSTRUMENTS AND ENDOWRIST™

The EndoWrist® devices of DaVinci Xi have a diameter of 8mm and a length of

about 60cm. They are equipped with a wrist that allows a freedom of movement

on 7 axes and a rotation of almost 360°, mimicking the natural motions available

in open surgery.

There is a range of different tools available: needle holders, graspers, scissors,

small clip applier, micro-forceps, long tip forceps, ultrasonic shears, cautery with

spatula, scalpel cautery, bipolar dissectors of different types and so on (Tooher &

Pham, 2014); and each of those can be used up to ten times before being replaces.

3.3 Robotic Surgery

Robotic surgery represents the latest frontier of Minimally Invasive Surgery.

Thanks to robotics it is possible to overcome many of the limitations observed in

the laparoscopic case, extending the benefits of Minimally Invasive approach to

extremely complex surgery procedures.

The first surgery robot prototype was developed in the 80’s by the American Army

and NASA, but only in the 1995 it was further expanded by two American

companies Computer Motion (Goleta, CA) and Intuitive Surgical Inc. (Mountain

View, CA).

These two companies have merged in the 2003 constituting the Intuitive Surgical

Inc., which cornered the market thanks to the DaVinci ® system.

The DaVinci robotic system has received the FDA approval in 2000, and was

rapidly adopted by hospitals all over the United States and Europe for the

treatment of a wide range of conditions. Up to June 30 2014 3,102 robotic systems

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were installed all over the world; of these: 2,153 in the United States, 499 in

Europe, 183 in Japan, and 267 in the rest of the world.

The extent of robotic surgery practice varies widely due to a variety of factors

implicated (i.e. physician training, equipment availability and cultural factors).

Over the years several applications have been developed in oncology,

gynaecology, orthopaedics, maxillofacial, thoracic, paediatric, ophthalmology

and cardiac surgery.

Robotic surgery, or robot-assisted surgery, allows doctors to perform many types

of complex procedures with more precision, flexibility and control; which may be

difficult, or impossible, with other methods (Al-Naami et al., 2013).

As already mentioned, robotic surgery has the goal to overcome limitations of

laparoscopic surgery; for instance flat two-dimensional vision, inconsistencies in

instruments movements, unnatural surgeon positions, dissociation between vision

and instrument control and inability to carry out micro sutures.

Thanks to a computer and a remote handling system, the surgeon is able to

reproduce the movements of the human hand in the surgical field (Al-Naami et

al., 2013).

The most widely used clinical robotic surgical system is composed by one camera

arm and several mechanical arms with surgical instruments attached to their ends.

The centre of action, if we can say so, is the desk of first surgeon, the one

responsible for the robot operation, which consists in a computer console,

detached from the operating table, from which he/she controls the robotic arms

relying on the high-definition, magnified, 3-D view provided by the cameras.

Indeed, it is from this location that the surgeon leads the rest of the team members

who assist the surgery at the operational table (Binder et al., 2004; Al-Naami et

al., 2013).

One of the most important, and probably the most appealing, aspect of the

adoption of this technique is for sure the impressive cost saving deriving.

Despite the fact that robotic surgery requires a large initial investment (in the order

of US$1 million to US$2 million); an on-going annual maintenance (costing

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approximately US$250,000; and disposable or limited use instruments (e.g.

shears, needle drivers, graspers, forceps; with an average cost of approximately

US$2,000 per instrument), which are replaced every 10 surgeries versus the

mostly reusable instruments in open surgery; many reports have shown that the

overall hospital costs were significantly lower for robotics compared with

traditional surgery, and that, in some cases, the hospital could break even on their

robotic investment after as few as 90 surgeries.

In fact, not only is Robotic Surgery already cost-effective for insurance companies

and hospitals and a better option for the patients recovery, but as robotic

technology expands and improves, as is the case with most other technologies,

costs will further decrease – it is only a matter of time before that is passed on to

‘consumers.’

The evidences of these statements are many and vary, and a list of the independent

articles and studies showing the cost-efficiencies and positive impact of robotic

surgery is proposed in a dedicated section of the Bibliography.

3.1.1 Benefits and limitations

The Da Vinci robotic system offers several advantages, compared to open

and laparoscopic surgery, for both operators and patients (Tooher & Pham,

2014), as shown in the table below.

Table 5: Major clinical and patient's advantages with DaVinci system (Ab

Medica website)

Major clinical advantages Major patient advantages

• Ease of access to

difficult anatomies;

• Excellent visualization

of anatomical

landmarks;

• Small incisions with

mild bleeding;

• Less need for blood

transfusions;

• Less postoperative pain;

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• More detailed view of

the cleavage planes;

• Greater precision in the

procedure;

• Greater accuracy;

• Ability to configure the

accuracy of motion

surgery.

• Reducing hospitalization

time;

• Reduced recovery times;

• Faster recovery of

normal

activities.

The DaVinci system has several safety devices: for instance, when the camera is

moved and repositioned the tools remain stationary; moreover, the system

automatically enters “standby mode” also when the surgeon removes the head

from the console; and tools can be stopped during the repositioning of the robotic

arms.

This does not mean that the robot replaces the surgeon, but that it becomes its

extension and reinforcement constituting an important technological aid; in fact,

experience keeps its fundamental role in the assessment, selection of information

and execution of the various tasks.

In order to get the best from Robotics it is important to properly assess the status

and condition of the patient, his/her disease and the "risk class" it belongs to; in

fact, for some patients/cases robotic surgery is definitely not suitable,

unnecessarily expensive and perhaps even more risky than the traditional one.

Aside of the various benefits that Robotic surgery offers over conventional

laparoscopic or open techniques, there is a significant learning curve and

substantial investment involved (Binder et al., 2004; Al-Naami et al., 2013).

In fact, surgeries made by means of the Da Vinci may encounter severe

complications, as any other surgery, which may require prolonged and/or

unexpected hospitalization and/or reoperation. Examples of serious or life

threatening complications may be: injury to tissues/organs, bleeding, infection and

internal scarring that can cause long-lasting dysfunctions and pain.

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The major issues of this technology are related to the fact that hardware and

software updates are required, as with any computer-based equipment; but

additional limitations of the DaVinci robotic surgery can be identified in the

robotic set-up (mainly time related) and equipment size; in familiarisation with

the robotic system (primarily related to the learning curve and lack of experience);

and in communication problems between the operating surgeon and the rest of the

surgical team, particularly the surgical assistant (Cao & Rogers, 2006).

Robotic surgery undoubtedly disrupts the existing workflow and introduces

modifications in the roles of every team member and teamwork, since it is based

on a new way of conducing surgery (Lai & Entin, 2005). Other technical

difficulties that may be encountered are related to the malfunction of the system,

or collision of the robotic arms either with the patient, the surgeon or with each

other, or instrumentation issues (Binder et al., 2004).

Despite the improvements, there are still some unresolved problems typical of

minimally invasive surgery: the assistants at the table, for example, remain

confined to a two dimensional view, and the high number of cables and wires

inside the room, necessary to connect the various components of the system, can

be dangerous both for the staff members and for the surgery itself, that can be

compromised producing a negative effect on the patient.

Main surgery risks can be attributed to equipment failure and human error; and as

reported from Intuitive Surgical, specific risks include the following conditions:

temporary pain/nerve injury associated with positioning; temporary

pain/discomfort from the use of air or gas during the procedure; longer operation

and time under anaesthesia due to the possible conversion to another surgical

technique, additional or larger incisions and/or increased complications.

3.3.3 Robot applications

Reports from abmedica® , Italy’s leading company in the production and

distribution of medical technologies, inform that over the last decade the DaVinci

System has brought Minimally Invasive Surgery to over 2 million patients

worldwide.

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In 2014, 570.000 robotic surgeries were performed in the world, increasing of 9%

compared to 2013, and the Surgical robot device markets, which was estimated to

be around $3.2 billion in 2014, are anticipated to reach $20 billion by 2021.

Gynaecology and General Surgery have driven the growth especially in the US;

while Urology supported the robotics activities at international level. During the

2015, in Italy, there have been more than 13,200 robotic procedures, the 66% of

which concerns urological diseases; certifying the growing interest and credit in

regard of this technology.

This point is also demonstrated by the increasing number of installations on the

Italian territory, which now counts for more than 70 hospitals proposing this

technology to their patients.

The graphs below show the increase of the number of procedure in the world in

the last seven years and the DaVinci system installations’ distribution in Italy.

Since its introduction on the market, the DaVinci Surgical System has been

successfully adopted in thousands of procedures; its safety, effectiveness and

superiority in terms of clinical results are proved by hundreds of scientific papers.

Figure 9: International increase of DaVinci surgical procedures

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The DaVinci surgical procedures are routinely performed in the specialties of:

- General and Vascular Surgery;

- Uro-Gynecological Surgery;

- Thoracic Surgery;

- Cardiac Surgery;

- Paediatric Surgery;

- Otorhinolaryngology.

The table below shows the main operations performed with the DaVinci robot,

while the graph displays world installations of the DaVinci system.

Table 4: DaVinci surgical procedures

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In particular, the series specialty in the world is divided as shown in the chart:

Robotic Surgery has proved to be the best technique for surgical treatment of

prostate cancer, and nowadays, in the US, over 80% of prostatectomies are

performed with the aid of the DaVinci Surgical System.

Figure 10: Increase of DaVinci speciality surgeries in recent years

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The immediate advantages of this technology are better and faster post-operative

urinary continence and savings of optimal neurovascular bundles, with net

benefits on erectile/sexual functions (more patients return to pre-surgery erectile

function at 12-month check-up). Moreover, the use of DaVinci robot in

prostatectomy surgery allows to have more precise removal of the cancerous

mass, less chance of nerve and rectum injuries, less risk of deep vein thrombosis,

lower risk of complications and shorter operating time (Rashid et al., 2006).

The introduction of robotics can offer to the patient radical grubbing of the cancer

and low impact on the quality of life and earlier return to normal activities, thus

improving the overall outcome of the procedure and satisfaction of the patient.

The chart above shows the global growth of robotic presence in Healthcare sector;

in addition, being DaVinci system the leader product in this field the trend

represented gives us an idea of its broadcasting.

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CHAPTER 4: STUDY METHODOLOGY

4.1 Introduction

As Kirwan & Gibson (2007) stated, if a system engineer can identify that a system

component will fail with a certain frequency, the human factor community needs

to be able to state whether the human component will be more or less reliable.

The aim of this chapter is to provide scientific evidences and illustration of the

various boundary conditions involved in our case study, and of the methodology

through which we quantitatively evaluate our model.

As we pointed out many times in our discussion the first thing to do is to highlight

the main characteristics of the scenario we are addressing through extensive and

specific use of PSFs. The key aspect of this chapter will be to prove the

consistency of our methodology; indeed, the most important aspect of this part of

the work is the adoption of a systematic approach; that we will have to apply in

tackling every aspect of the case study.

The points we have to address in order to justify our analysis are:

- The estimation of the Proportion of Affect (PoA) of the Influencing

Factors (IF);

- The individuation of the Error Modes (EMs) and the estimation of their

relative probabilities;

- The individuation of the Generic Task Type (GTT) involved in the

procedure according to HEART;

- The development of the algorithm for the calculation of the DET;

- The definition of the Patient Outcome classification.

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4.2 Dynamic Risk Assessment - preliminary phases

Of course, for starting our work we had to undergo several preliminary phases

since the elements needed to implement a study like the one we are approaching

to are numerous.

The first issue we will cope with is the one regarding the identification of the type

of data we will base our analysis on.

The main types of data sources available for HRA are experiments, experts’

estimates, empirical acquisition from real-life or accidents experience, and

simulation studies. The major issue in dealing with the Healthcare sector is that

reliable data are missing so opting for experts’ estimate is the only choice.

Several reasons have been individuated for this lack of useful information in

literature, and all the researchers agree on the fact that serious measures must be

taken to change the blaming attitude which makes the personnel reluctant to

objectively report accidents; and to increase the awareness of the effectiveness of

HRA tools starting from higher organization to single actor level.

As said before, in this kind of environment the best thing to do is to simply rely

on experts’ judgements, since taking into account any past data could lead to

misleading conclusions according to the afore mentioned issues. At second, it is

necessary to identify the level of detail of the procedure task analysis but also of

its branching. It is only at this point that we are able to associate the various failure

probabilities to the specific tasks of the process.

In our discussion, we will try to adopt a linear approach and to simplify as much

as possible the level of resolution of the problem preserving the description of the

standardise procedure already used in previous studies and keeping the recovery

path size reasonably small; combining the analysis of expert surgeons with the

need of simplicity required for the quantitative evaluation.

To perform effective risk assessment, aside a properly defined task analysis, it is

also necessary to have a specifically defined taxonomy, or at least adaptable to the

context under evaluation, at disposal.

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All these issues will be addressed in the following paragraphs, still trying to keep

the discussion general, for what regards Surgery applications; while in Chapter 5

the application to the specific case (BA-RARP) will be illustrated.

4.2.1 Task flow diagram and recovery paths

The task flow diagram of the main procedure was taken from (Trucco et al. 2017);

and is available for consultation in Appendix 2 and 3; while the step forward we

did in this work was to add recovery branches stemming from the most critical

tasks.

The most critical tasks were individuated in previous studies as “Isolation of

lateral peduncles and of posterior prostate surface”; “Santorini detachment from

the anterior surface of the prostate”; and “Anastomosis”.

In order to identify the most relevant recovery paths associated to these tasks we

collected the opinion of three surgeons through standardized and ad-hoc

interviews, whose text is also available in Appendix 7.

4.2.2 IFs and IFs’ impact definition

The PSFs to be considered has been elicited by three steps: literature review of

the PSFs taxonomies with particular focus on domains with high complexity and

human engagement; comparison of the different proposed taxonomies; and

collection of experts’ judgements.

As anticipated, the taxonomy relative to the Error Producing Conditions (i.e. IF

as we used to call them) will be the same outlined in (Trucco et al. 2017) and

presented below.

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Table 5 : Validated surgical taxonomy of Influencing Factors

SURGICAL INFLUENCING FACTORS

1 Noise and ambient talk

Continuous or sudden noise; team members talking in the background or coming

and going and moving around in a noisy way.

2 Music

Presence of background music in operating room.

3 Noisy use of social media

Team members talking about and obtrusively sharing social media content.

4 Verbal interruptions

Verbal Interruptions that are either untimely or not patient relevant.

5 Poor management of errors and threats to patient safety

Failure to share information promptly and openly about errors and threats to

patient safety.

6 Poor guidelines, procedures or checklists

Guidelines, procedures or checklists are inadequate: lacking, too complex, or not

at right level.

7 Rude talk and disrespectful behaviours

Derogatory remarks, behaviours showing lack of respect of OR team members,

shouting and harsh tones of voice.

8 Improper use of procedures and checklists

The improper use, or non-use, of the WHO checklist (or similar), protocols and

procedures.

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9 Unclear or failed communication

Communication that should have been given wasn’t or was inadequate or was

misunderstood and not corrected.

10 Poor or lacking coordination

Failure in coordinating team activities; failure to anticipate the needs of the lead

surgeon or lead anaesthetist (surgeon at the console in robotic surgery).

11 Poor decision making

Failure to consider, select and communicate options; inadequacy or delay in

implementing and reviewing decisions.

12 Poor situation awareness

Failure to gather and/or to integrate information or failure to use information to

anticipate future tasks, problems and states of the operation.

13a Lack of experience of surgical team colleagues

Lack of experience of within surgical team, with the surgical procedure or

technology.

13b Lack of experience of anaesthetic team colleagues

Lack of experience of within anaesthetic team, with the anaesthetic procedure or

technology.

14 Fatigue

Mental fatigue or physical fatigue.

15 Time pressure

Psychological stress resulting from experiencing a need to get things done in less

time than is required or desired.

16 Poor leadership

Failure to set and maintain standards or to support others in coping with pressure.

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17 Team member familiarity

Team members unfamiliar with each other and each other’s competencies.

18 Poor use of technology

Lack of ability to use relevant technology.

19 Inadequate ergonomics of equipment and work place

Equipment and workplace not designed to optimize usability and reduce operator

fatigue discomfort.

20 Preoperative emotional Stress

Stress caused by factors not directly related to the team, the characteristics and

evolution of the surgery, such as responsibility for the budget and other business

objectives, organizational problems of the department, other critical patients or

legal cases.

The surgical validated taxonomy was previously obtained through the following

phases (Trucco et al. 2017):

- Literature research of Human Factors in laparoscopic and robotic surgery;

- Identification of factors to place into macro categories;

- Observational activity of different laparoscopic and robotic surgeries

(face validity): all the elements found in literature were observed in the

surgical context too;

- Surveys and focus group with surgeons (in the Italian and Danish context):

discussion and confrontation with surgeons regarding meanings,

definitions and wording;

- Determination of the final taxonomy and validation from surgeons.

During the discussion of previous chapters regarding literature review, we did not

mention the work brought up by one of the PhD of the Politecnico di Milano,

Rossella Onofrio, specifically oriented in the direction of creating a statistical

ground for the definition of HEART’s weights in Healthcare. The main result of

this study, from the point of view of the work presented here, is the construction

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of triangular probability density functions, one for each of the 20 IFs, through a

national scale survey specifically regarding surgery applications.

For this study the list showed in Table 5 was presented to the surgeons involved

in the survey. They were asked to choose as many of these factors they considered

meaningful in the evaluation of recovery probability and to what extent, adopting

a range from 0 to 100 in order to allow higher resolution with respect to the

traditional 1 to 10 scale. The research has involved more than 200 surgeons, and

the resulting distributions are shown in the picture below.

Figure 11: Plots of the triangular pdf of IFs in surgery

As said before, the reason why we opt for HEART is that in the past decade it has

been the principal tool used to quantify the reliabilities of human interactions

(Williams, 1986).

Whilst this technique has served well, it was developed many years ago and it has

remained principally the same technique, based on the same original data (Kirwan

et al. 2016). Since, as suggested in the literature review, this technique did not

always 'fit' very well tasks assessed, for example for the NPP and ATC case. It

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was therefore felt that a redefinition of the Error Producing Conditions (EPCs)

involved, and of the relative multipliers, could be developed based on more recent

and relevant data.

We have shown the lists of the modifications proposed by the NARA and CARA

tools in the previous chapter, and since these are the guidelines for future

researches, we will provide an example of the adoption of these more recent

suggestions we readapted for Surgery application in the sections regarding the

quantitative evaluation and the comment of the results.

In particular, the following table shows the discrepancies between the multipliers

involved in the different proposals: HEART, NARA and CARA.

Table 6: Comparison between HEART, NARA, and CARA multipliers

EPC HEART

multipliers

NARA

multipliers

CARA

multipliers

1 17 20 20

2 11 11 11

3 10 10

4 9 9

5 8

6 8

7 8 9

8 6 6 6

9 6 24 24

10 5,5

11 5

12 4

13 4 4 5

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14 4

15 3 8 8

16 3 3 5

17 3

18 2,5 2,5

19 2,5 3

20 2

21 2 2

22 1,8

23 1,6 1,6

24 1,6

25 1,6

26 1,4 2

27 1,4

28 1,4

29 1,3 2 5

30 1,2

31 1,2 2

32 1,2

33 1,15 8 8

34 1,1

35 1,1

100

36 1,06

37 1,03

38 1,02

The cells highlighted in purple represent all those EPCs involved in the Surgery

taxonomy developed by (Onofrio et al. 2015), the one that has been adopted in the

present study. The cells highlighted in blue represent those cells for which we

have different multipliers suggested by NARA and CARA’s sets, i.e. the EPC is

considered in just one of the two classifications. Finally, the orange ones are those

EPCs for which different multipliers are defined for the two tools, i.e. the EPC is

considered in both sets but with different importance. This last group of EPCs will

require the identification of proper criteria for selection, in order to adopt a unique

and comprehensive set of EPCs to be implemented in the quantitative analysis.

Not being allowed to make considerations about the numbers themselves, we must

justify our choices for the definition of a new set of multipliers according to the

similarities between the field of application we have to deal with and the ones for

which the database have been updated and upgraded.

In literature, the similarity existing between ATC and Surgery’ working

environment has been largely addressed, and we can say that from an industrial

psychologist’ perspective, anaesthesia has much in common with the aviation, air

traffic control and nuclear power generation industries. In fact, all of these high

reliability domains share safety as a prime goal and rely on having well-designed

workplaces, equipment and systems, as well as safety-focused organizational

climates. Personnel must be suitably skilled to ensure they can deal with the

demands of their complex work environments; this usually involves maintaining

awareness of dynamic situations involving multiple players, and being able to deal

with critical events in stressful, time-pressured situations characterized by ill-

structured problems, shifting goals, and incomplete feedbacks (Fletcher et al.

2002).

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Situation awareness is a vital non-technical skill and it strongly depends on the

interface systems putting the operator in contact with the object of its operations.

Even though the differences between the various applications are numerous, for

example in Surgery we do not have a rigid, well defined, and unique procedure to

be followed, the choice we made of assimilating the surgical taxonomy to the ATC

one (i.e. CARA’s one) relies on the fact that these two environments have many

more commonalities with respect to the ones between Surgery and NPP contexts.

These similarities basically concern: workplace ergonomics; the centrality of the

operator in the execution of the procedure with respect to technology; the absence

of actual technological barriers preventing accidents (e.g. in NPP we have

instrumentation monitoring and filtering human behaviour and correcting

dangerous states of the system in a completely autonomous way, while this is not

the case in Surgery and ATC); the possibility of directly, and most of the time

personally, verify the empirical state of the system through visual inspection.

The first “orange EPC” is the thirteenth (i.e. Poor, ambiguous or ill-matches

system feedback) which is involved in the evaluation of the seventh IF with a

relative weight of 30%, according to (Onofrio et al. 2015) evaluation. We can see

that from a multiplier of 4, relative to the HEART technique, we have a value of

4 and 5 respectively for the NARA and CARA cases. Considering the previous

remarks and the description of the operating room environment, it was considered

appropriate to align the new set with CARA classification, since, unlike in NPP,

in both cases we have the possibility of visually checking the response of the

object of the procedure, i.e. the patient in the surgery case.

The second “orange EPC” is the sixteenth (i.e. An impoverished quality of

information conveyed by procedures & person interaction) which completely

defines the seventeenth IF, and represents the 88% of IF eight. This EPC describes

the difficulty in keeping the sequence of steps straight in mind along the evolution

of the action; for what regards this specific topic we do not have an evident overlap

of the different applications but to be consistent with the considerations made in

previous paragraphs we will keep the CARA result.

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The third, and final, “orange EPC” is the twenty-ninth (i.e. High level emotional

stress) which covers the 70% of the twentieth IF. This is the EPC for which the

difference in maximum effect magnitude, especially between traditional HEART

multiplier and the CARA one, is more evident, which is reasonable since the

context are completely different. It mainly depends on the different role played by

technology in the two cases: while in NPP human behaviour, and occasionally

errors, are mediated by the instrumentation, this is not possible for ATC

applications. All this considered, it is clear that the emotional and personal aspects

have so much more impact on the success probability for ATC, and that the

environment closer to the surgical one is the one described by CARA.

For all the others EPCs we had no need to assume or chose between the two

different techniques, since, as said before, the suggested multipliers were equals

for NARA and CARA, or constituted an only choice since those EPCs were

considered in just one of the two taxonomies.

Finally, the new set of multipliers we worked with is reported in Table 7, where

the cells highlighted in yellow show those IFs for which the multipliers have been

modified:

Table 7: Comparison between modified HEART multipliers and new ones

IF Old

Multipliers

New

Multipliers

1 10 10

2 10 10

3 9,8 9,8

4 1,048 1,048

5 9,725 9,775

6 1,4 2

7 3,3 5

8 3,101 5,7606

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9 6,8 6,8

10 4,525 4,525

11 1,95 1,95

12 17 20

13 3 8

14 1,31 1,31

15 11 11

16 1,6 1,6

17 3 5

18 6,3 6,4

19 1,285 6,08

20 1,45 4,04

4.2.3 Modified HEART and integration with the DET framework

The modified version of HEART proposed in this section is the result of a series

of considerations and adaptation of the original one in order to make it more

suitable for Surgery application.

The adjusting phase was developed by Cordioli (Trucco et al. 2017), and even

though it would have been interesting to introduce the concepts of task

dependency and PSFs’ evolution as additional modifications of the original

technique we decided to limit ourselves to the implementation of HEART as

quantitative method to evaluate the single node probability without including the

interrelations between the several branches of the tree.

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The original and modified version for Surgery application will be now illustrated

in order to point out the several actors coming into play during the estimation of

the probabilities, and the adjustments made.

The traditional HEART method flow structure can be divided in main functional

steps, as presented in the diagram below:

The first step consists in the identification of the task under analysis (Step 1), that

in our case consists in the identification of the critical tasks. There are eight

Generic Task Types (GTTs) described in HEART method; to each of them is

associated a range for the Nominal Human Unreliability (NHU) from which the

values to be assigned to the specific task are selected; this is done according to

HEART generic categories reported in the table below (Step 2).

Figure 12: Flowchart representing main steps of traditional HEART methodology

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Table 8: Generic Task Types (GTTs) and relative Nominal Human Unreliability (NHU)

If none of these eight task descriptions fits the type of task under analysis, then

the following values can be considered as reference points:

Generic Task Proposed Nominal Human Unreliability

(5-95th Percentile Bounds)

(M) Miscellaneous task for which

no description can be found 0.03 (0.008-0.11)

The assessor chooses the relevant EPCs that mainly influence the operator’s task

performance (Step 3); paying attention not to double-count EPCs by overlaying

them on generic tasks. Subsequently, the assessor determines the Assessed

Proportion of Affect (PoA) (Step 5). Thanks to this value, rated on a scale from

zero to one in the original version, it is possible to give a measure of each EPC’s

effect magnitude. The Multiplier factor associated to each EPC is defined by

Williams as “maximum predicted nominal amount by which unreliability might

change going from good conditions to bad” (Williams, 1986). If an analyst

perceives a multitude of applicable EPCs, then the model will tend towards further

unreliability (pessimism) (Williams, 1986).

The list of EPCs selected for the primary version of HEART is here provided:

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The set of general formula used to evaluate the error probability at each critical

task, and covering the procedure’s steps from number 6 to 9) is the following:

AssessedEPCAffecti= [(EPCMultiplieri − 1) ∗ PoAi] + 1 (4)

ANLU=NHU*∏ AssessedEPCAffecti 𝑛𝑖=1 (5)

%CU =AssessedEPCAffecti : (NHU + ∑ni=1 AssessedEPCAffecti) (6)

They respectively calculate: Equation (4) the Assessed Affect of the i-th EPC;

Equation (5) the Assessed Nominal Likelihood of Unreliability (ANLU); and

Equation (6) the Percentage Contribution to Unreliability (%CU) of each EPC.

The problem of lexical precision is crucial at this point since the whole EPC

classification, and so the quantitative assessment, is strictly related to it. Also in

literature, there are evidences of difficulties due to the fact that HEART was

written with an industrial language and it is not easy to translate it for Healthcare

applications. It is evident that, if Williams’ taxonomy is straight applied, possible

Table 9: HEART 38- Error-Producing Conditions (Williams, 1986)

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misunderstanding of background and in the use of the generic error categories and

EPCs may arise during the analysis.

Together with the cases presented before of new ad-hoc tools development,

specifically NARA and CARA ones, also for the Healthcare settings a prototype

of tailor made EPCs classification was proposed; in fact, also in literature there

are evidences of difficulties due to the fact that HEART was written with an

industrial language and it is not easy to translate it for this kind of application. It

is evident that, if Williams’ taxonomy is straight applied, possible

misunderstanding of background and in the use of the generic error categories and

EPCs may arise during the analysis, in the following table the link between

validated surgical taxonomy and the relative EPCs is presented.

Table 11 Comparison between IFs’ taxonomy and traditional EPC one

Validated surgical

taxonomy Traditional HEART EPC

1 Noise and ambient talk

3 A low signal-to-noise ratio.

2 Music 3 A low signal-to-noise ratio.

3

Noisy use of social media

3

4

A low signal-to-noise ratio.

A means of suppressing or overriding

information or features which is too

easily accessible.

4

Verbal interruptions 36

37

Task pacing caused by intervention of

others.

Additional team members over and

above those necessary to perform task.

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5

Poor management of errors and

threats to patient safety

2

7

12

18

A shortage of time available for error

detection & correction.

No obvious means of reversing an

unintended action.

A mismatch between perceived & real

risk.

A conflict between immediate and

long-term objectives.

6 Poor guidelines, procedures or

checklists 26

No obvious way to keep track of

progress during an activity.

7

Rude talk and disrespectful

behaviours

16

13

An impoverished quality of

information conveyed by procedures &

person-person interaction.

Poor, ambiguous or ill-matches system

feedback.

8

Improper use of procedures and

checklists

16

32

11

9

21

14



person-person interaction.

Inconsistency of meaning of displays

and procedures.

Ambiguity in the required performance

standards.

A need to unlearn a technique & apply

one which requires the application of

an opposing philosophy.

An incentive to use other more

dangerous procedures.

No clear, direct & timely confirmation

of an intended action from the portion

of the system over which control is to

be exerted.

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9

Unclear or failed communication

8

5

A channel capacity overload,

particularly one caused by

simultaneous presentation of non-

redundant information.

No means of conveying spatial &

functional information to operators in a

form which they can readily assimilate.

10

Poor or lacking coordination 10

25

The need to transfer specific

knowledge from task to task without

loss.

Unclear allocation of function and

responsibility.

11

Poor decision making

25

17

Unclear allocation of function and

responsibility.

Little or no independent checking or

testing of output.

12 Poor situation awareness

1 Unfamiliarity with a situation which is

potentially important.

13 Lack of experience 15 Operator inexperience.

14

Fatigue

35

22

Disruption of normal work sleep

cycles.

Little opportunity to exercise mind and

body outside the immediate confines of

a job.

15 Time pressure

2 Time shortage (from Williams’

description).

16

Poor leadership

24

A need for absolute judgements which

are beyond the capabilities or

experience of an operator.

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17

Team member familiarity

16



person- person interaction.

18

Poor use of technology 6

20

19

Poor system/human user interface.

A mismatch between the educational

achievement level of an individual and

the requirements of the task.

No diversity of information input for

veracity checks.

19

Inadequate ergonomics of

equipment and work place

33

23

A poor or hostile environment.

Unreliable instrumentation.

20

Emotional perioperative stress

29

22

High level emotional stress.

Little opportunity to exercise mind and

body outside the immediate confines of

a job.

In our study, as in (Trucco et al. 2017) one, surgeons were responsible for those

steps more “judgmental and structured”: selecting the appropriate Nominal

Human Unreliability (NHU) category; associating Influencing Factors (IF) from

the surgical validated taxonomy and their corresponding Assessed Proportion of

Affect (PoA); plus the definition of the Error Modes possible for each critical task.

Since the results significantly depend on assessor’s knowledge of the task and on

his personal opinion, the three surgeons involved in the study were all well

experienced, well trained, aware of the steps of the procedure, as well as of the

order in which they should be applied.

The PoA, used to determine the extent to which each identified EPC affects

operators’ performance, was rated on a scale from zero to one hundred, differently

from traditional HEART where PoA is a value ranging from zero to one; this

choice has been defined in order to obtain a greater precision of the values. This

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is just one of the modifications the traditional tool has undergone to apply to the

Surgery application; in the table below we find the main differences between the

traditional HEART algorithm and the one we will work with.

Proposed modifications

of HEART Traditional HEART Rationale

Observational data Data collections and

comparison with similar

applications.

Availability of

standardized procedures.

Lack of accurate

quantitative human

reliability data, poor data

audit from healthcare

HRA applications.

Observational data capture

based on video recording

of the operations and

direct observational

experience in surgery

room.

- Specific taxonomy for

surgical context: 20-

Influencing Factor

Traditional 38-EPC

taxonomy for the

industrial practice

Useful list of context

sensitive Influencing

Factors ad hoc for the

clinical/surgical practice.

- Assessor team

composed by three

people

Single assessors Reduce subjectivity

aspect, heavily based on

the experience of the

single assessor.

- Group of experts on the

subject: surgeon

External expert assessor Experts with highly

specialisation in medical

domains, task, and

processes.

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The main modifications introduced in this work to the already modified version

of HEART are the introduction of the Error Modes (ME) and the association of

Patient outcome grades to each of the branches of the tree.

- Rating Scales, from 0 to

100, is used to obtain

PoA values

for each EPC

Calculation of PoA rated

on a scale from zero to

one

In this way it is possible to

take into account, more

precisely, the uncertainties

of the EPC factors.

Averaging PoA values for

each EPC allows to obtain

a balanced result.

- Assessor team is asked

to assess the amount of

PoA (PoA*) attributed to

the EPC, already

established, that better

means the examined IF

(EPC*)

Not present In this way it is possible to

create a weighted analysis

- Component tasks are

not always easily

separable, it is necessary

to identify the dimension

and complexity of each

task

Easier task analysis,

characterized by repetitive

routinely operations

Hazard zones need to be

identified that consist of a

series of interrelated tasks.

For example, in the

anastomosis, the outcome

does not depend on a

single task (suturing or

stapling) but also on

preparation of the bowel

end, ensuring a good

blood supply, anastomosis

without any tension, etc.

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The Error Modes represent the ways in which the failure of a task may occur; in

this way, we have many paths stemming from a specific task for which is possible

to define different outcomes and probabilities.

The same three surgeons interviewed for the IFs’ selection were so asked to define

a set of ME and the relative probabilities (alpha) for each critical task, so keeping

the linearity of the judgements; and then the more meaningful ones were included

in the simulation following two guidelines: keep the number of branches

reasonably low, and comprehensively describe the scenario under study.

Finally, in order to properly define the outcome of each task we selected the

Clavien-Dindo classification for Patient outcome which is the most widely

accredited one in the surgical sector, and which is articulated as follows:

4.3 Dynamic risk assessment implementation

4.3.1 DET as a tool to integrate nominal probabilities procedures and

paths

When it comes to create an interface between an extremely quantitative and little

explored world as human mind and a likely complex world as Surgery thousands

of possible considerations can be done; introducing now the simulation tool we

will also illustrate all the hypothesis on which the model is based on. The

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interested reader is referred to the full text of the on Matlab® simulation code

available in Appendix 5.

Integrating those formulas illustrated in the last paragraph with a DET structure,

a tool able to randomly generate probable path of the procedure was set up. The

numerical data we started from are: the extremes of NHU ranges; the pdf of IFs

multipliers defined by (Trucco et al. 2017); the experts’ judgements regarding the

relative probabilities of the ME (alphas); the IFs involved for each critical task;

and the grades associated to the different ME.

The first step consisted in evaluating a proper number of trials for which the

simulations had to run in order to gain reliable results. This was done arbitrarily

setting the probabilities of the various ME as constant; so implicitly imposing the

final probability of all the grades as constant as well; and increasing the number

of iteration of the simulation till the best fitting Gaussian for each of the five

grades resulted to be the same (µ=0.12; σ=0.15) with a reasonably good degree of

approximation, which led us to opt for the number of 20,000 iterations. The graphs

representing the curves obtained for the six different grades are shown in the

picture below.

Figure 13: Pdf distributions for the "homogenous" case

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For what regards the initialization of the data coming from the survey: for the

alphas, we considered continuous ranges of values, also in this case delimited by

the top and bottom values assigned by the surgeons; while, for the Patient outcome

grades we kept a range described by a discrete rectangular PDF with the lowest

and the highest judgements assigned as extremes.

4.3.3 Critical tasks identification

The HEART method application requires the identification of one or more critical

tasks on which performing the quantitative analysis.

The criticality of the tasks may be attributed to different features, according to the

person’s opinion and the context. A task may be considered critical because it

requires significant additional time compared to others, or needs to be redone and

adjusted several times, or can have serious bad consequence for the completion of

the task, for the performers (serious injuries or death) or for the system (damage

or permanently compromise system), and so on. Critical tasks may be totally

unfamiliar, performed with no real idea of consequences, or contrarily completely

familiar, highly practised or even routinely; they may be fairly simple tasks

requiring low level of skill and attention or very complicated one, requiring high

level of comprehensions and skills.

Generally, two or three tasks are chosen as the most critical ones of the procedure

and, after validation from the performer of the tasks, a specific risk analysis is

executed.

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In (Trucco et al. 2017) work a critical tasks identification process was already

performed following the phases illustrated below:

Figure 14: Phases for the Critical task identification

Starting from literature research it was possible to find studies on laparoscopic

and robotic prostatectomy in which most critical, dangerous or complex stages of

the surgical procedure clearly emerged. Literature shown that there are several

studies regarding robotics training and, from these data, it was possible to deduce

which are the most critical tasks that consequently need more training (Trucco et

al. 2017).

Subsequently, surgeon’s opinion was asked in order compare it with the results

obtained from literature; and the resulting set of critical tasks for BA-RARP

procedure was the one below:

- Isolation of lateral peduncles and of posterior prostate surface;

- Santorini detachment from the anterior surface of the prostate;

- Anastomosis.

4.4 Illustration of the simulation procedure

The Matlab® code proposed can be ideally divided in three main parts:

- Initialization of data;

- Quantitative evaluation of paths (iterative part);

- Grade’s probability distribution evaluation.

The first section of the Matlab® code, was already discussed in Section 4.3.1,

while in the second one we find the computational part defined as a for-cycle

performing the necessary number of iterations.

At this point, the first thing to do is to extrapolate random values from the

distributions initialized during the first step; i.e. the PoA of the IFs involved, and

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the NHU value; so, these values can be introduced in the formulas of the modified

version of HEART for Surgery (Cfr. Section 4.2.3).

Since we are working with a linear and additive model we can assume that no

relation is involved in the random selection of PoA values so different random

inputs are selected for each IF; still, each IF’s PoA value will be fixed for the

single run.

In this way, adopting the modified HEART set of formulas, for each run and

critical task we obtain a failure, and consequently success, probability; where the

first of these will be additionally decomposed, according to the alphas randomly

selected from the range described by surgeons’ judgments, into the probabilities

of the different MEs.

Hence, we end up with a probability vector (Prob_ME) having size equal to the

total number of Error Modes of the procedure plus three positions representing

the success probability of the single Critical Tasks.

The resulting probability of the path chosen will be defined as the product of the

elements of this vector, so it was initialized as a unitary vector and only those cells

corresponding to the randomly selected event will be filled with the probability

value specifically evaluated.

When choosing the path according to the alphas value, the simulation tool is also

selecting the Patient Outcome Grade to be associated to the Critical Task

performance; so, at the end of each iteration we will end up with three potentially

different outcome grades for the three tasks. Since in our study we did not took

care of the dependency aspect in a quantitative and formal way at this point we

made the conservative and strong, but still reasonable, assumption that the final

patient outcome related to the single iteration path corresponded to the most

severe of the three.

Finally, the last step of the code consists in the assembling of Grade Probability

vectors (Grade_final_i with i ranging from zero to five) showing the probability

distribution of the various Patient Outcome grades. They are plotted through

histograms and to each of them a best fit Gaussian is associated. Some of the plots

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obtained through the running of the simulations will be shown in the Results’

chapter.

4.5 Factor Analysis

There is evidence in literature regarding the fact that, according to personal and

environmental factors describing the scenario in which the procedure takes place,

the influence of the different EPCs and the relative probability for different end

results can be sensibly different.

This is the reason way we decided to investigate the results obtained through the

assessment of the surgeons with the ones deriving from the decomposition of the

scenario itself in order to define a hierarchy of IFs for the selected procedure and

application; to do this a Factor analysis was performed.

Factor analysis is a method for explaining the structure of data by highlighting the

correlations between observed variables called factors; it is a useful tool for

investigating variable relationships for complex concepts.

The purpose of Factor Analysis is to analyse patterns of response as a way of

getting the underlying factors influencing the phenomenon; and allows the use the

weighted item responses to create what are called factor scores.

We can say that Factor analysis is a way to take a mass of data and shrinking it to

a smaller data set that is more manageable and more understandable; so, a way to

find hidden patterns, and show how those patterns overlap and their

characteristics.

Basically, there are two kind of FA: Exploratory and Confirmatory; namely,

Exploratory factor analysis is to be adopted when one has no idea of the structure

of the dataset and/or of how many dimensions are in a set of variables; while

Confirmatory Factor Analysis is used for verification, as long as the user has a

specific idea about the kind of dataset he/she is dealing with

In our case, we will opt for a simple version of a Confirmatory FA, since we have

a clear idea regarding the kind of result we are going to obtain.

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We will hence perform a one-by-one factor analysis aiming at confirming the

correctness of simulation code through an a priori reasoning of the data through a

theoretical approach. Still, we will make some reasoning about the results

obtained in terms of description of those factors more heavily influencing the

performance of our system; and a very simple scenario analysis, obtained by

grouping the Influencing Factors into classes, will be performed.

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CHAPTER 5: CASE STUDY

5.1 Introduction

The aim of the Case Study Chapter is to provide an example of application of the

methodology proposed for the analysis of the recovery paths, and a validation of

the variations applied to the modified HEART approach for a Surgery procedure.

Since, the creation of patterns and highlighting the crucial relationships between

IFs and operation performances opens many opportunities for future research and

will for sure foster the improvement of surgical training and teaching methods.

In the previous chapter the modified HEART technique, specifically designed for

the study of error recovery for surgery applications, has been presented. Now,

starting from the valuation of the surgical context and analysing a specific Radical

Robotic Prostatectomy procedure, the methodology is applied, in order to evaluate

recovery paths’ probabilities of success in robot-assisted Minimal Invasive

Surgery. In this way, we will gain a better understanding of the dynamics of the

problem and so we will be able to propose more efficient and effective

improvements.

Today, prostate cancer, especially if intercepted in the early stages of the disease,

has the opportunity to be fully removed, ensuring high probability of recovery,

and what is even more important, of total recovery thanks to increasingly

sophisticated surgical techniques and to the use of the DaVinci robot.

Despite that, radical prostatectomy surgery in some cases could lead to serious

implications regarding urinary incontinence and impotence. These forms are often

reversible with time, but in some cases, they permanently affect patients’ quality

of life.

Nowadays, the robot-assisted radical prostatectomy (RARP) technique has

become the surgical option of choice for clinically localized prostate cancer.

Additionally, the innovative approach named after surgeon Bocciardi, hence the

name BA-RARP, passing through the Douglas space, following a completely

121

intra-fascial plane without any dissection of the anterior compartment (which

contains neurovascular bundles, Aphrodite’s veil, endopelvic fascia, the Santorini

plexus, pub urethral ligaments) allows to preserve many important nerves and

therefore plays an important role in the maintenance of continence and potency.

(Bocciardi, 2014; Galfano et al., 2010).

The young age of robotic surgery, its high technology content, and the incredible

success of the DaVinci robot in prostatectomy surgery, justifies the outflow of

resources implied by the implementation of risk assessment techniques.

Obviously, the assessment of the values necessary for the application of modified

HEART, as well as the validation of the task analysis, the choice of the critical

tasks to be analysed, the validation and description of taxonomy together with the

Error Modes definition have required the opinion of experts, i.e. robotic surgeons.

Additionally, through observation of robot-assisted prostatectomy surgeries at

Niguarda Ca’ Granda Hospital in Milan, it was possible to directly experience

operating room environment, and to record and identify those factors influencing

human performance during the various stages of the surgery and their evolution

over time.

5.2 Surgical Technique

In the last years, the robot-assisted radical prostatectomy (RARP) has gained

increasing importance, changing the general approach and understanding of the

surgical anatomy of the prostate. It has become very popular in the United States

and Europe and it has been estimated that more than 75% of radical

prostatectomies are performed using the DaVinci platform (Tanimoto et al.,

2015).

Professor Francesco Rocco, Urology Director of IRCCS Foundation at Ca 'Granda

Ospedale Maggiore Policlinico in Milan, underlines that robotic prostatectomy is

a gold standard in Italy too, thanks to three-dimensional view (as opposed to the

2D vision of laparoscopy) and precision of the instruments that reduces to a

minimum the possibility of complications (Rocco, 2014).

122

As mentioned before, in 2010 a new access to the prostate for the robot assisted

radical prostatectomy has been presented: the “Bocciardi approach” (BA-RARP),

which uses only access through Douglas, without opening the anterior

compartment and the endopelvic fascia, and without the need to dissect the

Santorini plexus (Galfano et al., 2010).

Briefly, the originality of this technique is to use a fully posterior approach,

without opening the Retzius and passing through the Douglas, not only for the

isolation of the seminal vesicles (such as from Montsouris technique), but for the

whole isolation of the prostate and the anastomosis phase. The BA-RARP

technique uses an unusual access to the prostate for the urologist. However,

despite the initial apparent complexity of the technique, it allows to obtain

excellent results both from the oncological and functional point of view (Trucco

et al. 2017).

By analysing results from the first 200 patients operated with this approach, at

Niguarda Ca’ Granda Hospital in Milan, and with one-year minimum follow-up,

it is possible to conclude that the oncological results have improved after a

learning curve of 100 patients (Galfano et al., 2013).

The great strength of the “Retzius-sparing” technique seems to be the immediate

recovery of continence. Indeed, just a week after the catheter removal more than

91% of patients reacquire continence; and these positive margins are consistent

with those described in literature and reported in series of patients treated with the

anterior technique (Galfano et al., 2013).

Thanks to robot technology it is possible to have little bleeding permitting to

avoid transfusions and to have lower hospital stay (2 ½ days on average) and thus

it allows the patient to face the surgery with more serenity.

Of course, all this is possible in the early stages of the prostate cancer disease; so,

an early diagnosis is still a fundamental condition in order to permanently solve

the oncological problem, which is the priority of course, and to recover a full and

unrestricted daily emotional, social and work life (Bocciardi, 2014).

123

In conclusion, numerous studies have been conducted in the past few years to

measure the effectiveness of Robot-Assisted Prostate Surgery, and to compare it

with the results observed from open surgery; almost all the researchers agree that

Robotic-Assisted Radical Prostatectomy (RARP) has improved outcomes in the

longer terms when compared with open surgery. Some of the afore mentioned

studies’ results are reported in the tables below.

Table 12: Benefits of robotic prostatectomy over open and laparoscopic surgery

(http://roboticprostatesurgeryindia.com/)

)

Table 13: Outcomes following robotic radical prostatectomy in the select reported studies

124

5.3 Application of the proposed Dynamic HEART Methodology

First of all, experts must be selected in the area of HRA and operators with

professional experience or knowledge in the application domain (Embrey et al.,

1984; Seaver & Stillwell, 1983). In our case, we questioned surgeons with past

experience on this kind of practice; and after having gathered the information

related to the submitted questionnaires, it was possible to fulfil the data

initialization phase of the simulating tool.

As anticipated, the tool consists in a DET branching at three different points, i.e.

the three Critical Tasks, and for each of these a set of Error Modes is defined.

At this stage, we are now able to depict the total and general scheme of the DET

we will deal with; in particular, we are able to define the following graph

highlighting the sequence of the procedure and emphasising the final Patient

Outcome grade.

125

Figure 15: Sequence of the procedure simulated by the tool

126

5.3.1 Application of HEART technique

As said many times in this work, the basic technique that we are going to adopt to

compute the probability of the different paths is HEART; the fundamental

requirements in order to apply this methodology, and its “dynamic” version,

consist in the quantification of the Proportion of Affect (PoA) and of the number

of paths to be considered.

This information was obtained by the questionnaires presented in Appendix 7

(their result, in terms of DET, is presented in Figure 15), which has been submitted

to three surgeons of Niguarda Ca’ Granda Hospital, considered fully trained in the

procedure.

In particular; referring separately to the first, and third critical task, they had to

identify which Influencing Factors are considered as major influencers of human

operations’ performance; and, in this case also including the second CT, all the

possible Error Modes stemming from each task with its relative Patient Outcome

grade (referring to Clavien-Dindo taxonomy).

For what regards the IFs to be considered in the analysis of the second critical (i.e.

Detachment of Santorini from the prostate’s frontal surface) task we referred to

the results obtained by Cordioli’s survey, plus the one deduced from literature

review. The final sets of IFs to be involved in the evaluation of the different

probabilities are:

Critical

Task 1:

- Noise and ambient talk (IF 1)

- Poor management errors (IF 5)

- Poor coordination (IF 10)

Critical

Task 2:


- Rude talk and disrespectful behaviour (IF 7)

127

Critical

Task 3:


- Poor management errors (IF 5)

- Poor communication (IF 9)

- Poor coordination (IF 10)

128

CHAPTER 6: RESULTS

This chapter illustrates the results obtained from the simulation campaign based

on the data gathered through surgeons’ interviews and questionnaires. The chapter

illustrates the analysis of such outcomes in terms of factors’ impact, probability

distributions, and system’s reliability; moreover, some improvement measures are

suggested for limiting the negative effect of different Influencing Factors on

surgeon’s performance.

Through an empirical approach, previous works investigated which Influencing

Factors (IFs) of the surgical taxonomy are met when performing the single critical

tasks of the case procedure by means of the comparison of those identified in the

operating room, during the observational phase, with the ones directly selected by

surgeons. This information has been exploited in order to initialize our DET,

whose branches have been defined on the basis of surgeons’ judgements (the

completed questionnaires are available in Appendix 7).

In the quantitative phase of the work, surgeon’s unreliability for a fixed sequence

of Critical Tasks has been estimated by applying the modified dynamic HEART

technique in the evaluation of the DET’s nodes. Specifically, the following issues

have been addressed:

- Initialization of the Assessed Proportion of Affect, which gives a measure

of each EPC/IF effect magnitude;

- Initialization of the Assessed Nominal Likelihood of Unreliability

(ANLU) for the Critical Tasks “Isolation of lateral peduncles and of

posterior prostate surface”;” Santorini detachment from the anterior

surface of the prostate”, and “Anastomosis”;

- Identification of the Error Modes (Ems) undergone for each simulation,

i.e. paths, and evaluation of the branches’ probabilities through the

adoption of a linear additive model and the modified HEART’s set of

formulas;

- Identification of the final Patient Grade Outcome, according to Clavien-

Dindo classification;

129

- Calculation of the probability distribution of each Patient Outcome Grade

for the selected procedure, holding the Central Limit Theorem.

Once obtained the probabilities for the different grades, we performed a factor and

scenario analysis to investigate the effect of the various IFs considered in the

calculation on the probability of success of the surgery, and in particular on the

health and recovery of the patient.

6.1 Numerical analysis of the simulation results

In the Study Methodology chapter, we already mentioned the fact that, while for

the first and third Critical Tasks we considered as influencing only those factors

commonly identified by all the surgeons in previous studies, no data were

available for the second Critical Task, i.e.” Santorini detachment from the anterior

surface of the prostate”.

The choice made regarding the factors acting on the reliability of this task was

based on the results obtained from different studies, showing that two of the most

frequently named influencing factors in surgical practice are “Noise and ambient

talk” (IF 1) and “Rude talk and disrespectful behaviour” (IF 7).

We recall the fact that through the simulation tool it was possible to select all the

variables, and so paths, in a completely independent and random manner for a

number of 20,000 iterations so that, holding the CLT, the resulting probabilities

have global validity.

The range of probability for the different EMs are represented in the table below;

the extremes of the various ranges were defined through the individuation of the

minimum and maximum values assigned to the EMs by experts; the same criterion

was adopted for the grades’ range definition. For the second critical task, we see

that, having a unique identified Error mode, the range describing the possible

values of alpha corresponds to a unitary conditional probability.

130

Table 14: EMs’ probability range definition

Critical

Task / α

EM CT-1 EM CT-2 EM CT-3 EM CT-4

min max min max min max min max

CT 1 0.39 0.425 0.1 0.6 0.01 0.05 0.425 0.58

CT 2 1 1 - - - - - --

CT 3 0.28 0.5 0.1 0.5 0.2 0.6 0.3 0.57

Table 15: EMs' grade range definition

Critical

Task /

Grade

EM CT-1 EM CT-2 EM CT-3 EM CT-4

min max min max min max min max

CT 1 1 1 1 2 2 3 1 2

CT 2 1 2 - - - - - --

CT 3 1 2 1 2 1 1 1 1

As already mentioned, according to the questionnaires collected and so to our

analysis, the worst possible scenario for a patient undergoing this type of surgery

(i.e. BA-RARP) is the Grade 3 outcome (i.e. “Requiring surgical, endoscopic or

radiological intervention”); anyway, this is actually not true in terms of real

practice.

For example, the possible interference with the iliac artery can lead to much more

serious outcomes, and can also provoke the death of the patient; in any case, this

is quite a remote possibility since only the 0.2%, approximatively, of the patients

dies due to surgery complications.

Even though, the scope of our investigation was to model a tool able to replicate

the behaviour of a surgeon in the operating room, being a first attempt, it has been

131

meaningful to take into account only those complications considered as the most

frequent in common practice, so not to unreasonably complicate the tool

validation process. In Table 16 we can see all the results obtained for the

evaluation of the quantiles (q=0.95) of the optimum outcome, i.e. no deviation

from standard procedure (Grade 0), ant the maximum expected degradation of

patient outcome one (Grade 3). The simulation was run 12 times in order to cover

the following cases: no IF considered, all IF considered, only one IF considered

per simulation run (i.e. IF-i), and all except one IF (i.e. NO IF-i) considered per

simulation run.

CO

MP

LE

TE

93

.47 %

3.1

7 %

CO

M

PL

ET

E

93

.47 %

3.1

7 %

IF 1

0

99.0

3 %

0.1

0 %

NO

IF

10

94

.58%

0,6

%

IF 9

99.3

9 %

0.0

042 %

NO

IF

9

94.6

3 %

3.0

3 %

IF 7

99.4

8 %

0,0

031 %

NO

IF

7

95.9

7 %

2.9

8 %

IF 5

98.7

2 %

0.1

3 %

NO

IF

5

94.5

0 %

0.5

3 %

IF 1

96

,46

%

0.1

5 %

NO

IF

1

97

.86

%

0.6

%

NO

IF

99

.71

%

0.0

03

%

NO

IF

99

.71

%

0.0

03

%

PA

TIE

NT

OU

TC

OM

E

Gra

de

0

Gra

de

3

PA

TIE

NT

OU

TC

OM

E

Gra

de

0

Gra

de

3

Tab

le 1

6:

Pro

bab

ilit

y o

f h

avin

g t

he

95

% o

f p

ati

en

ts r

esp

ect

ivel

y w

ith

th

e m

inim

um

an

d m

ax

imu

m g

rad

e p

oss

ible

132

As already mentioned, the first and the seventh factors were arbitrarily attributed

to the second Critical Task respectively for the following reasons:

- As result of a national scale survey, IF 7 resulted to be one of the most

relevant factors acting in Surgery having recorded a mode value of 8, on a

scale from 0 to 10 assessing the IF’s maximum potential bad impact on

surgery operations’ performance, which actually is the highest mode value

observed;

- IF 1 has been selected by all the three surgeons as important influencer of

both the other two tasks and its mode value for maximum potential bad

impact on surgery operations has been evaluated as 4, which is actually

the second highest mode registered.

Anyway, since our decision was based on personal speculations, we recommend

the interested reader, and future researchers, not to keep the results presented in

these pages literally and to consider the numbers as the outcome of a mere, even

if reasonable, exercise more related to the validation of the proper behaviour of

the simulation tool than on the precision of the numbers themselves.

In any case, as shown in Table 16: Probability of having the 95% of patients

respectively with the minimum and maximum grade possible and in the following

histograms, an analysis of IFs’ impact was performed: firstly considering, and

then removing, a single IF per simulation, in order to better appreciate their impact

on the resulting system’s behaviour.

Focusing now on the upper part of the chart, we can refer to the graphs below to

better appreciate the relative impact of IFs on the achievement of the “extreme”

outcomes. In the first one, showing the probability of a Grade 0 outcome for the

0.95 percentile of patients, we see the progressive decrease in probability of Grade

0 from left to right; and in particular, we can say that the more impacting factor

on this Key Performance Index is, by far, IF 1; followed by IF 5 tied to IF 10, 7,

and 9.

133

Figure 16:The probability of a Grade 0 outcome for the 0.95 percentile of patients

Trying to make some reasoning on this result, we can say that we should have

expected IF 1 to be the factor more heavily impacting on surgeon’s performance

in terms of Grade 0 quantile (-3.54%), since it has been considered to describe all

the three Critical Task under exam.

Even though it is well known that background noise is a very relevant disturbing

factor, the effect produced from IF 1 on Grade 0 is also stressed by the way in

which the software evaluates the final grade of the procedure; in fact, in order to

get the no deviation case, we need to undergo a no deviation case for all the tasks

involved, otherwise, the highest grade encountered will be selected as the resulting

one.

The same considerations can be done, on a different scale since they are taken into

account just in CT 1 and 3, for IFs 5 and 10, which share the same order value

(around 99.0%); and to IF 7 and IF 9, considered only in one of the three tasks

(aroung 99.4%).

The small differences in terms of affected percentage can have several reasons.

For what specifically regards the evaluation of Grade 0 probability we can say

that, aside the approximations introduced setting a finite number or runs (20,000),

the discrepancies between the different factors showing up an equal number of

99,71 99,48 99,3999,03 98,72

96,46

93,47

90

91

92

93

94

95

96

97

98

99

100

101

No IF IF 7 IF 9 IF 10 IF 5 IF1 Complete

Grade 0 (q=0.95)

134

time in the evaluation, can be imputed to the differences in terms of mode values

and EMs probabilities introduced for the relative critical tasks.

Analysing now the scond hystogram, descirbing the probability of a Grade 3

outcome for the 0.95 percentile of patients, the a priori consideration we can make

is that the only task presenting the possibility of ending with this severity level is

task 1, hence only those factors affecting the first CT (IF 1, IF 5, and IF 10) are

supposed to have an impact on this KPI.


This is well illustrated by the picture above from which we can appreciate the

fact that considering only those factors not involved in CT1 evaluation (IF 7 and

9) we end up with a probability around 0.001% for Grade 3 (1.e. same result

obtained from the NO IF case), while we have very similar results for IF 1, 5 and

10, all involved in CT1 evaluation.

Aside of the comments made before for justifying the discrepancies between

results that would be supposed to be equals for the quantiles of Grade 0 (also

visible in the chart according to the colours associated to the different cells), for

what regards Grade 3 evaluation we must also remember the fact that the alphas

associated to the only Error Modes leading to Grade 3, i.e. ME 1-3, range from 1-

5%, resulting in a very small sample, and consequently not precise figures.

In order to provide clearer and sounder figures, we also decided to evaluate the

probability of a Grade 3 outcome for the 0.05 percentile of the patient. This

0,003 0,0031 0,0042 0,10 0,13 0,15

3,17

0

0,5

1

1,5

2

2,5

3

3,5

No IF IF 7 IF 9 IF 10 IF 5 IF 1 Complete

Grade 3 (q=0.95)

135

actually corresponds to the complementary result with respect to the 0.95

percentile calculated before; and in this way, we will appreciate the probability of

the Grade 3 being the outcome attributed to the 5% of the patients

The results obtained from its calculation are shown in Figure 18.


We can see that the same considerations done for the 0.95 percentile case are still

valid; but, in this case the impact of IF 1 is almost more than the double of the

ones of IF 5 and IF 10, fact probably to be imputed to the difference in their

maximum impact mode value: 4 out of 10 for IF1 and 0 out of 10 for IF 5 and IF

10.

Anyway, it is worth noting that having a Grade 3 outcome for the 5% of the

patients undergoing a BA-RARP is around the 0.03%; and that it is two orders of

magnitude less with respect to the one evaluated with a 95% confidence level.

To be complete, we implemented another a complementary analysis: starting from

the simulation of the complete scenario and removing one IF for each simulation

(results shown in Table 14).

Also in this case the coherency of the result is demonstrated since for those factors

not influencing CT1 (IF 7 and 9) we see that the results for Grade 3 quantile

remain unchanged with respect to the full-set case.

0,003 0,00310,0042

0,00560,00730

0,0196

0,0324

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

NO IF IF 7 IF 9 IF 10 IF 5 IF1 Complete

Grade 3 (q=0.05)

136

At the same time, comparable results are obtained for Grade 3 quantiles

respectively removing IF 1, 5, and 10 from the simulation; in fact, aside for their

PoA mode values, they affect the reliability of task 1 in the same way.

According to the considerations made before and regarding the number of times

a certain factor shows up in the model, we are not surprised that Grade 0 quantiles

for the different trials, exception made for the “without IF 1” case, are very

similar; while for this last case we see that if not considering it the probability of

no deviation sensibly increases.

Even though, IF 7 has been identified as a very relevant factor in surgery practice,

this does not emerge from our results (aside the fact that in the “without IF 7” case

we have the second highest quantile) and it was not identified from surgeons as a

major factor in the analysis of the three critical tasks of the BA-RARP; so it would

be no doubt interesting to verify its impact for those Healthcare applications where

it is kept in high consideration.

Before showing the plots resulting from the software runs, we want to make some

reasoning about the relative importance in BA-RARP of different categories of

Influencing Factors, specifically: Team, Organizational, and Personal factors. The

various IFs were grouped as follows:

INFLUENCING FACTORS CATEGORY

1 Noise and ambient talk Team

5 Poor management of errors Organizational

7 Rude talk and disrespectful behaviour Team

9 Unclear communication Team/Personal

10 Poor coordination Team/Personal

137

The relative results in terms of quantiles are shown in Table 17: Analysis of IF

clusters' impact; from which we can appreciate that for sure the most influencing

category on the outcome of the surgery is the one related to Team and Teamwork

conditions; secondly the Organizational one; and finally, the one concerning

Personal factors. Indeed, from left to right we see the probability of Grade 0

increasing and the one of Grade 3 decreasing at the same time.

As we expected, and as shown in Table 17, the simulation run with the full set of

IF has the minimum probability for Grade 0 and the maximum value for Grade 3.

Another interesting point is made by the fact that the “Complete” scenario is much

more similar to the “Team” one than to the “Organizational” and “Personal” ones;

which means that the first category is the one better describing and mainly

affecting the outcome of the realistic case.

Table 17: Analysis of IF clusters' impact: probability of Grade 0 for the 0.95 percentile of patients

and of Grade 3 for the 0.05 percentile of patients

PATIENT

OUTCOME

(UPDATED

MULTIPLIER

S)

COMPLETE TEAM

(IF 1, 7, 9, 10)

ORGANISATI

ONAL

(IF 5)

PERSONAL

(IF 9, 10)

GRADE 0 93.47 % 94.58 % 98.72% 98.38 %

GRADE 3 0.0324 % 0. 0221 % 0.0196 % 0. 0109%

In the following, a short qualitative description of the three scenarios, and of the

key features obtained from direct observation in the operating room, is provided.

Team Category

The first category is the one concerning the influence of Team related

factors. Coordination and communication can either occur explicitly or implicitly.

Team members can intentionally communicate or they can anticipate, assist and

adjust without verbal instructions, relying on shared understanding of tasks and

138

situations; they are continuously involved in reciprocal process of sending/

receiving information that forms and re-forms a team’s attitudes, behaviours, and

cognitions.

In this kind of scenario, it is recommended to train team member to develop open,

adaptable, accurate and concise communication.

Moreover, the implementation of inter-professional education should contribute

to provide guidance on how to implement information and exchange protocols,

indeed the identification of a team based approach for improving quality care

requires the implementation of inter-professional education, training sessions and

meeting involving the whole operating team to instil advanced knowledge, skills,

and attitudes required for optimal teamwork. (Trucco et al. 2017).

Another fundamental element is team-stabilization: it is important to keep the

surgical team, as much as possible, unchanged for similar surgeries that required

analogous knowledge and skills.

Familiarization between team members is a crucial factor, which contribute to

improve communication and coordination. Researches have shown that the longer

a team is together, the better its results, also in terms of good communication, are

(Lingard et al., 2004). For these reasons, the scheduling of work shifts should take

into account these issues and, above all, avoiding change of shift during the

execution of a surgical procedure, in order to keep silence and concentration.

Finally, to avoid problems related to communication and coordination, it is also

recommended to only use equipment or personnel that are strictly required.

Organisational Category

Despite this is a not in deep explored topic, for Healthcare applications, it

is demonstrated that organisational issues have a very powerful impact on

Healthcare operators and procedures’ performances; as shown by the results

obtained in (Trucco et al. 2017), were IF 5 was proven to be the most impacting

factor on ANLU when the single tasks were considered.

139

Nowadays, the importance of procedures to be clear, complete, understandable,

updated, well known and followed, as much as having emergency procedures

identified for possible scenarios of deviation, has been ascertained.

One of the factors that can affect good error management is surgeon’s experience.

It is fundamental for the surgeon to be always aware of the situation and of the

possible, and/or probable, relative consequences.

What is even more remarkable, from HRA perspective, is that for the

maximisation of error management, and so of surgery performance, it is important

to recognize the value and potentiality of clinical documentation for clinical risks

prevention and the analysis of the events related to it; to this end, the use of

checklists to count instruments used during surgery (threads, needle, etc.) and

verify their final number is also recommended.

These kinds of practices are gaining a more and more central role in the medical

sector, as their benefits are becoming undeniable, and we hope that this trend will

encourage and foster the spreading of Human Risk Assessment and Safety culture.

Personal Category

The third, and last, category considered is related to personal factors that

come into play during the execution of a surgery.

Unclear communication and poor coordination can be associate to the personal

aspect because they can be related to the individual temperament of the surgeon,

although there are clear links with team aspects too; indeed, IF 9 and IF 10 may

be associated to both categories and benefit from common improvement actions.

Lingard and his colleagues have found that 31% of all communications could be

categorised as unsuccessful (i.e. failures), whether the information was missing,

the timing was poor, or where issues were not resolved (Lingard et al, 2004).

With the aim of improving this aspect of operating theatre’s practice, it would be

for sure advisable to increase the number and quality of training sessions in order

to make surgeons, and medical staff in general, more comfortable with stressful

situations; and to promote the standardization of technical communications during

the procedures, so to avoid misunderstandings leading to potential failures.

140

A standardization through codes of communication would be even more valuable

in a Robotic surgery context since it would bypass the lack of visual feedback

arising from the implementation of the robot.

6.2 Probability Density Functions of Patient Grade Outcomes

Except for the Factor Analysis illustrated in the previous paragraph, the final

outcome of the simulation tool was constituted by the PDF plots obtained for each

of the scenarios listed above.

In particular, the plots of the Grades’ PDFs, together with the relative best fitting

Gaussian, resulting from the full set of IFs are shown in the following figures

(from Figure 19 to 5).

We see that the Grade 0, i.e. the No-deviation case, is the more probable grade for

all the computations and that the probability of the grades (µ and frequency),

decreases for increasing severity grades; which is a good point medically speaking

since it means that the worst-case surgery outcome is also the less probable.

Except for the fact that the plot of Grade 3 is not available for IF 7 and 9 due to

the lack of available data (for the reasons already explained in the previous

paragraph), for all the grades, and all single factor analysis, the plots result to have

a good, or at least acceptable, fit with the associated Gaussian; even though the

data relative to Grade 3 are always quite confused and sparse due to the paucity

of samples collected. For what regards Grade 0, all of the factors’ PDF result to

be squeezed in the region between [0.8;1].

Through the running of the simulation of the procedure’s simplified version, we

have been able to validate the correct behaviour of the simulation tool designed

for this study; but, more challenging and interesting results and comments could

be done by applying this software to a refined and more complete schematization

of the surgery procedure itself, including the recovery paths suggested in this

work.

141

Figure 19: Grades’ PDF for the complete set of simulation runs

Figure 20: Grades’ PDF for the "only IF 1" set simulation run

142



143



144

Figure 25: Grades’ PDF for the "NO IF " set simulation run

145

CHAPTER 7: CONCLUSIONS

This study allowed the development, testing and validation of a simulation tool

based on Dynamic Event Tree theory and structure adopting a modified HEART

methodology for application in the Healthcare sector.

The attention was directed to the analysis of surgeon’s unreliability in robotic

surgery, since it is an innovative sector where Minimally Invasive Surgery enables

optimizing precision, speeding up recovery, and potentially reducing human

errors.

The importance of robotic surgery and the clear investment in future

developments highlight the need to carry out studies and important researches in

the field of Human Reliability Analysis. Since, for now and the near future, the

robot does not replace the surgeon, but only supports him in close cooperation and

interaction, the analysis and management of human error, and the application of

HRA techniques, are fundamental and necessary.

The state of art review underscored firstly the important results obtained by HRA

techniques in the few surgery applications developed; and secondly, the need to

reduce the gap of applicability between the Industrial and Healthcare sectors.

Even though, the first baby steps have been done in this sense, the majority of the

efforts in the socio-technical complex system of healthcare organizations is

characterized by reactive approaches, strongly focused on the retrospective

analysis of adverse events, such as incident data analysis; while, it would be for

sure more interesting to develop that branch of HRA discipline concerning

anticipatory analyses, which would represent a new twist in Healthcare helping in

the prediction, and hopefully elimination, of system’s vulnerabilities without the

necessity of occurrence of the failures itself.

The first aim of this work was to develop a first prototype of a DET simulation

tool able to differentiate the various paths deriving from one, or more, failures

along a surgical procedure.

The introduction of a DET structure allows the inclusion of a procedural timeline,

still not considering the influence of the passage of time; while the update of the

146

multipliers used in Healthcare specifically designed HEART methodology,

defined a step forward in terms of database, and so results’ accuracy.

There is still much work to do in order to get specific and wide ranging database

directly produced by experts and experiences coming from the Healthcare sector;

nevertheless, through specific assumptions we manage to benefit from the

developments gained by more advanced, for what regards safety studies, contexts.

The methodological steps developed to achieve the objectives were:

- Literature analysis of dynamic HRA techniques and their applications in

the industrial and Healthcare sector;

- Empirical observational activity of two robotic surgeries;

- Modelling of a simulation tool basing on the modified version of HEART

in Surgery and DET principles: identification of the branches and of the

factors to be considered in the analysis;

- Identification of the most significant recovery paths and relative

probabilities through the collection of experts’ data (i.e. three robotic

surgeons);

- Initialization of the simulation tool and application of the model to a

specific case study;

- Influencing Factors Analysis; for those considered as the more impacting

on surgeon’s performances.

The observational activities and collaboration with team of robotic surgeons

allowed to:

- Obtain a validation for the recovery paths’ task analysis of BA-RARP;

- Define the relative probability for the different Error Modes;

- Get surgeons’ opinions on the impact of Influencing Factors on two of the

three different Tasks of the selected surgical procedure.

Finally, an Influencing Factor Analysis was carried out to validate the model and

understand how the various factors affect Human Unreliability rate with a special

focus on the variability of the extreme grades’ outcomes (i.e. Grade 0 - No

deviation/ and Grade 3 – Deviations requiring surgical, endoscopic, or

radiological intervention).

147

The quantitative analysis, brought up by means of the simulation tool, confirmed

the quantitative relevance of certain factors (such as IF 1 and in general Team

Factors) over others, so identifying those requiring special care and, eventually,

remedial measures aimed to limit their negative influence on the execution of the

procedure’s sequence.

The quantitative analysis was limited by the small scale of the survey affecting

the objectivity of the data used; anyway, it has been possible to show that the

model developed produces reliable and coherent results.

7.1 Theoretical implications and future research

Assessor Team’s inexperience in HRA techniques has been confined by the use

of the Influencing Factors taxonomy validated for Surgery and by the adoption of

minimal and simplified questionnaires. Therefore, the surgeons involved in the

work faced a familiar and understandable list of factors and were allowed to

describe the recovery tasks freely.

Despite this, there have been some misunderstandings and difficulties in assigning

the required values, probably due to the fact that they had not much experience in

HRA and Statistics. This is one of the limit of this kind of technique, since it aims

at involving and directly interacting with professionals with no or limited specific

knowledge on HRA methodology and safety techniques.

The questionnaires were submitted to three surgeons of the Ca’ Granda Niguarda

Hospital of Milan and they were asked to identify and give estimates of the relative

probability of the most significant Error Modes that could stem from the three

Critical Tasks of the procedure already identified in previous studies.

The surgeons were also questioned about the different recovery steps to be

followed in order to cope with the failures described, and an interesting fact was

the almost complete overlap of the answers gathered; this is for sure a relevant

indication, which will facilitate future studies’ attempts in defining more complete

DET including the probabilities of the various recovery tasks.

148

Moreover, the fact that we got reasonably homogeneous results about the tree

representation of surgeons’ procedure (i.e. Error Modes, recovery steps, and

probabilities), demonstrates the validity of the approach. Indeed, even though

there are not well defined procedures and recovery paths in literature for BA-

RARP, from our study we can appreciate that surgeons with the same level of

experience are on the same page, especially for what regards the branching of the

procedure.

Looking at the fuller picture, it was observed that if new tasks and procedures

would be analysed, it will be necessary to acquire new and specific values for all

those variables included in the evaluation of probabilities according to HEART

technique. In fact, the judgements included in the study are not only subjective,

according to the professional opinion of the surgeons interviewed, but also strictly

contingent to the selected phases of the specific procedure. indeed, each operation

is characterized by peculiarities and uniqueness that influence the choice of the

most significant IFs, their quantitative impacts, and also their correspondence with

Williams’s EPCs.

As a step forward from previous analysis, we focused on the full procedure

success probability, leaving behind the single-tasks performance analysis

approach. Anyway, the assumption regarding the GTT allocation to the different

tasks was preserved by assigning the same Generic Task -G- to all the three CTs,

which implies same Nominal Human Unreliability (NHU) range, even if they

actually have different technical complexity. This was made for seek of continuity

and simplicity, but it would be a good point to update the GTT description and

evaluation according to the developments introduced by NARA and CARA

approaches also for surgical applications.

For developing and improving this study, it is important that other procedures and

surgical settings could experience this modified methodology and proactive

simulation approach, enhancing its diffusion, so that this work does not remain a

mere exercise of study.

On the other hand, it is necessary to take into account that the applicability in

complex areas needs a long time and readjustments.

149

This works represents a first step for the inclusion of dynamics in HRA techniques

for surgery applications; as suggested before in this discussion, future

developments should explore:

• The description of the evolution over time of the Influencing Factors

involved;

• The dependences existing between the tasks composing the sequence of

the procedures and the IF/ECP themselves;

• The investigation of the cognitive models underlying surgeons’ behaviour

in order to develop high-performance simulating tools;

• The investigation of the recovery paths and of factors specifically designed

for recovery scenarios peculiarity.

To recall what has been said after the state of art overview, we will briefly resume

the starting points for future researches and the main goals to be pursued.

The results obtained from the work of (Ambroggi & Trucco 2011) showed the

relevance of considering both direct and indirect influence of the factors involved

in any HRA analysis and the predominance of the acquired component in

modifying the weights of the PSFs; thus not considering the latter leads to a biased

estimation (De Ambroggi 2010). It would be interesting to review the

interrelations between the various factors involved; to get a fuller picture and

allowing experts to modify their assessment for the same task in different scenario.

In this way, the set of factors involved will influence the properties of the set itself

having the possibility of changing its components’ impact, so being more realistic.

The first step for the introduction of IFs’ dynamics is the integration of factors’

latency and momentum, and a suggestion for the ways in which the factors could

be able to evolve along the procedure is the one proposed by Boring’s study

treating parameters’ dynamics (Boring 2006), differentiating their behaviour

between Static Condition; Dynamic Progression; and Dynamic Initiator.

Many studies have demonstrated the importance of considering the effect of task

dependencies on probability evaluation to correctly estimate them. To show the

validity of this literature’s statement, also on the specific case under analysis, the

150

suggestion is to perform a dependency analysis, at first, on the most critical tasks

of the BA-RARP, and lately on the full procedure.

For what regards the formulation of a dedicated set of factors and multipliers

referred to the recovery paths evolution, we had an example of that in (Subotic et

al. 2007) where a set of Recovery Influencing Factors (RIFs) were defined for

ATC applications and the importance of the logical differences deriving from the

fact that we are dealing with a situation where a failure has already occurred were

underlined.

For the transposition of this study in Surgery, it would be firstly necessary to

modify the taxonomy in use so that the differences in scenario could be better

appreciated, the same for what regards multipliers’ evaluation. This, together with

the development of a proper database would foster the optimization and reliability

of future simulation tools.

In conclusion, a better modelling of all aspects mentioned before would constitute

a valuable consolidation of our study; and in this way, quantitative considerations

of goodness for recovery strategies could be formulated to refine educational tools

and packages; so, the whole Hospital system would benefit from this line of

research.

7.2 Implications and relevance for practitioners

The prostate cancer is the top of the list in terms of male population’s incidence

and its numbers are relentlessly growing. For sure, with a view of prevention, the

first step consists in fostering PSA exam which, monitoring the medical situation

over time, can point out the necessity to undergo more detailed exams, so

permitting the detection of the cancer during its early stages and incrementing the

chance of cure without invoking surgery.

The introduction of MIS has marked the beginning of a proper revolution in the

Surgical sector.

The shorter stay allows to have the patient weight-bearing in two days, but the

gold standard of this technology is the maintenance of the functions; so, BA-

151

RARP, aside the removal of prostate cancer, is an efficient and effective solution

to post-operative incontinence and impotence.

We hope that this work will support future training of robotic surgeons and the

design of new procedures and checklists; but most of all that the immediacy of

use of simulation tools will foster the evolution of operating room’s environment

and organization.

What is appealing of the kind of technology we issued is the possibility of

manipulating those factors actively, or passively, influencing human behaviour

and putting them in relation with the probability of success of the surgery and its

probable outcomes.

As mentioned many times along the study, one of the most hampering factor in

the development of HRA techniques for Healthcare is the lack of reliable data;

but, we expect that the continuous theoretical development, and the increasing

ease of use and effectiveness of this kind of tools will get the attention of surgical,

and in general medical, world.

The study highlights the major factors, or class of factors, influencing surgeons’

performance. Therefore, it is important to take that information into account and

to try to reduce their effect by raising surgeons’ awareness about errors promoting

conditions and implementing improvement actions, such as those proposed in the

study.

Additionally, the work represents a useful contribution to technology providers,

paving the way to the introduction of dependencies and recovery paths’ evaluation

for HRA applications in surgery.

Thanks to the tool developed and tested in the present work, performing a reliable

and efficient simulation is more than ever affordable, and the refinement and

enlargement of the data involved would provide even more precise and effective

analyses, facilitating the optimization and improvement of the operating room

environment.

What is more fascinating of this kind of technique is its flexibility of application

to the most disparate fields of interest, and its adaptation from NPP to Surgery

environment is the prove that nowadays Safety Engineering is a transversally

152

valuable discipline for maximizing systems’ performances; which in the end

results in an improvement of work’s quality both from the point of view of the

worker/surgeon and of the client/patient

For what specifically regards Robotic Surgery, it has not yet expressed its full

potential, and we expect future studies to introduce all those elements and

strategies already experimented in the industrial sectors (e.g. NPP, ATC)

producing a more comprehensive description of the phenomena occurring along

the procedure and a more accurate analysis of probabilities; with the hope of

seeing the spreading of the use of these methodologies and the increase in risk

awareness among potential users.

153

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incontinenza-eimpotenza.html

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Abmedica website: http://www.abmedica.it/it/prodotti/da-vinci

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APPENDIX 1: Tools used for RARP

procedure

Tools used for RARP- Procedure at Ca’Granda Niguarda Hospital (Milano)

Bisturi lama 11

Kocker curvo per divaricare sottocute

Bakhaus sulla fascia e tensione verso l’alto

2 Farabeuf piccoli

2 pinze (anatomiche, chirurgiche o Durante)

3 trocar robotici

1 ottica robotica 30°

1 trocar Airseal

1 trocar 5 mm

1 trocar 12 mm

1 Johanne laparo

1 forbici laparo

1 clip Bbraun DS M

1 clip Bbraun DS SM

1 clip Bbraun DS S

1 clip Aesculap solo piccolo

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1 clip metalliche da 10 mm

1 Ago di Verres

2 Ethilon 2-0 con aghi retti

1 Vicryl rapid 3-0 ago HR 22 non tagliente

2 V-Lok (15 cm e 23 cm con ago a semicerchio non tagliente)

1 seta 1 ago tagliente per fissare il drenaggio

1 Vicryl 0 ago 5/8 non tagliente per fascia

2 Vicryl rapide 2-0 con ago tagliente da cute

1 aspiratore Elefant 45 cm

1 Forbice monopolare curva robotica

1 Cadiere robotica

1 Maryland robotica

1 portaaghi robotico

1 catetere Dufour 18 Ch Simplastic

1 set per cistostomia a palloncino Foley 14 Ch

1 drenaggio tubulare

160

APPENDIX 2: Validated Task Analysis of

BA-RARP procedure

Tecnica Prostatectomia Radicale Robotica RETZIUS SPARING

https://www.youtube.com/watch?t=13&v=DS7ddQltHRY (Retzius-sparing Approach for Robot-assisted Laparoscopic Radical Prostatectomy) (Dicembre 2013) TASK ANALYSIS

1) POSIZIONAMENTO DELLE PORTE

Per questo intervento vengono usati : - 4 bracci robotici - 2 trocars per gli assistenti

posizionati in modo standardizzato Il GRASP viene posizionato nel secondo braccio robotico mentre la bipolare nel terzo, differentemente da quanto avviene nell’approccio anteriore.

2) INCISIONE DEL PERITONEO ED ISOLAMENTO DELLE VESCICOLE SEMINALI L’operazione inizia con un’incisione di 5-7 cm nello spazio del Douglas in modo da isolare le vescicole seminali.

La prima struttura che si incontra è il deferente dentro: - Il deferente destro viene isolato e sezionato.

https://www.youtube.com/watch?t=13&v=DS7ddQltHRY

161

e vescicole seminali destre vengono isolate grazie all’utilizzo di clips (grandi circa 3 mm). La stessa manovra viene fatta a sinistra:

- Il deferente sinistro viene isolato e sezionato - Le vescicole seminali sinistre vengono isolate usando delle clips

1) SOSPENSIONE DEL PERITONEO Al fine di allargare lo spazio di lavoro a disposizione, vengono inseriti dall’assistente due punti

con aghi retti, tangenziali all’area prepubica in maniera transaddominale. Questi passano

attraverso il peritoneo (vicino alla vescica). Quelle che si vengono a creare sono come due

‘tendine’, una a destra e una a sinistra. - Le vescicole seminali e il deferente vengono sospesi alle due ‘tendine’

2) ISOLAMENTO DELLA SUPERFICIE POSTERIORE DELLA PROSTATA E DEI PEDUNCOLI LATERALI

- Viene aperto un piano intra-extrafasciale a seconda del livello oncologico del tumore

- Isolamento grazie all’utilizzo di clips delle vescicole seminali - Isolamento peduncolo destro, con l’utilizzo di clips - Sezione del peduncolo destro limitando l’utilizzo di energia - Isolamento peduncolo sinistro, con l’utilizzo di clips - Sezione del peduncolo sinistro limitando l’utilizzo di energia

In questo modo si ottiene lo spazio laterale della prostata.

3) ISOLAMENTO DEL COLLO VESCICALE - Le vescicole seminali vengono trazionate verso il basso con il Grasp, in modo da

avere una migliore esposizione del collo vescicale - La giunzione vescico-prostatica viene raggiunta

Sia dal lato destro che da quello sinistro del collo vescicale si osserva che la vescica è situata sopra e la prostata sotto, differentemente dalla tecnica standard

- La giunzione vescico-prostatica viene sezionata e il collo vescicale viene risparmiato (se oncologicamente fattibile)

- Le fibre muscolari possono essere coagulate seguendo il piano che separa la vescica dalla prostata

- Passaggio della Maryland dietro il collo vescicale, in modo che abbracci il catetere

- Con le forbici monopolari viene incisa la parte posteriore del collo vescicale - Il catetere appare - Vengono posizionati due punti di cardinali alle ore 6 e 12, per facilitare

l’identificazione del collo vescicale nella fase di anastomosi ed evitare la retrazione della mucosa del collo

- Pinzare il repere delle ore 6 (Primo punto) - Il catetere viene tirato verso il basso - Con la Maryland mollare il punto di ore 6 - Secondo punto a ore 12 sul collo vescicale, pinzare il repere delle ore 12 - Trazionare verso l’alto - Completamento dell’incisione del collo vescicale: viene incisa la parte anteriore

4) ISOLAMENTEO DELA SUPERFICIE ANTERIORE DELLA PROSTATA E DELL’APICE

PROSTATICO - La parte anteriore e quella laterale della prostata vengono isolate per via

smussa - Evitare di entrare nel plesso del Santorini: senza sezionare, legare o aprire i vasi

del complesso venoso del Santorini. - Dissezione delle fasce laterali, quando possibile

162

- La dissezione continua verso l’apice prostatico La differenza tra il metodo tradizionale è che in questo caso la vescica è posizionata al di sopra e non posteriormente alla prostata

- L’apice così è isolato e anche l’uretra viene identificata: è possibile vedere chiaramente le sue fibre longitudinali

- Sezione dell’uretra - Appare il catetere - La dissezione dell’apice prostatico viene completata con l’incisione della parte

posteriore dell’uretra 5) POSIZIONAMENTO DELLA PROSTATA IN ENDOBAG

- La prostata è completamente isolata - La prostata viene posizionata in una sacca: Endobag - Rimozione della prostata - Lavaggio della loggia - La loggia lasciata dalla prostata viene controllata da eventuali sanguinamenti con

l’utilizzo di clips - Si estraggono gli strumenti per pulirli

6) ANASTOMOSI - Quando è necessario possono essere utilizzate sostanze emostatiche - Pulizia con una garza

L’anastomosi viene fatta prima nel lato sinistro e poi in quello destro. L’anastomosi viene eseguita secondo una tecnica di Van Velthoven modificata. Utilizziamo due fili di sutura V-Loc partendo dal quarto anteriore sinistro del margine uretrale, quindi al quarto destro anteriore e posteriore, infine al quarto posteriore sinistro. Al termine dell’anastomosi viene eseguita una prova di tenuta.

- Lato sinistro (quarto anteriore sinistro): passaggi fuori-dentro vescica, dentro-fuori uretra

- Il primo punto tira verso il basso il collo vescicale, dopo 3-4 passaggi (dipende dallo spessore del collo vescicale) si passa al lato destro

- Lato destro (quarto anteriore destro): 5-7 passaggi fuori-dentro vescica, dentro- fuori uretra

Il piano anteriore è completato, si passa ad eseguire il piano posteriore. - Quarto posteriore destro: fuori-dentro vescica, dentro-fuori uretra - Il filo di sinistra viene utilizzato ancora per 2-3 passaggi per completare - I fili vengono tesi - Il catetere viene fatto passare - I fili vengono tagliati - I fili delle due ‘tendine’ vengono tagliati - La vescica viene riempita con soluzione fisiologica, in modo da testare

l’anastomosi - Se non ci sono controindicazione e l’anastomosi è a tenuta, posizioniamo una

cistostomia sovrapubica e rimuoviamo il catetere uretrale 7) DRENAGGIO 8) ALLONTANAMENTO DEL ROBOT 9) RIMOZIONE DELLA PROSTATA E DELLE PORTE

163

APPENDIX 3: Validated Task Analysis-

Parallelism between tasks performed at

console and those at the table

168

APPENDIX 4: Contributing factor

classifications in the human factors

classification framework for patient safety

(Mitchell et al. 2016)

172

APPENDIX 5: Simulation Tool’s Script

(Matlab®)

%% DEFINIZIONE INPUT COSTANTI e INIZIAIZZAZIONE

GT=8;

Bound_GT=[0.35 0.97; 0.14 0.42;0.12 0.28; 0.06 0.13;0.007

0.045;0.0008 0.007;8e-5 0.009;0.000006 0.0009];

chir=3;

CT=3;

trial=20000;%%

ME=[4 1 4];%% %% dim

[Crtask]

grade=5;

IF=20;

multiplier=[10 10 9.8 1.048 9.775 2 5 5.7606 6.8 4.525 1.95 20 8

1.31 11 1.6 5 6.4 6.08 4.04]; %%new %%dim [IF]

%multiplier=[10 10 9.8 1.048 9.725 1.4 3.3 3.101 6.8 4.525 1.95

17 3 1.31 11 1.6 3 6.3 1.285 1.45]'; %%old %%dim [IF]

NHU_int=zeros(GT,10);%%

min_tr=zeros(20,1); %%dim [IF]

moda=[4 0 0 0 0 0 8 0 0 0 0 0 0 0 2 8 0 0 0 0]; %%dim [IF]

max_tr=ones(20,1)*10; %%dim [IF]

Prob_grade=zeros(grade,1);

%% Range alfas for the relative ME

alpha_crtask1=[0.39 0.425;0.1 0.60;0.01 0.05; 0.425 0.58];%%

alpha_crtask2=[1 1];%%

alpha_crtask3=[0.28 0.5;0.1 0.5;0.2 0.6;0.3 0.57];%%

alpha=[alpha_crtask1;alpha_crtask2;alpha_crtask3];

Prob_fail=zeros(CT,trial);

grade_chir=[1 1;1 2;2 3; 1 2;1 2;1 2;1 2;1 1;1 1];

Prob_ME=ones(sum(ME)+3,trial);

Prob_grade=zeros(grade,trial);

Prob_ME_mean=zeros(CT+3,max(ME));

grade_jud=zeros(CT,trial);

Clavien_D=zeros(trial,1);

%% CALCOLO PROBABILITA' PATHs

for t=1:trial

crtask=1;

f_vect1=[1 5 10]; %collego gli IF alle CT

p=zeros(IF,1);

for f=1:IF;

173

p(f)=(trirnd(min_tr(f),moda(f),max_tr(f)))./10;%%

end

p1=zeros(IF,1);

for n=f_vect1

p1(n)=p(n);

end

g=7;

NHU_int = Bound_GT(g,1) + (Bound_GT(g,2)-Bound_GT(g,1)).*rand(1);

prod_vect=[];

if sum(p1)==0 prod_vect=1; else for f=1:IF

prod_vect(f)=((multiplier(f)-1)*p1(f))+1;

end

end

Prob_fail(crtask,t)=NHU_int*prod(prod_vect);

Prob_succ(crtask,t)=1-Prob_fail(crtask,t);

a=zeros(ME(crtask),1);

% Creo gli alfa randomici per i vari ME

while ( a(end)<alpha(sum(ME(1:crtask)),1) ) || (

a(end)>alpha(sum(ME(1:crtask)),2) )

for i=1:(length(a)-1)

a(i)=alpha(sum(ME(1:crtask-1))+i,1) +

(alpha(sum(ME(1:crtask-1))+i,2)-alpha(sum(ME(1:crtask-

1))+i,1)).*rand(1);

end

a(end)=1-sum(a(1:(end-1)));

end

scelta=rand(1);

if (scelta<=Prob_fail(crtask,t)*a(1))

Prob_ME(sum(ME(1:crtask-

1))+1,t)=Prob_fail(crtask,t).*a(1);

grade_jud(crtask,t)=randi([grade_chir(sum(ME(1:crtask-

1))+1,1),grade_chir(sum(ME(1:crtask-1))+1,2)],1);

elseif

(Prob_fail(crtask,t)*a(1)<scelta<=Prob_fail(crtask,t)*(a(2)+a(1)

))

Prob_ME(sum(ME(1:crtask-1))+2,t)=Prob_fail(crtask,t).*a(2);



elseif

(Prob_fail(crtask,t)*(a(2)+a(1))<scelta<=Prob_fail(crtask,t)*(a(

2)+a(3)))





174

elseif


4)+a(3)))





else

Prob_ME(sum(ME(1:crtask-1))+5,t)=Prob_succ(crtask,t);

grade_jud(crtask,t)=0;

end

crtask=2;

f_vect2=[1 7];

p2=zeros(IF,1);

for n=f_vect2

p2(n)=p(n);

end

g=7;


prod_vect=[];



end

end





while ( a(end)<alpha(sum(ME(1:crtask)),1) ) ||

( a(end)>alpha(sum(ME(1:crtask)),2) )


a(i)=alpha(sum(ME(1:crtask-1))+i,1)+

(alpha(sum(ME(1:crtask-1))+i,2)

-alpha(sum(ME(1:crtask-1))+i,1)).*rand(1);

end


end

scelta=rand(1);






175

else



end

crtask=3;

f_vect3=[1 5 9 10];

p3=zeros(IF,1);

for n=f_vect3

p3(n)=p(n);

end

g=7;


prod_vect=[];



end

end





while ( a(end)<alpha(sum(ME(1:crtask)),1) ) || (

a(end)>alpha(sum(ME(1:crtask)),2) )


a(i)=alpha(sum(ME(1:crtask-1))+i,1) +

(alpha(sum(ME(1:crtask-1))+i,2)-alpha(sum(ME(1:crtask-

1))+i,1)).*rand(1);

end


end

scelta=rand(1);






elseif

(Prob_fail(crtask,t)*a(1)<scelta<=Prob_fail(crtask,t)*(a(2)+a(1)

))





176

elseif


2)+a(3)))





elseif


4)+a(3)))





else



end

% Final run evaluation

Clavien_D(t)=max(grade_jud(:,t));

if Clavien_D(t)>0

Prob_grade(Clavien_D(t),t)=prod(Prob_ME(:,t));

Prob_grade0(t)=prod(Prob_succ(:,t));

else

Prob_grade0(t)=prod(Prob_succ(:,t))+prod(Prob_ME(:,t));

end

end

Prob_Grade_Final=[Prob_grade0;Prob_grade];

%% Raggruppo e riordino le probabilità per ogni grade

Grade_final_0=[]; Grade_final_1=[]; Grade_final_2=[];

Grade_final_3=[]; Grade_final_5=[]; Grade_final_4=[];

for i=1:trial

Grade=Prob_Grade_Final(:,i);

val=find(Grade);

for j=1:length(val)

if val(j)==1

if (Grade(val(j))>0) && (Grade(val(j))<1)

Grade_final_0=[Grade_final_0 Grade(val(j))];

end

elseif val(j)==2


elseif val(j)==3


end

end

end

Grade_prob_vect_0=sort(Grade_final_0);

pd = fitdist(Grade_prob_vect_0','Normal');

xi=linspace(0,1,100);

y = pdf(pd,xi);

177

figure

histogram(Grade_prob_vect_0')

hold on

scale = 10/max(y);

plot((xi),(y.*scale))

hold off

title(['PDF of grade',num2str(0)]);



pd = fitdist(Grade_prob_vect_1','Normal')


y = pdf(pd,xi);

figure

histogram(Grade_prob_vect_1');

hold on

scale = 10/max(y);


hold off

set(gca,'xlim',[0 max(Grade_prob_vect_1)])






y = pdf(pd,xi);

figure


hold on

scale = 10/max(y);


hold off







y = pdf(pd,xi);

figure


hold on

scale = 10/max(y);


hold off



perc_g0=quantile(Grade_prob_vect_0,0.95);

perc_g3=quantile(Grade_prob_vect_3,0.95);

178

APPENDIX 6: Matlab® functions

function randomVector = trirnd(minVal, topVal, maxVal,

varargin);

TRIRND generates discrete random numbers from a triangular distribution. randomValue

= TRIRND(minVal, topVal, maxVal);

The distribution is defined by:

- a minimum and a maximum value

- a "top" value, with the highest probability

The distribution is defined with zero probability at minVal-1 and maxVal+1, and with

highest probability at topVal. Hence every value in the range (including the maximum

and minimum values) have a non-zero probability to be included, whatever topValue is.

The output is a random integer.

randomMatrix = TRIRND(minVal, topVal, maxVal, nrow, ncolumns)

returns a (nrow x ncolumns) matrix of random integers.

NOTES:

* This is a numeric approximation, so use with care in "serious"

statistical applications!

* Two different algorithms are implemented. One is efficient for large number of random

points within a small range (maxVal-minVal), while the other is efficient for large range

for reasonable number of points. For large ranges, there is a O(n^2) relation with regard

to the product of range*number_of_points. When this product reach about a billion, the

runtime reach several minutes.

if nargin < 3

error('Requires at least three input arguments.');

end

nrows = 1;

ncols = 1;

if nargin > 3

if nargin > 4

nrows = varargin{1};

ncols = varargin{2};

else

error('Size information is inconsistent.');

end

end

if topVal > maxVal || topVal < minVal || minVal > maxVal

randomVector = ones(nrows, ncols).*NaN;

return;

end

179

% go for the randomization

mxprob = maxVal-minVal+1;

if mxprob < 51 || (mxprob < 101 && nrows*ncols > 500) ||

(mxprob < 501 && nrows*ncols > 8000) || (mxprob < 1001 &&

nrows*ncols > 110000)

vector = ones(1,mxprob).*topVal;

j = (topVal-minVal+1);

slope = 1/j;

j = j -1;

for i = (topVal-1):-1:minVal

vector = [vector ones(1,floor(mxprob*slope*j)).*i];

j = j - 1;

end

j = (maxVal+1-topVal);

slope = 1/j;

j = j -1;

for i = (topVal+1):maxVal

vector = [vector ones(1,floor(mxprob*slope*j)).*i];

j = j - 1;

end

randomVector =

vector(unidrnd(size(vector,2),nrows*ncols,1));

else

probs = mxprob:-1*mxprob/(topVal-minVal+1):1;

probs = [probs(end:-1:2) mxprob:-1*mxprob/(maxVal-

topVal+1):1];

probs = cumsum(probs./sum(probs));

if nrows*ncols*mxprob > 1000000

% dealing with large quantities of data, hard on

memory

randomVector = [];

i = 1;

while nrows*ncols*mxprob/i > 1000000

i = i * 10;

end

probs = repmat(probs, ceil(nrows*ncols/i), 1);

for j = 1:i

rnd = repmat(unifrnd(0, 1, ceil(nrows*ncols/i),

1), 1, mxprob);

randomVector = [randomVector sum(probs < rnd,

2)+1];

end

randomVector = randomVector(1:nrows*ncols);

else

probs = repmat(probs, nrows*ncols, 1);

rnd = repmat(unifrnd(0, 1, nrows*ncols, 1), 1,

mxprob);

randomVector = sum(probs < rnd, 2)+1;

end

end

% generate desired matrix:

randomVector = reshape(randomVector, nrows, ncols);

180

APPENDIX 7: Questionnaire Results

Si chiede gentilmente a TRE CHIRURGHI di completare le seguenti tabelle,

individualmente.

Le tabelle si riferiscono rispettivamente alla task:

- Isolamento dei peduncoli laterali e della superfice posteriore della

prostata;

- Distacco del Santorini dalla superficie anteriore della prostata;

- Anastomosi;

Le tre tabelle andranno compilate adottando gli stessi criteri di giudizio e le

seguenti istruzioni.

Nella prima colonna sarà necessario descrivere quali sono, a partire dal Critical

Task relativo alla tabella, i Modi di Errore (EMs) possibili che portano al

fallimento della task.

Nella seconda colonna si dovrà indicare la porzione (; valore percentuale da 0

a 100) che lo specifico Modo di Errore (EMs) rappresenta rispetto al numero totale

di fallimenti attesi dell’azione.

Ad esempio:

Task critico: 3. Anastomosi

Modi di Errore: 3.1) Asimmetria sutura;

3.2) Posizione sutura;

3.3) Mancato accostamento dei lembi;

Se 4 volte su 10 che si verifica un errore nell’eseguire un’anastomosi il modo di

errore è “Asimmetria sutura” allora il valore di α relativo al suddetto ME sarà 40.

Nella terza colonna (“Sequenza di recupero”) sarà necessario indicare la

sequenza di azioni, se necessarie, per recuperare l’errore; ovviamente è possibile

181

avere diverse strategie per risolvere lo stesso problema quindi si richiede di

evidenziare tutte le più significative.

Nella quarta colonna (“Punto di rientro”) sarà necessario indicare se e in quale

punto della procedura standard (descritto dal Task Flow fornito) la sequenza di

azioni di recupero si ricongiunge all’originale; nel caso non sia prevista nessuna

azione per recuperare l’errore sarà sufficiente lasciare la terza colonna vuota e

indicare nella quarta colonna la fase immediatamente successiva alla task critico

considerato.

Nella quinta colonna (“Outcome paziente”) sarà invece necessario indicare quale

tra gli outcome descritti da Clavien-Dindo (Mitropoulos, D., et al. (2013); Dindo, et al.

(2004)) è quello che meglio descrive la situazione del paziente assumendo che: si

sia verificato il ME relativo alla riga che si sta compilando, che l’errore sia stato

identificato e che il recupero sia stato portato a termine senza ulteriori errori.

Table 18: Clavien-Dindo grading system for the classification of surgical complications

(Mitropoulos, D., et al. (2013); Dindo et al., (2004))

Grades Definitions

Grade I

Any deviation from the normal postoperative course without the need for

pharmacological treatment or surgical, endoscopic and radiological

interventions. Acceptable therapeutic regimens are: drugs such as antiemetics,

antipyretics, analgesics, diuretics and electrolytes, and physiotherapy. This

grade also includes wound infections opened at the bedside.

Grade II

Requiring pharmacological treatment with drugs other than those allowed for

grade I complications. Blood transfusions and total parenteral nutrition are also

included.

Grade III Requiring surgical, endoscopic or radiological intervention

Grade IV Life-threatening complication

Grade V Death of a patient

182

References:

- Dindo, Daniel, Nicolas Demartines, and Pierre-Alain Clavien. “Classification of Surgical

Complications: A New Proposal With Evaluation in a Cohort of 6336 Patients and

Results of a Survey.” Annals of Surgery 240.2 (2004): 205–213. PMC. Web. 5 Jan. 2017.

- Mitropoulos, D., et al. "Complications after Urologic Surgical Procedures." (2013).

SURGEON ONE

TASK1: Isolamento dei peduncoli laterali e della superfice posteriore della

prostata

Descrizione possibili Modi

di Errore (ME) per la task 1

α

[0-

100]

Sequenza di

recupero del ME “Punto di rientro”

Outcome

paziente

[grade]

ME

1.1

ERRATO PIANO

CHIRURGICO CON

ROTTURA DELLA

PROSTATA

39 TORNARE

ALL’INIZIO DELLO

STEP CHIRURGICO E

RIPRENDERE IL

PIANO CORRETTO

INIZIO

DELL’ISOLAMENTO

DEI PEDUNCOLI

-

ME

1.2

ERRATO PIANO

CHIRURGICO CON

LESIONE DEL

BUNDLE

NEUROVASCOLARE

60 -

- 2d (disfunzione

erettile)

ME

1.3

LESIONE DEL RETTO 1 IDENTIFICAZIONE

DELLA LESIONE,

SUTURA E

RIPARAZIONE

PROSEGUE (SE NON

RICONOSCIUTO

Può PORTARE A

3b-d

(colostomia)

183

TASK2: Distacco del Santorini dalla superficie anteriore della prostata


di Errore (ME) per la task 2

α

[0-

100]

Sequenza di recupero

del ME “Punto di rientro”

Outcome

paziente

[grade]

ME

2.1

APERTURA

PARZIALE O

COMPLETA DEL

SANTORINI CON

SANGUINAMENTO

100 L’ASSISTENTE AL TAVOLO

COMPRIME CON

L’ASPIRATORE IL VASO

SANGUINANTE E USA IL

LAVAGGIO

L’INIZIO

DELL’ANASTOMOSI

2

(trasfusioni)

INCREMENTO DELLO

PNEUMOPERITONEO

L’INIZIO

DELL’ANASTOMOSI

2

(trasfusioni)

SUTURA DEI VASI

SANGUINANTI

L’INIZIO

DELL’ANASTOMOSI

2

(trasfusioni)

TASK3: Anastomosi

Descrizione possibili

Modi di Errore (ME) per

il task 3

α

[0-

100]


del ME “Punto di rientro”

Outcome

paziente

[grade]

ME

3.1

ANASTOMOSI

NON A TENUTA

40 POSIZIONAMENTO DI

PUNTI AGGIUNTIVI

POSIZIONAMENTO

DELLA CISTOSTOMIA

-

RIFACIMENTO

DELL’ANASTOMOSI

POSIZIONAMENTO

DELLA CISTOSTOMIA

MANCATA RISOLUZIONE NON VIENE

POSIZIONATA

CISTOSTOMIA

-

ME LESIONE

DELL’URETRA

10 POSIZIONAMENTO

PUNTI AGGIUNTIVI

POSIZIONAMENTO

CISTOSTOMIA

-

184

3.2

ME

3.3

PINZAMENTO

DEL CATETERE

CON I PUNTI DI

ANASTOMOSI

20 SEZIONE DELLA SUTURA

E RIFACIMENTO

POSIZIONAMENTO

CISTOSTOMIA

ME

3.4

SUTURA DELLA

PARETE

POSTERIORE

DELLA VESCICA

CON QUELLA

ANTERIORE

30 SEZIONE DELLA SUTURA

E RIFACIMENTO

POSIZIONAMENTO

CISTOSTOMIA

185

SURGEON TWO


prostata

Descrizione possibili Modi di

Errore (ME) per il task 1

α

[0-

100]

Sequenza di recupero del

ME

“Punto di

rientro”

Outcome

paziente

[grade]

ME

1.1

MANCATA

IDENTIFICAZIONE DEL

PIANO CHIRURGICO

CORRETTO

42.5 INDIVIDUAZIONE DI UN

NUOVO PIANO

CHIRURGICO

LA SEQUENZA

DI AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO I

ME

1.2

DIFFICILE CONTROLLO

DEL

SANGUINAMENTO

42.5 APPLICAZIONE DI CLIPS O

PUNTI DI SUTURA

LA SEQUENZA

DI AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO

I – II

ME

1.3

LESIONE DEL RETTO 5 RAFFIA DEL RETTO/

COLONSTOMIA

LA SEQUENZA

DI AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO

III

ME

1.4

LESIONE DELLA

PARETE VESCICALE

10 RAFFIA DELLA LESIONE

VESCICALE

LA SEQUENZA

DI AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO I

186



di Errore (ME) per il task 2

α

[0-100]


ME

“Punto di

rientro”

Outcome

paziente

[grade]

ME

2.1

APERTURA DEL

SANTORINI

100 SUTURA DEL SANTORINI

LA SEQUENZA DI

AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO I

TASK3: Anastomosi


di Errore (ME) per il task 3

α

[0-100]


ME

“Punto di

rientro”

Outcome

paziente

[grade]

ME

3.1

MANCATO

ACCOSTAMENTO DEI

LEMBI CON FISTOLA

URINOSA

50 RE-ANASTOMOSI/

APPLICAZIONE DI ULTERIORI

PUNTI DI SUTURA

LA SEQUENZA DI

AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO II

ME

3.2

LACERAZIONE

URETRALE E DEL

COLLO VESCICALE

50 RE-ANASTOMOSI/

APPLICAZIONE DI ULTERIORI

PUNTI DI SUTURA

LA SEQUENZA DI

AZIONI

PERMETTE IL

RECUPERO

TOTALE

CLAVIEN-

DINDO II

187

SURGEON THREE


prostata



α

[0-

100]


ME

“Punto di

rientro”

Outcome

paziente

[grade]

ME

1.1

PRESENZA DI

ADERENZE E

DIFFICOLTOSA

INDIVIDUAZIONE

PIANO CHIRURGICO

40 IDENTIFICAZIONE PIANO

CORRETTO

RECUPERO

PIANO

CORRETTO

GRADO I

ME

1.2

SANGUINAMENTO

PEDUNCOLI

58 APPLICAZIONE CLIPS

METALLICHE O

COAGULAZIONE

RECUPERO GRADO I

ME

1.3

LESIONE RETTO 2 IDENTIFICAZIONE LESIONE

E SUTURA

RECUPERO CON

AZIONI

SUDDETTE

GRADO II-

III

188




α

[0-100]


del ME

“Punto di

rientro”

Outcome

paziente

[grade]

ME

2.1

SANGUINAMENTO 100 CHIUSURA SANTORINI

(“TAPPATO” CON

ASPIRATORE) O

LAVAGGIO A BASSA

PRESSIONE

RECUPERO

SEMPRE

POSSIBILE

GRADO I

TASK3: Anastomosi



α

[0-100]


ME

“Punto di

rientro”

Outcome

paziente

[grade]

ME

3.1

PINZAMENTO DEL

CATETERE

57 RIMOZIONE/TAGLIO DEL

PUNTO DAL CATETERE

SEMPRE

POSSIBILE

GRADO I

ME

3.2

ANASTOMOSI NON

A TENUTA

28 POSIZIONAMENTO ALTRI

PUNTI

SEMPRE

POSSIBILE

GRADO I

ME

3.3

CHIUSURA DELLA

PARETE VESCICALE

ANTERIORE E

6 RIMOZIONE PARZIALE PUNTI

ANASTOMOSI O ESEGUIRE

NUOVA ANASTOMOSI

SEMPRE

POSSIBILE

GRADO I

189

POSTERIORE CON

PUNTO, TALE DA

IMPEDIRE

PASSAGGIO DEL

CATETERE

ME

3.4

LACERAZIONE

MARGINE URETRALE

9 POSIZIONAMENTO NUOVO

PUNTO SUTURA

RECUPERO

POSSIBILE

GRADO I

Dynamic HRA for Surgery applications: development of … · Figure 2: List of GTT developed for...

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