Event Oriented Representation for CollaborativeActivities in an Intensive Care Unit

Liliane Pellegrin

Equipe "Biomathématiques etInformatique Médicale"

Laboratoire d'InformatiqueFondamentale, UMR CNRS 6166Faculté de Médecine, Université

de la Méditerranée27, bd. Jean Moulin 13385Marseille cedex 05, France

liliane.pellegrin@medecine.univ-mrs.fr

Nathalie Bonnardel

Centre de Recherche enPsychologie de la Connaissance,

du Langage et de l’Emotion,Département de PsychologieCognitive et Expérimentale,

Université de Provence29, av. Robert Schuman, Aix en

Provence, cedex 01, Francenathb@up.univ-mrs.fr

Hervé Chaudet

Département d’Epidémiologieet de Santé Publique

Institut de Médecine Tropicaledu Service de Santé des Armées

Parc du Pharo 13998 Marseille Armées,

Francelhcp@acm.org

ABSTRACTWe introduce in this paper a method for describing thecomponents of medical activities during a patientmanagement in an ICU (Intensive Care Unit) by themedical team, including physicians and nurses. Thismethod allows both observing and representing thecollective activity of patient management and shouldbe used by the team members in order to preparethemselves to official accreditation procedures. Anevent-centred representation of medical activities isbuilt during a 3-steps procedure. It successivelyinvolves an event-centred observation phase, an actionextraction and coding phase, and an event andcollaborative representation phase. The results allow usto characterize specific features of this complex andtime-constrained situation as well as the collaborativeactivities between members of the team.

KeywordsComplex situation, medical decision, collaborativework, Task performance and analysis, Intensive CareUnit.INTRODUCTIONDeveloping medical decision-support tools for ICUs’environment are particularly challenging due to thecomplexity and high risk-level of these decisions. Thegreat amount of clinical data issued from patientmonitoring requires an accurate assistance forclinicians’ information processing and decision-making(Randolph, Kane, 1998). This implies thegeneralization of clinical guidelines such as standardcare plans, critical pathways and protocols. It leads tochanges in current practice of medicine. Despite widepromulgation, the application of clinical practiceguidelines has limited effects on medical practice by

physicians (Cabana et al., 1999). Especially, suchguidelines are perceived as formalized at a too highlevel and as too far from realistic conditions of currentmedical activities. A consequence is that their use indaily care situations and the adhesion by physicians arelimited, though some solutions consisting in computer-based decision-support systems were developed (DeClerq et al., 2004). This is described as a “medicalknowledge crisis” whose solution can be given throughknowledge management (http://www.openclinical.org/about.html). In this context, a description of elementsof patient medical management observed in realsituations and based upon a formal task analysis shouldcontribute to fill the actual gap between formal medicalguidelines and realistic medical activities.EORCA project was conducted to give a possibleresponse to these new constraints for clinical practiceswith the implication of knowledge in cognitiveergonomics about task and activity analysis in complexand time-constrained situations. It would be helpful tostudy decision-making in a specialised medicalsituation: the patient management of multiple traumaand neurological injuries in an intensive care unit(ICU). Research in cognitive psychology andergonomics, have been related to medical decision-making (Gaba, 1992, Raufaste, 2001, Patel, Kaufmanand Arocha, 2002). Especially, the focus has been uponcomplex and time-restricted situations, such asanaesthesiology, emergency and intensive care units(Gaba, 1995, Xiao, Hunter, et al., 1996, Nyssen and DeKeyser, 1998, Alberdi et al., 2000, Seagull andSanderson, 2001). These medical activities are, to suchan extent, similar to other activities occurring indynamic complex and critical-safety environments inindustry (nuclear power plans, flight regulation)

implying collective aspects of patient managements(Cicourel, 1990, Marc and Amalberti, 2002, Reddy,Pratt, Dourish, Shabot, 2002) and their implication inclinical information systems (Beuscart-Zéphir et al.,2001, 2005).The basic objective of the project is to propose anintegrated method to observe and formally describeactivities in a given specialized medical field: the ICUmanagement of multiple and neuro traumas patient. Inthis context, the aim of the study that is described inthis paper is to achieve an in-depth understanding ofthe ICU team clinical practice by investigating theoperational composition of action sequences andinformation transmissions during direct observations ofpatients’ management. Our own approach focuses onbuilding a formal method of observation and EventOriented Representation of Collaborative Activities(EORCA), to describe activities of team membersduring patient’s management in an Intensive Care Unit.The event-centred characteristic of the representationwas suggested by the fact that events are the observableparts of medical activities. The expected results are toobtain comparable descriptions of the situations byoriented observations and the application of a formaldesign representation based upon predefined events.One first constraint imposed to this method were to beenough robust and reproducible to allow furtherqualitative and quantitative analyses of the situations,especially from the objective of improving the qualityof patient’s care and the reliability of medical decisionsassociated with the decisions algorithms or guidelinesconcerning severe-injured patients’ managements(Bullock et al., 2000, Albanese and Arnaud, 1999). Ourmethod aims at contributing to build and validatedecision protocols, in which medical decisions not onlyresult from an isolated physician but also from a groupof expert physicians in complex and time-constrainedsituations (Xiao, Milgram and Doyle, 1997). Thepreparation of accreditation implies the recurrent andauto-evaluative analysis by the medical teams ofmedical situations. So, the second constrain was tobuild a method, which will be usable not only byergonomists and medical informatics specialists but bymembers of medical team, physicians and nurses.Thanks to the formalisation of the observed situations,the team will be in position to analyse medical eventsand decisions.The goal of this paper is to introduce the EORCA mainfeatures, its building and the field studies that havebeen conducted to acquire observation data on themedical cases resolution during 2 years of patients’management observations. This paper is organized asfollows. First, we introduce the method we used foranalyzing medical team’s activities, from observationmethodology to the domain ontology that specifies andstructures observables, actors and actions. Secondly,we present the context of the studies in an ICU servicein Marseille (France). In the Results section, wedescribe the application of EORCA model on a set of

real data to evaluate its quality as a representation ofobserved events.

EORCA’S RATIONALETwo main rationales guided the development ofEORCA.Rationale 1: a standardized method of task observationand analysis usable by ICU team membersIdentifying and formalizing ICU practitioners’ actionsand decisions during health care from team behaviorobservation may appear as particularly complex.Intensive Care especially requires a coordinating work,with a strong collaboration between various specialists,such as radiologists, neurologists and nurses. This worksituation is also seen as heterogeneous since each actoris focused upon a single patient with differentactivities, motivations and concerns (Reddy, Dourishand Pratt, 2001). Our first choice was to base themethod upon task observation and representationprocedures that would take into account thecollaborative dimension of health care as well as thehuman factors of decision-making in dynamic andcritical safety conditions (Blandfort and Wong, 2004).Stress due to time pressure, specific situationawareness and mental workload, planning andcooperation processes in experts’ teams are indeedinfluent and systematic elements of medical decision-making that must be considered.Our goal was to develop an accurate and usable taskanalysis-based tool for observing and representingmedical tasks and activities that could be usedrecurrently by unit staff (doctors or nurses). Inaddition, the results should be enough stable to allowfurther qualitative and quantitative analyses of thesituations. For each observed patient management, thecorresponding formal representation should also allowto identify clinical adverse events, such as potentialincidents, dysfunctions, mistakes (Busse and Johnson,1999, Zhang & als, 2004). Such problems may becollective and organisational ones (delays in bloodpockets arrival, disagreements between experts). Theyalso may be issued from individual actions or decisionsfrom physicians and nurses staff (failure in placingartery ways, a novice intern is not able to pass adequateinformation to nurses concerning patients’ state).Theresulting descriptive task model would then be thebasis for developing the prescriptive task model.

Rationale 2: an event-centered representation ofactions identified from observationsObservation methods for studying human actions are,in a general way, restricted to the level of operations,which are the observable part of a whole, including theoperators’ underlying cognitive activities. This impliesthat events involving actors are the observable parts ofmedical activities, hence our choice of an event-centredrepresentation. An event corresponds to theperformance or occurrence of an action. If actions are

time independent, defining activities that may beconducted by agents for changing world state, eventsare both time and space dependent and may betemporally and spatially connected (i.e. sequential,simultaneous…).A domain-oriented ontology formalizes and organizesevery concept involved in observations (Figure 1). Thisontology aims at standardizing observationrepresentations. In association with this ontology, anevent-oriented language in first order logic (STEEL)(Chaudet, 2004) derived from Event Calculus, coupledwith a graphical representation, allows the formalrepresentation of medical team members’ activities.

Figure. 1. Top-level categories of the domain-dependant ontology with some examples.

MATERIAL AND METHODIn accordance with the previous rationales, EORCA isbased on a three-manual-steps method that allowsbuilding the representation of activities from fieldobservations, performed from patient’s admission tohis/her transfer or delegation. Figure 2 summarizes themethod.- Step 1: This step aims at collecting sequences

of events by direct observation, and is ruledby some definite and mandatory instructionsthat enforce standardization of observationand recording. An “observed event” includesan action with an agentive social object. Theaction may be a deed or a verbalcommunication occurring between caregiversas explicit decision-making requests orquestions. Events are recorded inchronological order with non-systematictimestamps. Semi-directive post-taskinterviews with physicians about the observedcases complete observations. The result of thisstep is an accurate, constrained and event-oriented written description in naturallanguage of patient’s care scenario.

- Step 2: This step analyses the scenario forextracting events’ components andsequencing. Actions and agents are identifiedand coded according to the ontology. Thetaxonomy of actions included in the ontologyis based both upon a yet-existing nationalcodification of medical acts and an analysis ofdata coming from a first tentative observationcampaign. If needed, the lower ontologyclasses are completed from observation duringthis step. The result is a scenario transcriptionwhere each event component (i.e. action,agent, time, location) is identified and coded.

- Step 3: This last phase consists in rewritingthe coded scenario with the event-orientedfirst-order logic language. Each element of therepresentation is an event occurrencecomponent, identified during the previousstep. A graphical representation of thescenario is directly derived from this data. Itallows representing in a single template themain elements of patient’s management bymedical team members. The result of this stepis an event-oriented model of the scenario thatmay be processed for further analysis.

Figure 2. Overall description of EORCA’s steps,showing the correspondences between methods.

EXPERIMENT CONTEXTTwo studies were successively conducted in the ICU ofa public hospital in Marseille (France), in 2001 and2003, both of them during 4-5 months. The data wegathered dealt with the management of 24 cases ofneurological and multiple traumas occurring duringthese periods of time.Such pathologies were chosen fortwo main reasons. The first one is that this departmentis specialized in neurological traumatisms. Secondly,multiple traumatisms and neurological injuries are

particularly representative of the kinds of medicalproblems that are encountered in the ICU, andespecially represent the safety-critical dimension ofthese problems. The main features of these 24 cases areas follow:- Various patients’ ages (from 19 to 91 years old),- Injuries mainly caused by road accidents.The data acquisition was manual, directly performedby the one or two ergonomists,without videotapedrecording (Xiao et al., 2004), computer-based methodsusing hand-held devices (such as the FIT technique -Manser & Whener,2002) or specific data acquisitiontool (such as the BabyWatch, Ewing et al., 2002). Twomain reasons are adduced for this restrictive choice.First, the medical team did not accept videotapedrecording for legal reasons (the patient cannot givehis/her agreement during the admission). Secondly, wewanted to use the less sophisticated methods ofinformation acquisition and, in the same time, the mostopened possible without predefined observablecategories. We applied these ecological methods forgathering a large scale of different actions during thepatient management by the team members.

RESULTSAssessing the EORCA method implies analyzing itsability to accurately represent the observed events.Based on the data in the final representation, the goalwas to identify elements of collaborative informationmanagement between the medical team members(clinicians, nurses and outside medical consultants) andinteraction between information management andundertaken actions. For this purpose, two kinds ofanalyses have been performed: a quantitative analysisof the repartition of observations into classes and sub-classes of actions, and a more qualitative analysis ofthe final graphical representation.

Results about classes of observed actionsThree main classes of observed events were taken intoaccount for analyzing the 24 observed patientsmanagement cases:- “Speech acts” between the actors. We define

as a speech (verbal or written) act, eachinformation transmission between actors thathave been observed and transcribed in theobservation sheet. Several sub-classes wereidentified as for instance, the requests foractions from one actor to another one, theexplicit decisions for actions upon patient.

- “Operative actions”. This class gathers actionsperformed by team members, which wererecorded during the patient management astherapeutic deeds linked to major classes ofmedical acts. Non-strictly medical actionswere also identified, essentially collective

actions as mutual help, action attempts andactions of time management.

- “Information acquisitions”. This classincludes all actions implying an informationacquisition performance as clinical andmonitoring examinations, radiologyconsultations, monitoring and other situationparameters, such as control, observation andverification.

Concerning the repartition of the observations intothese 3 main classes, we have hypothesized thatinformation transmissions plays an important role toguide actions, to adjust behaviors when facing todifficulties, to make anticipations and previsions.In the data we gathered, a total amount of 1439observed actions was obtained (see Table 1). Amongthem, speech acts are the most frequent (they represent49.48% of the totality of identified events). Thedifferences between the study modalities are notaleatory (Pearson's Chi-squared test, X-squared =7.9573, df = 2, p-value = 0.01871).

Actions/Years Cases 2001 Cases 2003 Total/ Cat.Speech acts 296

(53.1%)416

(47.2%)712

(49.5%)Operativeactions

164(29.4%)

323(36.6%)

487(33.8%)

Informationacquisitions

97(17.4%)

143(16.2%)

240(16.7%)

Total/Year 557(38.7%)

882(61.3%)

1439

Table 1. Distribution of the recorded observations byyear, set of management cases and main categories ofaction. Percentages are put into brackets.The management of patients by this ICU team ischaracterized by a majority of informationtransmissions between the actors of the situation andby care-related actions (operative actions). Eventsbelonging to information acquisitions, essentiallyclinical examinations and radiological diagnosis, arethe less observed in these situations. Such activitiesconcern essentially physicians and represent only onepart of the overall actions undertaken by all teammembers, for example the nursing acts. An“Observers/Situations” difference is also observedbetween the number of recorded observations in 2001and 2003. Such results strengthen our positions to builda more efficient observation tool, which wouldfacilitate longitudinal studies of patient managements.The analysis of detailed events for Speech Acts havehighlighted that various kinds of oral communicationshave been identified. If we group the two years, themost frequent are 309 information exchanges,following by 115 requests for actions, 96 requests fotinformation, 94 explicit decisions expressed to otherteam members, 61 common discussions between

partners and 31 responses (Pearson's Chi-squared test,X-squared = 16.3015, df = 5, p-value = 0.006034).

Figure 3. Distribution of the percentages of recordedobservations in the categories of class “Speech Acts”,for the cumulated set of observations.Concerning the various kind of Information Exchangesand cumulating the 2 years-sets, transmission ofmedical information follow up are the most observedevents (117 ) , 57 problems and difficultiesmanagements, 51 requests by phone calling, 39information management concerning the current caseevents and 27 actions and time management wereobserved (Chi-squared test for given probabilities, X-squared = 82.0194, df = 4, p-value = < 2.2e-16). Thefigure 3 illustrates the global repartition of observationsin these sub-classes cumulating the two years.

Figure 4. Distribution of the percentages of recordedobservations in the categories of the sub-class“Information Exchanges”, for the cumulated set ofobservations.These detailed results illustrate first the variety ofspecific linguistic activities in such expert andconstrained situations. Secondly, they could be alsointerpreted as an efficient management of the situationby these physicians, which pass verbally informationabout acts, decisions concerning the patient’s care.

Application of the formal representation to a caseEach case has been represented in a final graphicalchart using ontology objects. Each chart illustratesexplicitly the sequence of different kinds of events andthe elements of distributed support for decisions duringcase resolution. Such distributed decision-making is

encountered especially during comments uponradiological results, generally done betweenresuscitators and radiologists or others specialists. Asan example, we introduce an observed patientmanagement. This case concerned a patient, age 48,with multiple injuries and neurological traumatisms(with a Glasgow Coma score ≤ 8) transferred to theunit by the fire brigade after a work accident. A totalamount of 12 persons participated in the patientmanagement for total duration of 26 minutes:

• members of the ICU team (one attendingphysician, one resident, one anaesthetistnurse, two nurses, one medical student werepresent in the unit)

• external medical consultants (a fire brigadephysician, a radiologist, an orthopaedist, twoanaesthetists and a vascular surgeon wererequired and present at the patient’s bedside).

As quantitative indicators, we have obtained an eventdensity ratio of 1.42 observation/min (37 observationsduring 26 min of management). 22 speech acts, 8operative actions and 7 information acquisitions wererecorded. It was also possible to provide otherindicators extracted from this final eventrepresentation, such as the amount of 3 overlappingactions, 19 transmission origins (square), 50transmission receptions and undertaken actions (tombs)and 8 arrivals or departures. Such kinds of informationmay be interpreted as an expression of the casemanagement complexity and of global team workload.The final representational sheet illustrates the essentialmomentum of this patient management:

- Patient’s arrival packageIt concerns the arrival of the patient was characterizedby the information transmissions between the outsidecaregivers, in this case the fire brigade physician, andthe members of the team. These transmissions occurredalternately with other actions as abdomen palpation.The temporal representation allows the description ofthe successive arrival of the ICU specializedcaregivers, the resident followed by both the attendingphysician and the anaesthetist nurse. The patient‘sarrival package is closed at the last transmission of thefire brigade physician to the assistant and the ICUnurse.

Figure 5: Example of a patient management by theICU team (part I). The abbreviations are the following:FBP (fire brigade physician), Ra (radiologist), Or(orthopaedist surgeon), VS (vascular surgeon), A1(Anaesthetist 1), A2 (Anaesthetist 2)

Examinations and medical deed packageThis second phase of the management began with thefirst medical acts upon the patient by the physicianassisted by the anaesthetist nurse. Two majorscomponents can be extracted from theserepresentations: the temporal management of thissituation and its complexity management by bothindividual and collective actions.

Figure 6 : Example of a patient management by theICU team (part II)Temporal management features: During this phase, weobserved that different kinds of actions andtransmissions were performed alternatively or inparallel responding to high temporal constraints. Theseobservations could be interpreted according to the

situation analysis of Xiao and the Lotas Group (1996).They describe the complexity of tasks in emergencymedical care as divided in 4 components: multipleand/or concurrent tasks, uncertainty, changing plansand compressed work procedures associated with highworkload. From our own point of view, some of thesetasks are intrinsically linked with compressed timemanagement. Parallel actions and transmissions, whichhave been observed in our case, can be interpreted asmultiple and concurrent tasks occurring in the sametimestamp (blue elements in figure 6). Secondly,managing the uncertainty of this situation implies theapplication of early planning of future actions linkedwith anticipation of possible problems. The patientarrival package presents essentially such planningactions especially phone calls given to ask for thepresence of others physicians, ordering radios andbloods pockets. The anticipative actions are based uponthe patient status and his/her evolution. In the presentcase, one of the major risks is the deathly evolution dueto haemorrhagic shocks. Undertaken decisions andactions, especially the choice of Noradrenaline and theadaptation of its dosage, have the final goal to correctthe effects of haemorrhagic shock as it is recommendedin clinical guidelines (Hollenberg et al.s, 2004).

Figure 7 : Example of a patient management by theICU team (part III).The collective management features: they are given bythe team involvement represented by 7 nodes on itsaxis, especially at the end of this case (figure 7). This isan efficient response to complexity and timeconstraints and it could be seen as compressed workprocedures managing an high level of workload. Theteam was solicited generally when a caregiver givesglobally information to the team (for example in figure5, nurse 1 informs the team that blood bags wereordered). We can also observe other kind ofcooperation and collaboration as discussions abouttreatments or interpretation of laboratory findingsbetween physicians and especially, collectivedecisions. Several partners mostly executed a similar

action or information transmission. Such actions arethe palpation of the patient’s abdomen by the attendingphysician and the anaesthetist nurse, or the commondiscussions between them. The patient’s delegation tothe surgery unit ended this observation. This finaldescription sheet shows that, even if the trauma patientresuscitation management is not a fully distributeddecision, actions and decisions of responsiblephysicians are based upon an active collaboration andinformation transmissions between all participants.Thanks to this representation, it is possible to describesome features of the caregivers’ activity in a situationcharacterized by a cognitive cooperation betweenactors as J.-M. Hoc introduces it (2002). This activityconcerns both a set of individual performances of thetasks due to the implication of several medicalspecialties and management of all the required tasks bythe actors belonging to the team (cooperation inaction).The case of cooperative activity observed here could bedescribed as a supervised cooperation implyingplanning and allocation of the future tasks and actionsbetween staff members (cooperation in planning).

CONCLUSIONEORCA is a method using a representational modelassociated to a set of procedures for building an event-oriented representation of complex medical situationsinvolving several caregivers. It has been applied uponobservations of neurological and multiple traumasmanagements by an ICU team. The results show thatthis method is able to report the temporal organizationof care management and especially the dynamics ofcommunication and collaboration between actors. Allthese elements are gathered in a descriptive model ofthe situation that highlights the difficulties of casemanagement linked to diagnosis severity, thecomplexity of the situation, and time constraints. It isthen adapted to time-constraint and risky situationswhere a high level of cooperation and planning isrequired. This method should make easier thetranscription of a descriptive model of the situation to aprescriptive one in terms of designing or re-designingprotocols to adjust them to relevant features of thesituation from a social and individual perspective (VanOosterhout et al., 2005). But at least two issues remain.The first one is the reproducibility of the method. Theset of procedures and the ontology aim at giving thismethod its reproducibility, but this characteristicremains to be demonstrated. The second is to find theadequate level of abstraction, as for example actionssequences and scenarios, to describe such situationsand their specificities with granularity appropriate forguideline writing (Dojat, Ramaux and Fontaine, 1998).

ACKNOWLEDGMENTSThis project was granted by a national researchprogram Hospital Clinical Research Program (PHRC-

2000) from French Heath Department for the years2000-2003. We whish to thank Pr. Claude Bastien fromthe Department of Cognitive and ExperimentalPsychology (University of Provence) and the studentsin Cognitive Ergonomics V. Murillo, C. Dobigny, C.Boureau, N. Devictor and also all members of the ICUteam of the DAR-Nord for their precious participationin this project.

REFERENCESAlbanese, J., Arnaud, S. (1999). Traumatisme crânienchez le polytraumatisé. In Sfar, eds. Conférencesd'actualisation. 41ème congrès d'anesthésie et deréanimation. Paris, 737-63.Alberdi, E., Gilhooly, K., Hunter, J., Logie, R., Lyon,A., McIntosh, N., Reiss, J. (2000), Computerisationand decision making in neonatal intensive care: Acognitive engineering investigation. J Clin Monit; 16:85-94.Beuscart-Zéphir, M.C., Anceaux, F., Crinquette, V.,Renard, J.M.(2001), Integrating users’ activitymodeling in the design and assessment of hospitalelectronic patient records: the example of anesthesia,International Journal of Medical Informatics,64, 157-171.Beuscart-Zéphir, M.C., Anceaux, F., Menu, H.,Guerlinger, S., Watbleda, L., Evrard, F. (2005), User-centred, multidimensional assessment method ofClinical Information Systems: a case-study inanaesthesiology, International Journal of MedicalInformatics, 74, 179-189.Blandford, A., Wong, W. (2004). Situation awarenessin emergency medical dispatch, Int. J. Human-Computer Studies 61, 421-52.Bullock, R. & Joint Section on Trauma and CriticalCare of the American Association of NeurologicalSurgeons and the Brain Trauma Foundation (2000).Guidelines for the Management of Severe Head Injury.American Association of Neurological Surgeons, ParkRidge IllBusse, D.K., Johnson, C.W. (1999), Identification andAnalysis of Incidents in Complex, MedicalEnvironments. In Johnson CW, eds. First Workshop onHuman Error and Clinical Systems, Glasgow 101-20.Cabana, M.D., Rand, C.S., Powe N.R., Wu, A.W.,Wilson, M.H., Abboud, P.A., Rubi,n H.R. (1999). Whydon’t physicians follow clinical practice guidelines ? Aframework for improvement, Jounal of AmericanMedical Association, 282, 1458–1465.Chaudet, H. (2004). STEEL: A spatio-temporalextended event language for tracking epidemic spreadfrom outbreak reports. In U. Hahn (Ed.), Proceedingsof KR-MED 2004, First International Workshop onFormal Biomedical Knowledge Representation.Whistler, BC, Canada, 21-30. Obtained in http://

sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS//Vol-102/chaudet.pdfCicourel, A.V.( 1990), The Integration of DistributedKnowledge In Collaborative Medical Diagnosis. In J .Galegher, R.E. Kraut, C. Egido (Eds). IntellectualT e a m w o r k , Hillsdale NJ, Lawrence ErlbaumAssociates; pp. 221-42.De Clerq, P.A., Blom, J., Korsten, H., Hasman, A.(2004). Approaches for creating computer-interpretableguidelines that facilitate decision-support, ArtificialIntelligence in Medicine, 31, 1-27.Dojat, M., Ramaux, N., Fontaine, D. (1998). Scenariorecognition for temporal reasoning in medical domains.Artificial Intelligence in Medicine 14, 139-155.Ewing, G., Ferguson, L., Freer, Y., Hunter, J.,McIntosh, N. (2002). Observational Data Acquired ona Neonatal Intensive Care Unit, University ofAberdeen Computing Science Departmental TechnicalReport TR 0205Gaba, D.M. (1992). Dynamic decision-making inanesthesiology: Cognitive models and trainingapproaches. In: Evans DA, Patel VL, editors. AdvancedModels of Cognition for Medical Training andPractice. Heidelberg, Germany: Springer-Verlag,123-148.Gaba, D.M., Howard, S.K. (1995), Situation awarenessin Anaesthesiology. Human Factors, 37, 20-31.Hoc, J.-M., Carlier, X. (2002). Role of common frameof reference in cognitive cooperation: sharing tasksbetween agents in air traffic control. Cognition,Technology & Work, 4, 37-47.Manser, T., Wehner, T. (2002). Analysing actionsequences: Variations in Action Density in theAdministration of Anaesthesia., Cognition Technology& Work, 4, 71-81.Marc, J., Amalberti, R (2002), Contributionindividuelle à la sécurité du collectif : l’exemple de larégulation du SAMU-(Individual contribution to groupsafety: the example of SAMU (the county medicaltelephone regulation of emergencies), Le travailHumain, 65(3), 217-244.Nyssen, A.S., De Keyser V. (1998). ImprovingTraining in Problem Solving Skills : Analysis of

Anesthetist's Performance in Simulated ProblemSituations. Le travail humain. 1; 61:387-402.Patel, V.L., Kaufman, D.R., Arocha, JF. (2002)Emerging paradigms of cognition in medical decision-making. Journal of Biomedical Informatics. 35, 52–75.Randolph, A.G., Kane, J. (1998). Potential for meetingclinician information requirements in an intensive careunit (ICU) via the web, Proc AMIA Symp, 96-100.Raufaste, E. (2001). Les mécanismes du diagnosticmedical optimisation et expertise, Coll. Le TravailHumain, PUF, Paris France.Reddy, M, Pratt, W, Dourish, P, Shabot, M.M. (2002)Asking Questions: Information Needs in a SurgicalIntensive Care Unit. Proc AMIA Symposium, 647-51.Reddy, M.C., Dourish, P., Pratt, W. (2001).Coordinating Heteroge.neous Work Information andRepresentation in Medical Care. W. Prinz, M. Jarke, Y.Rogers, K. Schmidt, V. Wulf (Eds). Proceedings of 7ththe European Conference on Computer SupportedCooperative Work-ECSCW'01. Bonn, Germany, 239-258.Seagull, F.J., Sanderson, P.M. (2001) AnesthesiaAlarms in Context: An Observational Study. HumanFactors, 43, 66-77.Van Oosterhout, E.M.W., Talmona, J.L., de Clercq,P.A., Schouten, H.C., Tangea, H.J., Hasmana, A.(2005) Three-Layer Model for the design of a ProtocolSupport System, International Journal of MedicalInformatics, 74, 101-110.Xiao, Y., Hunter, W., Mackenzie, C., Jefferies, N., &the LOTAS GROUP (1996). Task complexity inEmergency Medical Care and its implications for teamcoordination, Human Factor,s, 38,4, 636-645Xiao, Y., Milgram, P., Doyle, DJ. (1997), PlanningBehavior and Its Functional Role in the Interactionwith Complex Systems. IEEE Trans. on Syst., Man,and Cybern., Part A: Systems and Humans, 27 313-24.Xiao, Y., Seagull, F.J., Mackenzie, C.F., Klein, K.(2004). Adaptive leadership in trauma resuscitationteams: a grounded theory approach to video analysis,Cognition, Technology & Work , 6, 158-164.Zhang, J., Patel, V., Johnson, T., Shortcliffe, E. (2004).A cognitive taxonomy of medical errors, Journal ofBiomedical Informatics, 37, 193-204.