MANCHESTER TRIAGE SYSTEM

Faculdade de Medicina da Universidade do Porto

MANCHESTER TRIAGE SYSTEM

Analysing Waiting Times:

Theory vs. Practice

Introdução à Medicina Ano lectivo 2009/2010

SUMMARY

� INTRODUCTION

Background and Justification

� RESEARCH QUESTION

ObjectivesObjectives

� METHODS

Study Sample and Variables

� RESULTS�Statistics

�DISCUSSION

INTRODUCTION

BACKGROUND AND JUSTIFICATION

[1]Mackway-Jones K: Emergency Triage, Manchester Triage Group. London: BMJ Publishing Group; 1997.

Figure 1 – Time Growth of Triage System. [1]

TRIAGE SYSTEM THE MANCHESTER TRIAGE SYSTEM

� The aim of the Manchester Triage System is to determinethe clinical priority of patients based on their signs andsymptoms

� There are five urgency categories differentiated by colours,with a maximum waiting time[2] :

� Immediate - Red - 0 minutes� Immediate - Red - 0 minutes� Very Urgent - Orange - 10 minutes� Urgent - Yellow- 60 minutes� Standard - Green - 120 minutes� Non-urgent - Blue - 240 minutes

� Manchester Triage System must be always adjusted, kepton permanent mutation and dynamism, by studying itssensitivity and specificity levels [3]

[2]Mackway-Jones K: Emergency Triage, Manchester Triage Group. London: BMJ Publishing Group; 1997.[3]Hardern RD: Critical appraisal of papers describing triage systems. Acad Emerg Med 1999, 6(11):1166-1171

MANCHESTER TRIAGE SYSTEM OTHER STUDIES

� All around the world, studies about MTS and some specificsubjects such as the mortality, triage errors and waitingtimes, have been done as same as simulation surveys

� Hospital Reynaldo dos Santos - evaluates the good managementof waiting times as a factor of efficiency [4]of waiting times as a factor of efficiency

� Hospital Fernando Fonseca, in Lisbon - association between thepriority group and short-term mortality [5]

� Survey made in Netherlands - assess the reliability and validity ofthe Manchester Triage System (MTS) in a general emergencydepartment patient population [6]

[4]Matias C, Oliveira R, Duarte R, Bico P, Mendonça C, Nuno L, Almeida A, Rabaçal C, Afonso S. The Manchester Triage System in acute coronary syndromes. Revista Portuguesa de Cardiologia. 2008 Feb; 27(2):205-16.[5]Martins HM, Cuña LM, Freitas P. Is Manchester (MTS) more than a triage system? A study of its association with mortality and admission to a large Portuguese hospital. Emergency Medicine Journal. 2009 Mar;26(3):183-6.[6] Van der Wulp I, van Baar ME, Schrijvers AJ. Reliability and validity of the Manchester Triage System in a general emergency department patient population in the Netherlands: results of a simulation study. Emergency Medicine Journal. 2008 Jul; 25(7):431-4.

RESEARCH QUESTION

In Manchester Triage, is the specific waiting time of each urgency category being time of each urgency category being

respected?

OBJECTIVES

� The aim of this study is to evaluate the ManchesterTriage system:

� Analyse waiting times at an hospital’s emergencyservice where it is appliedservice where it is applied

� Analyse the outcome of the patients who waited moretime than what was expected

� Confirm the correspondence between the colourassigned and the waiting time

OBJECTIVES

� Clarify the differences between theory and practice onthe maximum waiting time

� Understand if the rate of death is superior in thepatients who waited more time in the Urgency

� See if there are any differences in the results along thetime.

METHODS

� Our analyze starts on pre-collected data

METHODSSTUDY DESIGN

Study is:Study is:

-Retrospective

-Observational

METHODSSTUDY PARTICIPANTS

� Target population - all the patients who had entered inthe emergency care that uses the Manchester TriageSystem

� Inclusion Criteria:- Initially patients with more than 18 years old but now the age 16- Initially patients with more than 18 years old but now the age 16was chosen as a criteria because in hospital’s terms people areconsidered adults since this age.

- Had gone to emergency care between the 1st October 2005 toSeptember 2008

� No exclusion criteria

� Sample – the target population

� The whole data that might be considered on our study derives from theregistry of characteristics of all urgency episodes, including many variableslikely to be studied and deeply analyzed, that were already present on theSPSS database:

METHODS

VARIABLESALREADY EXISTENT VARIABLES

�Date of Birth

� Sex

�Date and hour of triage

� Priority – recodified into the variable Colour

�Date and hour of the medical observation

�Date of discharge

METHODS

VARIABLESVARIABLES CREATED

� However, other variables needed to be created andadopted such as:

�Age

A numerical variable obtained by the difference between the momentA numerical variable obtained by the difference between the momentthey were subjected to MTS and the date of birth

�Waiting times

A numerical variable that informs us about the time that each patient

had waited on the emergency room before being seen by a doctor

� Time spent on ER

Numerical variable which results from the difference between the dateof triage and the moment of medical permission to return home

� Exceeded Time

Nominal categorical variable which results from the relation betweenWaiting Times and the time assigned to each colour, due to the priority ofpatient, reporting if the time was respected or not

METHODS

VARIABLESVARIABLES CREATED

� Registration of Medical Observation

Categorical and nominal variable that informs us if the doctor has

registered the moment when he saw the patient

�Death

Categorical and nominal variable which, from the result of the ER

episode, notices if the patient ended up dead or alive

� Secondary Data;

� Information was transferred into an SPSS database;

� Several computer programs have to be used

METHODSDATA COLLECTION METHODS

� Several computer programs have to be used

� Variables were deeply studied through a statisticalassessment to clear the problems that haveappeared on the development of our study aims.

SPSS

Estimate the median time from MTS/ triage moment to first medical assessment

Percentage (%) of patients who had waited more than the specified time for each colour on

Manchester Triage System

Estimate of the median of the waiting time of each colour and comparison with the time that was

supposed to be in theory

METHODSPLANNED STATISTICAL ANALYSIS

supposed to be in theory

Estimate of the mortality rate at each colour(when it respects or exceed the assigned waiting time)

Comparison between the mortality rate and the time of waiting and conclude if:

•The colour was right assessed or if that was the cause of death

•In case of a right assessment by the triage nurse, the time of

waiting was not the correct and

if that might have been the

death cause

•There were other intervenient factors

RESULTS

STATISTICS

Statistics

N Valid 336526Missing 0

Feminine 57,9%Masculine 42,1%

STATISTICSGENERAL STATISTICS

Statistics

Colours given

N Valid 336477Missing 49 Cases without colour

Table 1 – Total of cases

Table 2 – Total of cases with a colour

126474

164294

120000

140000

160000

180000

Patients

STATISTICSGENERAL STATISTICS

37,6%

48,8%

1446

27490

431812455

490

20000

40000

60000

80000

100000

120000

Vermelho Laranja Amarelo Verde Azul Branco Missing

8,2%

red orange yellow green blue white missing

0,4%1,3% 3,7%

0,0

Graph 1 – The percentage of cases for each colour

Analyse waiting times at an hospital’s emergency service where it is applied

STATISTICSRESPONSE TO THE OBJECTIVES

Clarify the differences between theory and practice on themaximumwaiting time

240 min

Time(min)

120 min120 min

60 min

10 min

0 min

Graph 2 – Comparison between the waiting time established (lines) and themedian of the real waiting time (columns)

STATISTICSRESPONSE TO THE OBJECTIVES

Confirm the correspondence between the colour assigned and thewaiting time, if the mean time from MTS to first medicalassessment determined in theory is being done sucessfully inassessment determined in theory is being done sucessfully inpractice

Variable – waiting time

Valid Missing(Cases that don’t have thewaiting time, because thetime of medical assesment

wasn’t registered)

Variable – Waiting Time CodifiedVariable – Waiting Time Codified

Valid Missing(Cases that don’t have thewaiting time because thetime of medical assessmentwasn’t registered + Cases

that besides having the valueof the waiting time, it is

incorrect)

Table 3 – Waiting Time Codified

Figure 2 - Select cases for analyse

Colour Percentage

Red 25,2%

Orange 34,3%

Yellow 38,1%

Medical AssessmentWithout the time of the medical assessmentWith the time og the medical assessment

Yellow 38,1%

Green 66,3%

Blue 80,3%

White 76,7%

Table 4 – Percentage of cases without the time of themedical assessment

Graph3 – Percentage of cases without the time of themedical assessment

Red orange yellow green blue white

Percentage

Valid Yes 30,8%

No 14,9%

unknown ,9%

Missing 53,5%

The waiting time exceeded the time expected?

Table 5 – Percentageof cases that thewaiting time exceededthe time predicted

White colours

Colour Percentage

Red 100%

Orange 73,5%

Yellow 32,8%

Green 18%

Blue 4,9%

White unknown

Table 6 – Percentageof cases that thewaiting time exceededthe time predicted for each colour

The waiting timeexceeded the timeexpected?

NoYesunknown

100% 32,8% 18% 4,9% 73,5%

Graph 4 – Percentage of cases that the waiting time exceeded the time predicted for each colour

red orange yellow green blue white

STATISTICSRESPONSE TO THE OBJECTIVES

Analyse the outcome of the patients who waited more time thanwhat was expected

Understand if the rate of death is superior in the patients who waited more time in the Urgency

Patient waited more than theforeseen time?

Yes No

STATISTICSRESPONSE TO THE OBJECTIVES

Yes No

Patientdied?

Yes 80,5% 19,5%

No 32,4% 67,6%

Table 7 – Percentage of cases that the waiting time was exceeded, knowingthat the patient died

Didn’t wait more than the foreseen

time

Didn’t wait more than the foreseen

time

Waited more thanthe foreseen timeWaited more thanthe foreseen time

STATISTICSCURIOSITIES

� What was the percentage of patients that died and the percentage ofpatients that didn´t die in each colour?

Red Orange Yellow Green Blue White

D 28,8%(416)

0,7%(199)

0,1%(81)

0,0%(6)

0,0%(0)

0,0%(3)

D 71,2%(1030)

99,3%(27291)

99,9%(126392)

100,0%(164288)

100,0%(4318)

100,0%(12455)

D=Patient that died

D=Patient that didn´t die

Table 8 – Percentage of cases that died for eachcolour

Final Result - DEATHTotal

YES NO

COLOUR

REDCount 294 786 1080

% of Total 0,6% 1,6% 2,2%

ORANGECount 101 13173 13274

% of Total 0,2% 26,3% 26,5%

YELLOWCount 12 25667 25679

COLOUR YELLOW% of Total 0,0% 51,3% 51,3%

GREENCount 1 9961 9962

% of Total 0,0% 19,9% 19,9%

BLUECount 0 42 42

% of Total 0,0% ,1% ,1%

TotalCount 408 49629 50037

% of Total 0,8% 99,2% 100,0%

Table 3 – Crosstab comparing the outcome Death for each colour in the cases in which the waiting time was exceeded.

Final Result - DEATHTotal

YES NO

ORANGECount 48 4731 4779

% of Total 0,0% 4,6% 4,6%

YELLOWCount 50 52489 52539

% of Total 0,0% 50,7% 50,7%COLOURS

% of Total 0,0% 50,7% 50,7%

GREENCount 1 45421 45422

% of Total 0,0% 43,9% 43,9%

BLUECount 0 807 807

% of Total 0,0% ,8% ,8%

TotalCount 99 103448 103547

% of Total 0,1% 99,9% 100,0%

Table 4 – Crosstab comparing the outcome Death for each colour in the cases in which the waiting time was not exceeded.

� Mean of the age of thepatients that died

� Mean of the age of thepatients that didn’t die

STATISTICSCURIOSITIES

Statistics Statistics

Age (years)

N Valid 705

Missing 0

Mean 72,63

Age (years)

N Valid 335820

Missing 0

Mean 46,47

Table 9 – The mean of age of the patients that died and didn’t died

See if there are any differences in the results along the time.

STATISTICSRESPONSE TO THE OBJECTIVES

See if there are any differences in the results along the time.

Graph 5 – Evaluation of thepercentage of the waiting timeexceeded%

wai

ting

time

exce

eded

Trimesters per year

4ª-2005 1ª-2006 2ª-2006 3ª-2006 4ª-2006 1ª-2007

25,7% 28,6% 27,4% 31,4% 30,6% 34,9%

2ª-2007 3ª-2007 4ª-2007 1ª-2008 2ª-2008 3ª-2008

29,5% 30,4% 35,0% 46,9% 55,5% 52,5%

Table 10–Evaluation of thepercentage of thewaiting timeexceeded

STATISTICSCURIOSITIES

Frequency of returns

� Analyzing the frequency of returns after 48 and 72hours for each colour we observed that the lesshours for each colour we observed that the lessserious cases such as blue and green have a higherrate of patients that return to US. In contrast, thered colour had the lowest percentage of return.

STATISTICSCURIOSITIES

FREQUENCY OF RETURNS

Tables 11 & 12– Percentage of return, 48 and 72h after the first entry in US

DISCUSSION

DISCUSSIONMAIN CONCLUSIONS

The number of missing cases is very high (53,5%).

The number of cases that the medical observation was

made in an incorrect way (58 errors and 21 possible

errors).errors).

Cases whichexceeded the

default waiting time

The situation’surgency

Yellow, green and bluecases never exceedbecause the defaultwaiting times are veryhigh.

In the orange and redcases the waiting time isalways exceeded.

Those cases are seriously urgent, doctors wanted to ensure thebest care for the patient first as a way of saving his life and somaybe they treated the patient first and just then they recordedthe case.

RATE OF DEATH

Urgency ofcases

Waiting time

DISCUSSIONMAIN CONCLUSIONS

• Urgency of casesThe rate of death increases as thesituation’s urgency increases too.

• Waiting time• Waiting timeThe behaviour is similar when wetalk about the time patients waitedand frequency they die.

However for instant: RED CASES

The high rate of death in red colour:

related to the urgency of the cases;

not to the waiting time.

DISCUSSIONGENERAL CONCLUSIONS

The waiting times will correspond to the colour given to the patient, but obviously there will appear some differences.

In the orange and red cases where the waiting time

exceeded in a large scale the expected one

≠

The mortality rate willcorrespond to each colour.

expected one

The mortality rate is higher in the more serious cases

(patients that received the red and orange colour).

=

≠The efficiency rate of MTS increased along the time.

The percentage of cases which waiting time is

exceeded increased along the time.

DISCUSSIONGENERAL CONCLUSIONS

The waiting times will correspond to the colour given to

the patient.

Some patients waited more time then the recommended for colours which represent

less urgent cases.

≠

The cause for the death of some patients was the urgency of the case.

less urgent cases.

80,5% of the cases which waited more than the

standard waiting time of the correspondent colour, the patient ended up dying.

≠

DISCUSSIONFINAL CONCLUSION

As a general conclusion, we think that in theory, MTS could really be very useful, but these triage system presents some limitations, such as the lack of information about medical

observation which means a great number of missing cases in the DB.

In spite of having all the advantages already experimented, we think that MTS is still possible to improve and even explore the effects of social status and gender on the colour assigned and the time spent waiting before being seen by a

doctor.

ABOUT OUR WORK…

� Finally, we could give an answer to the main aims of our work and so we were able to evaluate what we proposed to – The efficiency of MTS, namely the we proposed to – The efficiency of MTS, namely the waiting times that correspond to each colour of this

system.

PRODUCED BY:

TURMA 8

Ana Pinho mimed09194@med.up.pt

Ana Costa mimed09198@med.up.ptAna Costa mimed09198@med.up.pt

Ana Sofia Pereira mimed09240@med.up.pt

Claudia Marinho mimed09007@med.up.pt

Diana Gonçalves mimed09029@med.up.pt

Helena Brandão mimed09052@med.up.pt

Inês André mimed09235@med.up.pt

José Magalhães mimed09155@med.up.pt

Mariana Morais mimed09101@med.up.pt

Rita Soares mimed09152@med.up.pt

Tania Costa mimed09185@med.up.pt