Manchester Triage System

MANCHESTER TRIAGE SYSTEMAnalysing Waiting Times:Theory vs. Practice

Faculdade de Medicina da Universidade do Porto

Introdução à Medicina Ano lectivo 2009/2010

SUMMARY INTRODUCTION Background and Justification

RESEARCH QUESTION Objectives

METHODS Study Sample and Variables

RESULTSStatistics

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 determine the clinical priority of patients based on their signs and symptoms

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

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, kept on permanent mutation and dynamism, by studying its sensitivity 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 specific subjects such as the mortality, triage errors and waiting times, have been done as same as simulation surveys

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

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

Survey made in Netherlands - assess the reliability and validity of the Manchester Triage System (MTS) in a general emergency department 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 respected?

OBJECTIVES

The aim of this study is to evaluate the Manchester Triage system:

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

Analyse the outcome of the patients who waited more time than what was expected

Confirm the correspondence between the colour assigned and the waiting time

OBJECTIVES

Clarify the differences between theory and practice on the maximum waiting time

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

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

METHODS

Our analyze starts on pre-collected data

METHODSSTUDY DESIGN

Study is:

-Retrospective

-Observational

METHODSSTUDY PARTICIPANTS

Target population - all the patients who had entered in the emergency care that uses the Manchester Triage System

Inclusion Criteria:- Initially patients with more than 18 years old but now the age 16 was chosen as a criteria because in hospital’s terms people are considered adults since this age.- Had gone to emergency care between the 1st October 2005 to September 2008

No exclusion criteria

Sample – the target population

The whole data that might be considered on our study derives from the registry of characteristics of all urgency episodes, including many variables likely to be studied and deeply analyzed, that were already present on the SPSS database:

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 VARIABLESALREADY EXISTENT VARIABLES

METHODS VARIABLESVARIABLES CREATED

However, other variables needed to be created and adopted such as:

AgeA numerical variable obtained by the difference between the

moment they 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 ERNumerical variable which results from the difference

between the date of triage and the moment of medical permission to return home

Exceeded TimeNominal categorical variable which results from the relation

between Waiting Times and the time assigned to each colour, due to the priority of patient, reporting if the time was respected or not

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

METHODS VARIABLESVARIABLES CREATED

Secondary Data;

Information was transferred into an SPSS database;

Several computer programs have to be used

Variables were deeply studied through a statistical assessment to clear the problems that have appeared on the development of our study aims.

SPSS

METHODSDATA COLLECTION METHODS

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

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

METHODSPLANNED STATISTICAL ANALYSIS

RESULTSSTATISTICS

Feminine 57,9%Masculine 42,1%

Cases without colour

STATISTICSGENERAL STATISTICS

Table 1 – Total of cases

Table 2 – Total of cases with a colour

STATISTICSGENERAL STATISTICS

red orange yellow green blue white missing

0,4%

37,6%

48,8%

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 the maximum waiting time

240 min

Tim

e(m

in)

120 min

60 min

10 min0 min

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


Confirm the correspondence between the colour assigned and the waiting time, if the mean time from MTS to first medical assessment determined in theory is being done sucessfully in practice

Variable – waiting time

Valid Missing(Cases that don’t have

the waiting time, because the time of medical assesment wasn’t

registered)

Variable – Waiting Time Codified

Valid Missing(Cases that don’t have

the waiting time because the time of medical assessment wasn’t

registered + Cases that besides having the value of 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%

Green 66,3%

Blue 80,3%

White 76,7%

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

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

Graph3 – Percentage of cases without the time of the medical assessment

Red orange yellow green blue white

Percentage

Valid Yes 30,8%

No 14,9%

unknown ,9%

Missing 53,5%

White colours

Colour Percentage

Red 100%

Orange 73,5%

Yellow 32,8%

Green 18%

Blue 4,9%

White unknown

The waiting time exceeded the time expected?

Table 5 – Percentage of cases that the waiting time exceeded the time predicted

Table 6 – Percentage of cases that the waiting time exceeded the time predicted for each colour

The waiting time exceeded the time expected?

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


Analyse the outcome of the patients who waited more time than what was expected

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

Patient waited more than the foreseen time?

Yes No

Patient died?

Yes 80,5% 19,5%

No 32,4% 67,6%


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

STATISTICSCURIOSITIES

What was the percentage of patients that died and the percentage of patients 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%(12639

2)

100,0%(16428

8)

100,0%(4318)

100,0%

(12455)

D=Patient that died

D=Patient that didn´t die

Table 8 – Percentage of cases that died for each colour

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

% 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

COLOURS

ORANGECount 48 4731 4779

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

YELLOWCount 50 52489 52539

% 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 the patients that died

Mean of the age of the patients that didn’t die

STATISTICSCURIOSITIES

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.


Graph 5 – Evaluation of the percentage of the waiting time exceeded

Trimesters per year

%w

aiti

ng

tim

e ex

ceed

ed

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 the percentage of the waiting time exceeded

STATISTICS CURIOSITIES

Frequency of returns

Analyzing the frequency of returns after 48 and 72 hours for each colour we observed that the less serious cases such as blue and green have a higher rate of patients that return to US. In contrast, the red colour had the lowest percentage of return.

STATISTICS CURIOSITIES

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).

Cases which exceeded the

default waiting time

The situation’s urgency

Yellow, green and blue cases never exceed because the default waiting times are very high.

In the orange and red cases the waiting time is always exceeded.

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

DISCUSSIONMAIN CONCLUSIONS

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

• Waiting timeThe behaviour is similar when we talk about the time patients waited and 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.

The waiting times will correspond to the colour given to the patient, but

obviously there will appear some differences.

The mortality rate will correspond to each colour.

The mortality rate will correspond to each colour.

In the orange and red cases where the waiting time exceeded in a large scale the expected one

In the orange and red cases where the waiting time exceeded in a large scale the expected one

The mortality rate is higher in the more serious

cases (patients that received the red and

orange colour).

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.

The waiting times will correspond to the colour

given to the patient.

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

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

Some patients waited more time then the

recommended for colours which represent less

urgent cases.

Some patients waited more time then the

recommended for colours which represent 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.

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 waiting times

that correspond to each colour of this system.

PRODUCED BY:TURMA 8

Ana Pinho [email protected] Costa [email protected] Sofia Pereira [email protected] Marinho [email protected] Gonçalves [email protected] Brandão [email protected] Inês André [email protected] José Magalhães [email protected] Morais [email protected] Soares [email protected] Costa [email protected]

Documents

Manchester Triage System