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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