Manchester Triage System

1. Faculdade de Medicina da Universidade do Porto Manchester Triage System Analysing Waiting Times: Theory vs. Practice Introdução à Medicina Ano lectivo 2009/2010

2. SUMMARY INTRODUCTION Background and Justification RESEARCH QUESTION Objectives METHODS Study Sample and Variables RESULTS Statistics DISCUSSION

3. INTRODUCTION BACKGROUND AND JUSTIFICATION

4. Figure 1 – Time Growth of Triage System. [1] [1] Mackway-Jones K: Emergency Triage, Manchester Triage Group. London: BMJ Publishing Group; 1997.

5. 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

6. 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.

7. RESEARCH QUESTION In Manchester Triage, is the specific waiting time of each urgency category being respected?

8. 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

9. 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.

10. METHODS

11. Our analyze starts on pre-collected data METHODSStudy Design Study is: -Retrospective -Observational

12. 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

13. METHODS Variablesalreadyexistentvariables • 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

14. METHODS Variablesvariablescreated • However, other variables needed to be created and adopted such as: • Age A 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 ER Numerical variable which results from the difference between the date of triage and the moment of medical permission to return home

15. METHODS Variablesvariablescreated • Exceeded Time Nominal 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

16. METHODSData Collection Methods • 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

17. METHODSPlanned Statistical Analysis

18. ResultsStatistics

19. StatisticsGeneral Statistics Feminine 57,9% Masculine 42,1% Table 1 – Total of cases Cases withoutcolour Table 2 – Total of cases with a colour

20. StatisticsGeneral Statistics 48,8% 37,6% 3,7% 1,3% 0,0 0,4% redorangeyellowgreenbluewhitemissing Graph 1 – Thepercentageof cases for eachcolour

21. Statisticsresponse to the objectives Analyse waiting times at an hospital’s emergency service where it is applied Clarify the differences between theory and practice on the maximum waiting time

22. Time(min) • 240 min 120 min 60 min 10 min 0 min Graph 2 – Comparisonbetweenthewaitingtimeestablished (lines) andthemedianofthe real waitingtime (columns)

23. Statisticsresponse to the objectives 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

24. Table 3 – WaitingTimeCodified

25. Figure 2 - Select cases for analyse

26. Medical Assessment Without the time of the medical assessment With the time og the medical assessment Table 4 – Percentageof cases withoutthetimeofthemedicalassessment Redorangeyellowgreenbluewhite Graph3 – Percentageof cases withoutthetimeofthemedicalassessment

27. Thewaitingtime exceeded thetimeexpected? Table 5 – Percentageof cases thatthewaitingtimeexceededthetimepredicted White colours Table 6 – Percentageof cases thatthewaitingtimeexceededthetimepredicted for eachcolour

28. 100% 73,5% 32,8% 18% 4,9% Thewaitingtime exceeded thetimeexpected? No Yes unknown redorangeyellowgreenbluewhite Graph 4 – Percentageof cases thatthewaitingtimeexceededthetimepredicted for eachcolour

29. Statisticsresponse to the objectives 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

30. Statisticsresponse to the objectives Table 7 – Percentageof cases thatthewaitingtimewasexceeded, knowingthatthepatientdied

32. Statisticscuriosities • What was the percentage of patients that died and the percentage of patients that didn´t die in each colour? D=Patientthatdied D=Patientthatdidn´tdie Table 8 – Percentageof cases thatdied for eachcolour

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

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

35. Statisticscuriosities • Mean of the age of the patients that died • Mean of the age of the patients that didn’t die Table 9 – Themeanof age ofthepatientsthatdiedanddidn’tdied

36. Statisticsresponse to the objectives See if there are any differences in the results along the time.

37. %waitingtimeexceeded Graph 5 – Evaluationofthepercentageofthewaitingtimeexceeded Trimestersperyear Table 10– Evaluationofthepercentageofthewaitingtimeexceeded

38. Statisticscuriosities Frequencyofreturns • 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 redcolour had the lowest percentage of return.

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

40. DISCUSSION

41. DiscussionMainconclusions 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). Failure Manchester TriageSystem

42. Yellow, greenandblue cases neverexceedbecausethedefaultwaitingtimes are veryhigh. Thesituation’surgency Cases whichexceededthedefaultwaitingtime Intheorangeandred cases thewaitingtimeisalwaysexceeded. Those cases are seriouslyurgent, doctorswanted to ensurethebestcare for thepatientfirst as a wayofsavinghislifeandsomaybetheytreatedthepatientfirstandjustthentheyrecordedthe case.

43. DiscussionMainconclusions • Urgencyof cases • The rate ofdeathincreases as thesituation’surgencyincreasestoo. • Waitingtime • Thebehaviouris similar whenwetalkaboutthetimepatientswaitedandfrequencytheydie. The high rate of death in red colour: related to the urgency of the cases; not to the waiting time. However for instant: RED CASES

44. DiscussionGeneral conclusions Expectedresults vs ObservedResults 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 ≠ Themortality rate willcorrespond to eachcolour. The mortality rate is higher in the more serious cases (patients that received the red and orange colour). = The percentage of cases which waiting time is exceeded increased along the time. The efficiency rate of MTS increased along the time. ≠

45. DiscussionGeneral conclusions Expectedresults vs ObservedResults 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. 80,5% of the cases which waited more than the standard waiting time of the correspondent colour, the patient ended up dying. ≠

46. 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.

47. Aboutourwork… • 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.

48. Producedby: TURMA 8 Ana Pinho mimed09194@med.up.pt Ana 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