Health Services Research Fund & Health Care and Promotion Fund: Research Dissemination Reports (Series 3)published_or_final_versio

Chu, SCK

Fung, H

Ho, MPP

Tinsley, H

Hong Kong Medical Journal

HKU Scholars Hub

Title Operational improvement in a specialist out-patient clinic inHong KongAuthor(s) Ho, MPP; Fung, H; Chu, SCK; Tinsley, HCitation Hong Kong Medical Journal, 2006, v. 12 n. 6, suppl. 3, p. 7-10Issued Date 2006URL http://hdl.handle.net/10722/57391Rights Hong Kong Medical Journal. Copyright © Hong Kong MedicalAssociation.Hong Kong Med J Vol 12 No 6 Supplement 3 December 2006      HEALTH SERVICES RESEARCH FUND – HEALTH CARE AND PROMOTION FUNDMPP Ho 何姚翩翩H Fung 馮康SCK Chu 朱進強H Tinsley 丁詩妮Operational improvement in a specialist out-patient clinic in Hong KongKey Messages1. The maximum number of pat ients  is  an important measure for quantifying clinic congestion.2. The mean patient waiting time is a useful measure for studying the operation of clinics.3.	 An	 efficient	 smoothing-out	 of	the number of patients booked in	 specialty-sessions	 can	 help	to resolve common congestion and waiting time problems. This will lead to operational improvement	of	 specialist	out-patient clinics.Strategy and Planning, Hospital AuthorityMPP HoNew Territories East Cluster, Hospital AuthorityH FungDepartment of Mathematics, The University of Hong KongSCK ChuCaritas Medical Centre, Hospital AuthorityH TinsleyHCPF project number: 512021Principal applicant and corresponding author:Mrs Mary PP HoStrategy and Planning, Hospital Authority, 14B Argyle Street, Kowloon, Hong Kong SAR, ChinaTel: (852) 2300 6295Fax: (852) 2882 436E-mail:	mppho@ha.org.hkHong Kong Med J 2006;12(Suppl 3):S7-10IntroductionCongestion	 is	 a	major	 problem	 in	 specialist	 out-patient	 clinics	 administered	by the Hong Kong Hospital Authority (HA). In some clinic sessions, the total number of patients and accompanying relatives/friends far exceeds the number of available seats in the waiting hall. Another problem is the long patientwaiting time for a doctor consultation. In some instances, patients have to wait for more than 2 h, making the waiting hall even more crowded. The main objective of this study was to quantify and address the problems of congestion and long patient waiting times in the clinic under study.MethodsThe	 study	 clinic	was	 open	 5.5	 days	 per	week	 and	 had	 six	morning	 and	fiveafternoon	sessions.	The	clinic	occupied	two	floors	and	multiple	specialties	were	scheduled	 on	 each	 floor	 in	 each	 session.	 The	 clinic	 provided	 10	 specialisedout-patient	services:	medicine,	surgery,	geriatrics,	paediatrics,	orthopaedics,gynaecology, antenatal, postnatal, ENT (ear, nose, throat), and dermatology. All patients attending this clinic were referred from either hospitals or otherclinics. Both the registration time and the number of booked patients were set by hospital management staff. Workload statistics and the schedule of clinic sessions during the studyperiod from November 1995 to April 1996 were analysed. An illustration of a typical	clinic	schedule	with	three	specialty-sessions	is	shown	in	Table	1.Congestion modelIn order to quantify the level of patient congestion in each specialty andclinic session, a modelling approach was adopted. This was in line with many overseas studies into the operational performance of medical facilities.1,2 Patient waiting time is a useful measure for studying clinic operation.3 Our previous experience indicated that the maximum number of patients in the waiting hall is also important when quantifying the level of congestion.4,5 We adopted a congestion model to assess this problem. Input measuresincluded the number of patients attending each session, case types, the mean doctor consultation time, and the number of available doctors. Output measuresincluded the maximum number of waiting patients and the patient waiting time. Model validationComputer	 simulation	was	 applied	 to	model	 the	 flow	of	 patients	 and	 used	 tovalidate the congestion model.6 We simulated a variety of patient arrivalpatterns through four different scenarios designed to highlight problemsincluding long patient waiting time (>1 h), and overcrowding, with themaximum number of patients exceeding a ‘tolerable limit’. Different values were set for the ‘tolerable limit’ of different specialties,taking	 into	 account	 the	 available	 space	 in	 the	waiting	hall	 of	 each	floor.	For	example, the value of the ‘tolerable limit’ for the specialty medicine was much larger than that for orthopaedics, because the orthopaedic patients were more likely to be accompanied by relatives/friends and because some orthopaedic 8      Hong Kong Med J Vol 12 No 6 Supplement 3 December 2006Ho et alpatients used wheelchairs, thus requiring more space.Scenario ADepicted the actual patient arrival pattern and was used as a control for comparison. Scenario BWe hypothesised that a major cause of the overcrowdingand long waiting times was the ‘time lag’ between the registration	 start-time	 and	 the	 consultation	 start-time.	For some clinic sessions, the ‘time lag’ was nearly 2.5 h.Assuming	that	the	doctor	consultation	start-time	was	fixed	(due to the availability of doctors after ward rounds) and the ‘time lag’ was necessary for the movement of records, we proposed shortening the ‘time lag’ to 1 h. We set the registration	start-time	at	1	h	before	the	consultationstart-time	for	scenario	B.Scenario CThe	original	scenario	was	modified	by	manually	evening	out	the	number	of	booked	patients	between	the	two	floors.Scenario DA	smoothing	technique	(patient-peak	smoothing)	was	built	into the congestion model to help solve the congestion problem. The purpose was to smooth out the congested clinic sessions that had a large number of patients in the waiting	hall.	This	was	achieved	by	re-scheduling	patients	from	 the	 more-congested	 specialty-sessions	 to	 the	 less-congested	specialty-sessions.ResultsScenario AMean patient waiting timeIt	was	 found	 that	 all	 sessions	 on	 each	 floor	 had	 at	 least	one specialty with a mean waiting time of >1 h. Of the 3 specialty-sessions,	 31	 had	 a	mean	waiting	 time	 of	 >1	 h,	25 had >1.5 h, 4 had >2 h. ‘Time lag’ and overbooking for specialty sessions were postulated as the reasons for this long waiting time.Maximum number of waiting patientsIn	six	of	the	37	specialty-sessions,	the	maximum	number	of waiting patients was over the ‘tolerable limit’. In two extremely	 congested	 specialty-sessions,	 there	were	more	than 100 patients above the ‘tolerable limit’.Scenario BWe performed two experiments under scenario B: (1)registration	 start-time	was	 set	 at	 1	 h	 before	 consultation	start-time;	and	(2)	similar	to	(1)	but	the	duration	of	regis-tration was extended by 30 min. This aimed to spread the patient arrivals over a longer period.Mean waiting time of patientsOf	the	37	specialty-sessions,	the	number	of	sessions	with	a	mean waiting time of >1 h, >1.5 h, and >2 h for scenarios A and B are illustrated in Table 2. Patients experiencedless waiting time in scenario B(1) than scenario A. The registration	 start-time	was	 set	 so	 that	patients	only	 came	1	h	before	the	session	start-time.	Scenario	B(2)	was	even	better than scenario B(1). The extension of the registrationstart-time	enabled	patients	to	spread	out	their	arrivalpattern and resulted in an overall shortening of the waiting time per patient.Maximum number of waiting patientsIn	scenario	A	we	identified	four	specialty-sessions	with	a	mean	waiting	time	of	>2	h	and	six	specialty-sessions	with	the maximum number of patients over the ‘tolerable limit’. Since	 two	of	 these	specialty-sessions	had	both	problems,	there	were	eight	problematic	specialty-sessions	(Table	3).	Under scenario B(1), the maximum number of patients was	reduced	and	brought	below	the	‘tolerable	limit’	in	five	of	 the	 eight	 specialty-sessions	 (Table	 3).	Under	 scenario	B(2), there was an even greater improvement over scenario A. The maximum number of patients were reduced and brought below the ‘tolerable limit’ for all eight problematic specialty-sessions	(Table	3).Scenario CDifferent	options	were	tried,	especially	for	those	specialty-Table 1.  Sample of a typical clinic scheduleDay of Session Floor Specialty Regis- No. ofthe week    tration booked    time patientsMonday	 Morning	 1st	 Medicine/	 8:00-10:00	 80-90	 	 	 surgeryFriday	 Afternoon	 2nd	 Geriatrics	 13:30-15:00	 200Table 2.  Waiting times under different scenariosMean waiting time Scenario (No. of sessions) A B(1) B(2)>1	h	 31	 24	 12>1.5	h	 25	 12	 20>2	h	 24	 20	 20Table 3.  Problematic specialty-sessionsSession Specialty Floor Scenario (maximum   No. of waiting patients)   A B(1) B(2)Monday	am	 Geriatrics	 1st	 170	 170	 100Tuesday	am	 Medicine	 2nd	 160	 130	 123Wednesday	am	 Medicine	 2nd	 374	 147	 118Wednesday	pm	 Orthopaedics	 1st	 108	 108	 153Thursday	am	 Geriatrics	 1st	 152	 115	 165Thursday	am	 Surgery	 2nd	 190	 197	 173Friday	pm	 Geriatrics	 1st	 167	 169	 175Saturday	am	 Medicine	 2nd	 301	 117	 111Hong Kong Med J Vol 12 No 6 Supplement 3 December 2006      9Operational	improvement	in	a	specialist	out-patient	clinic	in	Hong	Kongsessions where the maximum number of patients was over the ‘tolerable limit’. However, most of the trial options were not operationally feasible due to the mix of special-ties and the availability of manpower resources. Only one option proved to be feasible and this involved dividing	 the	 440	 patients	 for	 the	 problematic	 specialty-session, Wednesday am (Medicine) [originally locatedon the second floor], evenly between the two floors.This option was feasible because there were no otherspecialties scheduled on the first floor. Although the mean waiting time per patient remained unchanged, thecongestion problem was greatly eased with the reduction of the maximum number of patients from the original 34 on	the	second	floor	to	187	on	each	floor,	which	was	below	the ‘tolerable limit’.Scenario DMaximum number of waiting patients and mean patient waiting timeA comparison of the performance before and after applyingpatient-peak	 smoothing	 is	 given	 in	 Figs	 1	 and	 2.	 There	was	a	significant	improvement	in	the	two	most	congested	specialty-sessions,	 namely,	 sessions	 11	 and	 22	 in	 Fig	 1.	The maximum number of waiting patients was reduced by nearly 50%. However, the mean waiting time for these two specialty-sessions	remained	unchanged	(Fig	2).	Althoughspecialty-sessions	 7,	 9,	 and	 19	were	 less-congestedperformance worsened after smoothing. Smoothingincreased both the maximum number of waiting patients and the mean waiting time for patients in these threespecialty-sessions.	 However,	 the	 congestion	 was	 keptbelow the ‘tolerable limit’ and the waiting time was <2.5 h.This	 worsening	 effect	 in	 some	 specialty-sessions	wasunavoidable due to the shifting of patients from themost-congested	sessions	to	less-congested	sessions,	while	keeping the overall total number of patients unchanged. The overall performance in scenario D was better than that in scenario A.DiscussionThe original situation at the clinic had been quantifiedusing modelling techniques. A study of the highlighted problematic	specialty-sessions	led	to	a	better	understand-ing of the major causes of the congestion and long patient waiting time problems. The time lag between the registra-tion	start-time	and	consultation	start-time	and	the	number	of	patients	booked	for	each	specialty-session	were	identi-fied	as	the	two	main	causes	which	could	be	modified	for	improvement. Scenarios B, C, and D offer some solutions to address theproblems in the clinic. Scenario B should be implemented with care as it involves the education of and acceptance by patients who have been attending the clinic for many years. Although the redistribution of patients (scenario C)	between	different	floors	involved	fewer	changes	thanscenario B, careful implementation with clear instructions to patients at the centralised registration counter will be necessary.	Scenario	D	offered	the	most	flexible	option	for	application, though the usual limitations of manpowerresources and operational feasibility should still be taken into consideration. In this scenario, we are able to postulate‘what-if’	 questions	 in	 a	 user-friendly	 decision	 support	system for future planning of clinics addressing commonproblems related to physical capacity, availability ofdoctors, congestion, and long patient waiting time.	 As	our	project	finished,	a	new	computerised	appoint-ment	 system	 for	 specialist	 out-patient	 clinics	 was	 intro-duced across all HA hospitals and helped to reduce some of the waiting time between registration and consultation. Regular audits were conducted into clinic performance, facilitating more punctual staff arrival time. Subspecialty clinic sessions were also introduced, so spare capacity in the	 less-congested	 sessions	 could	 be	 better	 utilised.	 As	these few measures managed to reduce patient waiting time and congestion, the results of this study were not put BeforeAfter1 3 5 7 9 11 13 15 17 19 21050100150200250300350400SessionNo. of patientsFig 1. Maximum patient number before and after patient-peak smoothing103 5 7 9 11 13 15 17 19 2120406080100120140160SessionWaiting time (minutes)BeforeAfterFig 2. Mean waiting time before and after patient-peak smoothing10      Hong Kong Med J Vol 12 No 6 Supplement 3 December 2006Ho et alinto practice. The clinic management acknowledged that the modelling framework of the project was ‘conceptually’applied with the implementation of the measures men-tioned above. It was unfortunate that the results of the project were not properly evaluated at the time, as we were unable to verify the impact of the smoothing technique on patient congestion and waiting time. However, the problems of serious congestion and long patient waiting times have recently	 re-appeared	 due	 to	 the	 increasing	workload	 and	physical	space	constraints	in	out-patient	clinics.	The	resultsof this project may be applicable in the current situation with	 minor	 modifications	 and	 these	 findings	 should	 befurther evaluated.AcknowledgementsThis study was supported by the Health Services ResearchFund (#512021). The authors thank Dr SS Lam forresearch assistance.References1. Franz LS, Baker HM, Leong GK, et al. A mathematical model for scheduling	 and	 staffing	multiclinic	 health	 regions.	Eur	 J	Oper	Res	1989;41:277-89.2. Franz LS, Miller JL. Scheduling medical residents to rotations:	 solving	 the	 large-scale	multiperiod	staff	assignment	problem.	Oper	Res	1993;41:269-79.3. O’Keefe RM. Investigating outpatient departments: implementable policies	 and	 qualitative	 approaches.	 J	Oper	Res	 Soc	 1985;36:705-12.4. Chu SC, Ho MP. A modelling framework for the doctor allocation problem	of	a	multi-branch	medical	practice.	Proceedings	of	the	Third	Conference	of	the	Operational	Research	Society	of	Hong	Kong;	1993	May	28;	Hong	Kong;	1993:159-66.5. Ho MP, Chu SC, Wong YL. Simulation modelling for the operation of a comprehensive clinic. Proceedings of the Second Conference of	the	Operational	Research	Society	of	Hong	Kong;	1992	May	27;	Hong	Kong;	1992:130-40.6.	 Romanin-Jacur	G,	Facchin	P.	Optimal	planning	of	a	paediatric	semi-intensive	care	unit	via	simulation.	Eur	J	Oper	Res	1985;29:192-8.

Operational improvement in a specialist out-patient clinic in Hong Kong

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Operational improvement in a specialist out-patient clinic in Hong Kong

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