Proceedings of the 2007 INFORMS Simulation Society Research Workshop EXTENDED ABSTRACT 1INTRODUCTION Mostsimulationstudiesinthehealthcaredomainare aimed at solving specific problems in particular health care facilities.Thecommonalitiesinthesestudiesaretheirob-jectivesofimprovingperformancesuchasreducingwait-ing times or using resources more efficiently.Because they are specific, these studies guide a modeller how a specific problem can be solved. In this paper, we discuss thedevelopment of a generic wholehospitalsimulationmodelthatcanbeusedbygen-eralhospitalsinEnglandtounderstandhowwaitingtime performanceisaffectedbyvariousfactors.TheNational HealthService(NHS)intheUKhasaperformanceas-sessment frameworkthatincludeswaitingtimetargetsfor variousstagesofhealthcareprovisioninhospitals. We presentinitialoutcomesofaproject,DistrictGeneralHos-pitalPerformanceSimulation(DGHPSim)whichaimsto build generic hospital models with sufficient detailforthis purpose.Here,thetermGenericmeansthatthemodel hasadefinedstructurewithprobabilitydistributionsthat can be parameterized by the user.2RELATED LITERATURE The literature has very few examples of generic simulation models in health care. Most papers focus on specific parts of hospitals,especiallyA&Edepartmentsandout -patient clinics. Fletcher et al (2006) discusses the development of agenericA&Edepartmentsimulationmodelforuseby variousA&EsinEngland,pointingoutthedifficultyofgenericmodelling.SinreichandMarmor(2004)attempt somegeneralisationsoffourEmergencyDepart mentsin Israel. They used sophisticated mathematical techniques to findoutsimilaritiesoftheprocessesinthesedepartments andsuggestametric :thesimi laritymeasure.Pauland Kuljis(1995)presentsagenericoutpatientclinicsmodel, CLINSIM, that was built for UK Depart mentofHealth.It simulates how operating policy can influence patient wait-ing times. The model has been used successfully by a num-ber of clinics in UK, though is not in current use, probably because of the difficulties in providing support to new us-ers. Another example of generic modelling in healthcare is Proudlove et al (2007), which reports a simple Excel based simulationtooltospecificallyassesstheeffectofDayof Week(DoW)andTimeofDay(ToD)changesinemer-gencyadmissiononin-patientoccupancy.Themodelis based on data fromtwo hospitalsthat was used to analyze DoW and ToD patterns of occupancy. Pitt (1997) is one of theveryfewexamplesofgenericwholehospitalsimula-tions.Heanticipatedthreenotionstogeneralizehospital simulations;asimulationengine support edbyamodeling methodology to develop models of hospital units, a gener-alized user interface to display and control key parameters ofasimulationmodel,andadatabasetopopulatemodels and to link the interface with the model. This study specifi-cally focuses on interactivity of models with users.Oneveryrecentstudyinthisfieldisreportedin FletcherandWorthington(2007),whichsurveys20ex-perts in the field and 350 papers in the literature to identify differences between generic and specific modelsin health-care. They suggest four levels of models according to their genericity based on their abstraction and transportability. generic principle models,generic framework models,setting-specific generic models,setting specific models.Their literature survey revealed that no clear design differ-encesandsurprisingamountofsimilaritiesexistbetween generic and specific models. Inasense,thetechnicalissuestobefacedinthede-velopmentofgenericmodelsfortheimprovementin healthcaresystemsisnodifferentfromthatinotherdo-mainssuchasmanufacturing.Pidd(1992a,1992b)dis-cussessomeoftheseissues,thoughusingearliergenera-tionsofcomputersoftware.Theseissuesalsorelateto MOVING FROM SPECIFIC TO GENERIC: GENERIC MODELLING INHEALTH CARE All generalizations are dangerous, even this one.Alexandre Dumas (1802-1870) Murat M. Gnal Michael Pidd Department of Management Science Lancaster University Management School Lancaster University LA1 4YX, United Kingdom Gnal and Pidd those faced in almost any modeling exercise in OR/MS, as discussed in Pidd (2004) and in Powell (1995). 3WHOLE HOSPITAL MODELLING 3.1Understanding performance targets in healthcare Thedemandforhealthcareseemstobeinsatiableand healthcareprovisionisrationedinallcountries.When healthcare is provided by the free market, it is rationed by price the poor and uninsured get less and possibly worse healthcarethantherichortheinsured.Inothers,particu-larlywhenprovidedthroughtaxation,healthcareisra-tioned by limiting capacity, leading to waits for healt hcare than can sometimes be rather long. In the UK, most health-care is provided by the National Health Service (NHS) and is free at the point of need, though financed through taxa-tion.Asiswidelyknow,muchinpatientNHScareisra-tionedbywaitingliststhathadgrownmuchtoolongby the mid-1990s.TheincomingLabourgovernmentin1997 made it a priority to reduce these waiting times and waiting listsandinstitutedapolicybasedontargets.Healthcare providersthatmetthesetargetswererewardedandthose who failed to do so were penalised. TheongoingDGHPSimprojectisfundedbyEPSRC andaimstounderstandwhatactionhealthcareproviders must take to meet these targets and also to appreciate what, if any, distortions, these targets introduce. The basic idea is to develop a generic whole -hospitalsimulationmodelthat could bepopulated by data specific to a particular hospital toinvestigatethetargetsandtheireffectsinthathospital. Hospitals are very complicated institutions and so the ques-tionofconceptualmodellingloomslargeinthisresearch. However, unlike the police work discussed earlier, there is noclearclientgroupwithwhomtoworktodevelop this conceptualisation.Insuchasituation,itisi mportantto generate a group of committed stakeholders who can act as a proxy for a client group. Perhaps inevitably, such stake-holders may wish to use such a model to answer different questions that suit their own interests.Atitssimplest,ageneralhospitalisaninput:output systematwhichpatientsarrive,someaccordingtoanad-missions schedule and others asemergencies. At the hospi-tal, they receive treatment and remain there, consuming re-sourcesuntildischargeor,sometimes,death.However, thingsareofcoursemuchmorecomplicatedinpractice, sincethehospitalwilloffermultiplespecialtiesandtreat-ments.Viewedasaninput:outputsystem,waitingtimes canbereducedeitherbydemandmanagement(diverting patients to other healthcare providers), adding capacity, or increasingthroughputbyusingexistingcapacitymoreef-fectively. If the effect of government targets on hospitals is tobeassessed,whatformofconceptualmodelisneeded. In this, two basic questions loom large. What elements should be included in the model and at what level of detail? A hospital, or even a single clinic, could be modelled in great detail by trying to represent allstaff,everythingthatthesestaffdo,allequipment andallitsuses,andallpatientsandthatisdoneto them.However,suchamodelwouldrunextremely slowly and would take a huge resource to develop. By thetimethatitwasfinished,itislikelythatthereal world will have moved on.That is,the conceptualis a-tionneedstobedoneinthelightoftheexperimental frame and the available data. Whatrelationshipsbetweenelementsshouldbei n-cluded in the model? In a congestedsystem with con-strainedresources,bothofwhichapplytomostgen-eralhospitals,thereareboundtobeimportant relationshipsbetweensystemelements.Aswiththe questionoftheinclusionandexclusionofelements, the decision about which relationships to include is not straightforward. For exa mple, many hospitals have an AccidentandEmergency(A&E)Departmentthatof-fers immediate treatment and also serves as a gate-way for emergency admissions. One important relationship to be considered is the link between A&E activity and thoseadmissions.However,itwouldbeamistaketo assume that this requires a detailed model of A&E ac-tivities;itmaybeenoughtoknowthatx%ofA&E demand results in inpatient admissions. 3.2Model concept ualization in DGHPSim DGHPSimisa research project that aims to develop a ge-neric whole hospital simulation model for use in examining theperformanceregimeintheUKNationalHealthSer-vice. Much of the effort in this research project has centred around a search forthe appropriate level of abstraction.At thehighestlevel,ahospitalcanbesimplifiedasaninput-outputsystem.Howevertheinputsmustbeelaboratedbe-causethemainstreamsofpatients,emergencyandelec-tive, follow complex stages before being admitted.A high level conceptualisation of a hospital is presented in Figure 1.EachrectangleinFigure1representsasubmodelwhich can run independently orwith the others. Clouds represent stochasticpatientdemand.Apatientcanbeadmitted to a hospitalaseitheranemergencyoranelective.Emergen-ciesarriveeitherthroughanA&Edepartmentordirect fromaGeneralPractitioners(GP)referral.GPscanalso refer patients to a specialist clinic as out-patient. Some out-patientsneedsurgery,whichrequiresanadmissioneither as day-case or non-day-case.The NHS Performance measurement framework meas-uresthedelaysthatoccurwithinandafterA&Edepart-ments,andbeforeandafterout-patientclinics.Therefore werequiredsubmodelsofA&EandOut-patientClinics and must elaborate the inputs to the hospital. The inpatient Gnal and Pidd (Hospital)model,ismainlyconcernedwithbedmanage-ment,whichisimportantbecauseemergencyandelective patients consume beds from the same pool, although some hospitalskeeptheirelectivebedsseparate.Variabilityin length of stay (LoS) makes bed planning complicated.WeworkedwithapilothospitalinEnglandandbuilt the first sub-model for its A&E department. The inputs are numberofpatientsexpectedbythedepartment,service time distribution parameters of doctors and nurses, bed ca-pacity parameters, and arrival patterns by day of week and time of day. In this work, one of our findings is the mult i-taskingbehaviourofmedicalstaff, which we represent us-ingatechniquewhichwecallmini-doctors.Thistech-niqueisespeciallyusefulwhendetaileddatafordoctors activities are not available (Gunal and Pidd, 2006). Thesecondsub-model,foroutpatients,uses the planneddemandwhichisassumedtobemorepredictive thantheemergencydemand.Outpatientclinicsarethe main sources of elective patients. This model takes patient pathwaysfromprimarycarestageintotheout-patient stage. Patients may require more than one clinic and some patients requireadmission.Havingaconstrainedmodelis problematic and we faced some challenges in generalizing processes.Thethirdsub-modelintendstocapturethecomplex natureofbedmanagementwithinahospitalandflowsof patientsfromandtopatientwards.DifferentORtech-niqueshavebeenusedtopredictbedoccupancy.We choose a conventional approach using routing probabilities and Ward Transition Matrices. We use stationary length ofstaydistributionsforpatientsdurationonwards. For longtaildistributionssuchasforIntensiveCareUnit (ICU) and for elderly wards, we use hyper-exponential dis-tributions.Forthevalidationofthesub-models,weadopteda black-boxapproachandcomparedmodeloutputswith hospitalperformancefigures.Weobservedgoodfits mainly because we estimated LoS distributions parameters, transitionprobabilities,anddemandpatternsbyusingthe hospitals information system.4GENERIC PROCESSES AND PARAMETERS In the DGHPSim project we considered two approaches to generalize our models; first, identifying common processes for the three sub-models , and secondly, the use ofcommon types of parameters to these processes.Thefirstsub-modelreflectstheprocessesinatypical A&E. TheHealthcareCommission(2005) revealsthat pa-tients in A&E follow a sequence of processes such as arri-val,registration,initialassessment(ortriage),branching for tests, second assessment on decision, and discharge. In a simulation model, types of parameters of these processes canalsobegeneralized:forexample,hourlyarrivalrates for ambulance and walk-in patients, process times and staff needs of triage, process times and staff needs ofinitial as-sessment,branchingprobabilitiestotestsetc.Obviously, the parameter values of these processes change in different A&Esandhenceproducingdifferentperformances.This sub-modelisdiscussedinmoredetailinGunalandPidd (2006). The second sub-model (outpatients) is harder to gener-alize fortwomainreasons.First,thetypesofclinicsof-feredtopatientsvariesineachspecialty.Themaincon-straintinanoutpatientsystemistheclinicappointments whichareofferedbyspecialists.Everyspecialisthas his/herownscheduleinwhichsomeoftheirtimeisdedi-catedtoout-patientclinics.Atypicalcliniclasts3anda half hours, and patients are assigned to slots according to a bookingmechanism.Therearethreetypesofappoint-ments;Firsttimereferrals,follow-upsbeforeoperation, andfollow-upsafteroperation.Somespecialtiescanhave other types of clinic appointments regarding specific types of treatment, such as fracture clinics for emergency ortho-pedic patients, special ointment clinics for dermatology pa-tients, audio-test clinics for ENT patients etc. The timeal-locatedforeachtypeofclinicvariesandthereforethe number of patients that can be seen also varies.In addition, consultants prioritize different types of patients differently.Thesecondproblemingeneralizingout-patientproc-essesisthatconsultantsgenerallykeeptheirownwaiting lists. For one specialty of one hospital, this may be possible tomodel.Howeverthinkingforotherspecialtiesanda wholehospital,addingthisdetailincreasesthelevelof complexity dramatically. Hence we are pursuing asimpli-fiedrepresentationofindividualconsultantlistsinour model.Thesetwoproblemswhenmodelingoutpatient clinicsandtheireffectsontherestofahospitalsuggests that a family of generic models may be needed, these being (tosomeextent)specialtyspecific.Theapproachestaken inmodelssuchasCLINSIM(PaulandKuljis,1995), whichfocusonlyonpatientwaitingtimesataclinic,will notworkforthetypesofmodelneededintheDGHPSim project.The third sub-model, the in-patient model, in a sense is theeasiesttogeneralizeitsprocesses.Inessenceitisasimpleinput-outputsystem;patientsareadmittedtoa ward,staythereforsomerandomtimeandthentheyare discharged.Inaddition,somepatientsmaybetransferred betweenwardsduringtheirstay.Correctclassification of patient is crucial in determining the duration of stay of pa-tients and the routing probabilities between wards.Routing probabilitiesforNHShospitalsintheUKcanbederived fromtheHealthEpisodeStatistics(HES)Data that is col-lected for all hospitals and includes data on each and every inpatient episode for each year. Using this, we classify pa-tientsbyspecialty, thetoplevelclassificationandderive routing probabilities. There are, of course,lower level clas-sificationsthanspecialty,suchaspatientswhorequirea Gnal and Pidd specific type of operation or treatment, but this may not be necessary in the DGHPSim models. BoththeA&Esub-modelsandoutpatientsub-models generate patients who may be admitted as inpatients . How-ever, it may not be necessary to have a detailed, patient-by-patient simulation of A&E. Instead, it may be just as useful to generate inpatient admissions from A&E by a sampling processifthearrivalratesandprobabilitiesofadmission from A&E areknown. However, it is necessary to simulate outpatient interactions on an individual patient basis, since theNHSPerformanceFrameworkincludesmaximumde-lay targets from referral to outpatients through to inpatient admission, if needed. 5MODEL USE AND PARAMETERIZATION Themodelsareintendedfor use in operational and policy levelanalysis ,toallowtheidentificationofpossiblebot-tlenecksinahospital.Atpolicylevel,themodelscanbe run for different hospitals , hence the need forgenericfea-tures.The genericmodelsaremadespecificbyproviding parameter values.Asmentionedearlier,theNHSroutinelycollects data fromhospitalseveryyearandpublishestheseastheHES data.HESisactuallytwoverydetaileddatabaseswhich include inpatient and outpatient episodes of all patients that ahospitalhasseenduringtheyear.Thesetwodatabases includealmosteverydetailforahospitalforourmodels. Wecanworkout,usingthesedatabases,LoS,transition probabilities, arrival volumes and patterns, number of out-patientclinicsvisitedbeforeanadmission,numberofre-ferralsbyspecialtyetc.Asyet,thesedatasetsdonotin-cludephysicalbedusageanddiagnostictests.Although it isnotourmaininterest,detailedinformationofA&Epa-tients are also not in HES. Although HES databases tell us about the past, we can investigatepossibleproblemareasofahospitalandways of improving them. We can investigate if the problems are relatedwiththelackofcapacityand/orthelackofgood managementofthecapacity.Experimentationwiththese modelswillalsorevealthatiftheperformancetargetsset bytheNHSarefeasibleforthehospitalsinEnglandwith their current capacity. ACKNOWLEDGEMENTS The DGHPSim project is funded by the UKs Engineering andPhysicalSciencesResearchCouncilundergrantref EP/C010752/1.CollaboratorsareGwynBevanandAlec Morton (London School of Economics) and Peter C. Smith (University of York). We are grateful for the co-operation theUKDepartmentofHealthandfromtwoNHSTrusts: theUniversityHospitalsofMorecambeBayandtheSal-ford Royal Hospitals. REFERENCES Fletcher A and D.Worthington (2007) What is a Generic HospitalModel?LancasterUniversityManagement School Working Paper 2007/03. Fletcher A,D.Halsall, S. Huxham, D. Worthington (2006) TheDHAccidentandEmergencyDepartmentmodel a national generic model used locally. Journal of the OperationalResearchSociety,availableasadvance onlinepublication,December6,2006; doi:10.1057/palgrave.jors.2602344. GunalM.M.andPiddM(2006)Understandingaccident and emergency department performance using simula-tion. Proceedings of the 2006 Winter Simulation Con-ferenceL.F.Perrone,F.P.Wieland,J.Liu,B.G. Lawson, D. M. Nicol, and R. M. Fuji moto, eds. HealthcareCommission(2005)AcuteHospitalPortfolio Review: Accident and Emergency. Paul R J and Kuljis J (1995) A generic simulation package for organising outpatient clinics, In the Proceedings of the1995WinterSimulationConference,3-6Decem-ber1998,Arlington,Virginia.AssociationforCo m-puting Machinery, Baltimore. pp 1043-1047. PiddM.(1992a)SKIM:asimulationsketchpadforplant design. Jnl Opl Res Soc., 43, 12, 1121-1133. PiddM.(1992b)Principlesofdatadrivengenericsimula-tion models. Simulation Jnl., 59, 4, 237-243. PiddM.(2004)Toolsforthinking:modellinginmanage-mentscience.(Secondedition)JohnWiley&Sons Ltd, Chichester, 319. Pitt M.(1997) A Generalised Simulation System to Support StrategicResourcePlanninginHealthcare.Proceed-ingsofthe1997WinterSimulationConference.Eds. S.Andradottir,K.J.Healy,D.H.Withers,and B.L.Nelson.Powell S. G. (1995) The teachers forum: six key modelingheuristics. Interfaces 25, 4, 114-125. SinreichD.,MarmorY.N.(2004)ASimpleandIntuitive SimulationToolforAnalyzingEmergencyDepart-ment Operations. Proceedings of 2004 Winter Simula-tion Conference. AUTHOR BIOGRAPHIES MURATGUNALispursuinghisPhDatLancasterUni-versity. He received his MSc degree from the sameuniver-sity in 2000.He is an experienced simulation modeller and hiscurrentresearchinterestistoinvestigatetheuseof simulation on performance measurement in public services such as hospitals. He worked as a simulation analyst in the Turkish Navy where he was interested in military applica-tionsofoperationalresearch.Emailto . Gnal and Pidd MIKE PIDD is Professor of Management Science at Lan-caster University where his work spans simulation model-ling and the development of improved simulation methods utilising current developments in computing hardware and software.Hiscurrentapplicationworkfocusesonsimula-tionmodellingforimprovementinpublicservices,espe-ciallyinpolicingandhealthcare.Heisknownforthree books: Computer simulationinmanagement science (in its 5th edition), Tools for thinking: modellinginmanagement science (in its 2nd edition) and Systems modelling: theory andpracticeallpublishedbyJohnWiley. . Figure 1: High level representation of hospital processes.