Bierlaire, Michel

Garnerin, Philippe

Osorio, Carolina

Perez, Pao

Weibel, Christophe

Infoscience - École polytechnique fédérale de Lausanne

33SMI 2006: Nº 58Summary Patient safety in operative and postoperativestructures is closely linked to adequate resourceallocation and patient flow organisation. Newpolicies concerning working hours or resourceallocation are designed to improve patientsafety. However, it is necessary to measure theirimpact before taking action. In this paper wedescribe a simulation tool which makes it pos-sible to measure the impact of various organisa-tional policies in a hospital operating theatreand postoperative structures. We illustrate itsuse by an example assessing the impact of a newtraining policy for operating room personnelupon recovery room utilisation and   the atten-dant staffing requirements at the Geneva Uni-versity Hospitals.IntroductionThe set of hospital operative and postoperativestructures (hereafter denoted OPS) constitutes a complex system. Unorganised patient flowsthrough  these structures are a potential cause ofinappropriate bed utilisation or room overcrowd-ing and thus, occasionally, of adverse events. Patient mismanagement may be due to improperresource allocation or communication failures[1]. Improving the organisation of the OPS, the most costly hospital structure [2], can beachieved, for example, by better management ofits resources [3]. Nevertheless, when consideringOPS resource reallocation it is necessary to eval-uate the impact of a policy of this kind before-hand. As on-field trials can rarely be carried out,computer simulation plays a key role in illustrat-ing and quantifying these effects. OPS perform-ance depends heavily on the list of patients andtheir scheduling [4]. Designing an efficientorganisational structure constitutes a complextask due to the large number of individuals (e.g. patients, surgeons, anaesthetists, nurses),their often contradictory objectives, and clinical,technical and time constraints [5]. Additionally,many unpredictable events arise. They fall intotwo main categories: resource unavailability (e.g. unannounced staff absence), and variationof event duration (e.g. an intervention lastslonger than expected). Due to the already com-plex constraints imposed on weekly OPS or-ganization, these unpredictable events are rarelytaken into account. When considering a newpolicy it is nevertheless important to acknowl-edge their existence and evaluate their impactupon the OPS’s overall performance. In thispaper we describe a simulation tool which hasbeen designed and developed by the HôpitauxUniverstaires de Genève (HUG; Geneva Univer-sity Hospitals) and the Ecole PolytechniqueFédérale de Lausanne (EPFL) [6–8]. The overalldesign of the simulator is first of all presented,followed by an example illustrating the impact ofa new training policy for operating room person-nel on the utilisation of the recovery room andthe attendant staffing requirements.Simulator designAlthough the design is fairly general, the simula-tor has been tailored to the specific case of theHUG. The main OPS are shown in figure 1. A set of events takes place in each of these struc-tures, such as room/patient preparation, anaes-thesia induction, patient emergence, admission,discharge and room closing. These events deter-mine the functioning of the OPS. Each of theseevents involves a set of actors constituting theresources required for the event to take place,e.g. rooms, beds, surgeons, nurses, anaesthetists,patients. The simulation of the OPS was initially designedusing preexisting generic simulation software,which unfortunately did not allow integration ofkey OPS working rules. We decided to develop anew simulator which would fully acknowledgethe complexity of the OPS system and adapt tothe specific needs of the HUG. The simulatorwas developed in Java. It takes as input an XMLfile containing a set of actors (e.g. electivepatients, surgeons, rooms, beds), the events theyare involved in, and their working constraints(e.g. room opening hours, personnel availabil-ity). The simulator then reproduces a typicalweek including the occurrence of unexpectedevents such as surgical delay or actor/roomunavailability. The output consists of a sample ofhow the set of patients have interacted with theavailable resources, e.g. which interventionsPatient flow simulation as a tool for estimating policy impact C. Osorioa, C. Weibela, P. Perezb, M. Bierlairea,Ph. Garnerinba Chaire de recherche opérationnelleRoso, Institut de Mathématiques,Ecole Polytechnique Fédérale deLausanneb Service d’anesthésiologie, Hôpitaux Universitaires de GenèveCorrespondence:Carolina OsorioEPFL SB IMA ROSOMA B1 517 (Bâtiment MA)Station 8CH-1015 Lausannecarolina.osoriopizano@epfl.chPatient safety34SMI 2006: Nº 58lasted longer than expected and thus delayed/cancelled other interventions. The informationconcerning changes in interventions is tabulatedfor further analysis. To make the interface moreuser-friendly a graphic output highlighting theunexpected events can also be obtained. By run-ning numerous simulations with the same set ofpatients and resource constraints it is possible toestimate OPS performance, e.g. the averagenumber of cancelled/delayed interventions, aver-age resource utilisation/idle time. The simulatorcan thus be used to evaluate the impact of longterm policy changes such as personnel and med-ical equipment allocation (e.g. working/openinghours) or new surgical policies (e.g. schedulingcriteria). Its output can also be used to measurequantitatively the impact of uncertainty uponthe current patient and resource configuration. The simulator is based on an object-orienteddesign composed of the following “objects”. Theevents describe the dynamic aspect of the OPS.Each event can be classified into a categorydenoted as type of event, e.g. closing a room,transferring a patient, surgical intervention. Anevent is fully determined by its type and expectedstarting time, while other details such as expectedduration can also be set. Each event involves aset of physical resources denoted as actors. Onlyscarce or constrained resources that have animpact on the dynamic behaviour of the systemare considered (e.g. patient, personnel, equip-ment). Certain events need to be carried out in agiven order (e.g. patient is transferred to OPSthen prepared for intervention). Such a set ofevents is denoted as an activity. Each actor has itsown agenda, which consists of a set of activities.The interactions between the different objectsare illustrated in figure 2. The events are organ-ised in a priority queue andthe simulatorprocesses them iteratively. Each type of event is associated with a set ofrules; each rule consists of a set of conditions andactions. As an example of the event type “surgicaloperation” (or intervention) a set of conditionscould be: “if an actor is late” and “if the inter-vention can no longer be delayed”, the corre-sponding action could be “cancel the interven-tion”. The advantages of defining the logic of thesimulator by subdividing the rules into condi-tions and actions allows incremental coding(rules are added progressively) and the combina-tion of a large number of conditions and actions,thus rendering the creation of rules flexible. Theconditions include the following: are all precedingevents completed? Can the event be delayed?Can the event be cancelled? Is an actor availablefor the proposed time slot? Does the event inter-fere with a higher priority event? The actionsinclude the following: start an event, defer anevent. The rules are combined into a decisiontree and tested accordingly.To estimate the parameters of the simulator, astatistical analysis of HUG data was carried out.The dataset consisted of 40 weeks of OPS inter-ventions. For each intervention we know themedical speciality, the type of anaesthesiarequired, the date, time and duration, and thewaiting time, time of arrival and length of stay in RR/ICU. Distributions characterising bothelective and emergency patient flows were fittedand appropriate statistical tests carried out toevaluate the convenience of this fitting. Empiri-cal distributions were used for the parameterswhose fitting was not satisfactory. Specific rulesPresence of an actor An event needs a pre-specified set of actors inorder to take place. The presence of all actorsmay not be mandatory, e.g. the event “patientemergence” starts whether or not a bed is avail-able in the RR, i.e. the patient may wake upwhile waiting in the operating room. To take thisFigure 1. Main operating and postoperative patient flows.Upon admission to an operating theatre patients proceeed to an operating room whereanesthesia induction, intervention and anesthesia emergence take place. They thenproceed to their corresponding postoperative structure.At the HUG there are two operating theaters. One is for elective cases, the other foremergency patients. There are two postoperative structures: the recovery room (RR) andthe intensive care unit (ICU), these are shared by elective and emergency patients.Figure 2. Interaction between the main “objects”.35SMI 2006: Nº 58into account each actor contributes a certainamount to the achievement of a given event. Anevent can take place if the total contribution (the sum of contributions over all participatingactors) exceeds a tolerance threshold. It is alsopossible to decide that an event requires only apartial contribution from an actor, thus allowingthe actor to work simultaneously on severalevents. This allows, for example, the same anaes-thetist to work on simultaneous interventions.Time constraints A delicate and complex issue in the simulator isthe rescheduling of events so that all resourceand actor constraints remain satisfied. Delayingor cancelling events brings additional constraints.Events which cannot be arbitrarily delayed areassociated with a latest feasible time. To accountfor the relative importance of the differentevents, a priority is assigned to each one. Whenseveral events are candidates for a time slot, theevent with higher priority prevails. This allows,for example, an operating room to close later ifan intervention lasts longer than expected, butdoes not allow an intervention to start if it isexpected to end after closing hours. These eventpriorities also allow us to differentiate betweenevents that can be delayed and cancelled (e.g. anintervention), and those that can be delayed butnot cancelled (e.g. closing a room). ExampleKeeping personnel training and qualificationsuptodate plays a key role in patient safety.Courses are often scheduled according to person-nel position (e.g. one course is scheduled for allanaesthetists), leading to events that cannot takeplace due to the absence of some actors: thesurgeon cannot work without the collaborationof an anaesthetist or vice versa. It is thereforenecessary to find a way to simultaneously sched-ule courses across all medical positions. Anappealing idea is to completely suspend electiveinterventions once a week, thus allowing coursesto be scheduled on the same day for all staffmembers. We will now estimate the impact of suspendingelective surgery on Wednesday and increasingsurgery working time by 2 hours on the otherweekdays.In this simulation the structures of interest arethe elective and emergency operating rooms, theRR and a virtual structure denoted as “otherpostoperative structure” representing both theICU and wards. Data consist of patients thatvisited any of these four structures during 2004,i.e. approximately 4450 elective and 3850 emer-gency interventions. Seven different events werespecified: four types of intervention (defined onthe basis of the sequence of rooms visited by thepatients), two events for stay in the RR, and onedenoting room closing. Each event was assigneda starting time and duration. One hundred simulations were carried out foreach scenario (Wednesdays off or not). Figure 3yields the utilisation of the RR under the twosimulated scenarios. It was verified that the sim-ulated utilisation within current opening hours isconsistent with the observed utilisation. The topplot of Figure 3 shows that if elective operationswere to be suspended on Wednesdays the RRwould have on average a higher occupancy after4pm and until 8am the following day. The bot-tom plot illustrates that in order to satisfy 100%of the demand, three extra beds (including corre-sponding staff ) would be required. Such a policyFigure 3. Weekday recovery room occupancy.The number of occupied beds is plotted versus the time in hours starting from Mondayat midnight until Friday 11pm. The top figure displays the mean occupancy, thebottom shows the maximum occupancy. The solid line corresponds to the simulatedoccupancy given the current policy and the dashed line in the simulated occupancyunder the new “Wednesdays off ” policy.Patient safety36SMI 2006: Nº 58change therefore has a strong impact upon RRoccupancy. Anticipating this impact would bene-fit patient safety.DiscussionThis tool allows staff at HUG to obtain perform-ance measures of the service level of their OPS.The common resources used for a variety ofevents justify an object-oriented analysis. Theinput and output interfaces are user-friendly,thus allowing hospital staff to interact with thetool. Calibration of the model was feasiblethanks to the extensive and detailed data pro-vided by the HUG. The tool was adapted to thehospitals’ needs  by implementing their specificrules. The use of the simulator was illustrated byestimating the impact of a new personnel course-scheduling policy upon RR utilsation and itsstaffing requirements. Accurate estimation of policy changes is data-expensive. In the case of the HUG their up-to-date data retrieval system yields detailed andextensive information. Unfortunately this is notthe case of most healthcare institutions, whichlimits the use of such tools in other facilities. Onthe other hand simulation makes it possible toidentify the main deficits of  existing data, thuspushing the main IT systems to evolve. This tool considers patient-, surgeon- and room-specific information, thus mimicking the behav-ior of the OPS and grasping its complexity. Theperformance measures obtained are realistic andrich in detail. Nevertheless, studying separatemodules does not allow system-wide interactionsto be evaluated. This motivates the need formodelling at a less detailed and more aggregatelevel, which yields less detailed performancemeasures  but acknowledges the interactionsbetween the different hospital structures. This isthe aim of the current HUG – EPFL collabora-tion.The authors would like to thank Emma Frejingerand Olivier Gallay for their contribution to this project. References1 Sutcliffe KM, Lewton E and Rosenthal MM. Communication Failures: An Insidious Contributor toMedical Mishaps. Acad Med 2004;79:186–94.2 Macario A, Vitez T, Dunn B, et al. Where are the costs in perioperative care? Analysis of hospital costsand charges for inpatient surgical care. Anesthesiology 1995;83:1138–44.3 Cendan JC, Good M. Interdisciplinary work flow assessment and redesign decreases operating roomturnover time and allows for additional caseload. Arch Surg 2006;141:65–9.4 Jun JB, Jacobson SH, Swisher JR. Application of discrete-event simulation in health care clinics: A survey. Journal of the Operational Research Society 1999;50:109–23.5 Oddi A, Cesta A. Toward interactive scheduling systems for managing medical resources. ArtificialIntelligence in Medicine 2000;20:113–38.6 Weibel C, Bierlaire M. Simulation du bloc opératoire et salle de réveil: analyse statistique. Rapportinterne 2002.7 Frejinger E. Simulateur pour les hôpitaux de Genève. Rapport de projet de semestre, 2003.8 Gallay O. Simulateur pour les hôpitaux de Genève. Rapport de projet de semestre, 2003. 

Patient flow simulation as a tool for estimating policy impact

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Patient flow simulation as a tool for estimating policy impact

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