Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Optimization of Healthcare Delivery System under Uncertainty: Schedule Elective Surgery in an Ambulatory Surgical Center and Schedule Appointment in an Outpatient Clinic

This work investigates two types of scheduling problems in the healthcare industry. One is the elective surgery scheduling problem in an ambulatory center, and the other is the appointment scheduling problem in an outpatient clinic. The ambulatory surgical center is usually equipped with an intake area, several operating rooms (ORs), and a recovery area. The set of surgeries to be scheduled are known in advance. Besides the surgery itself, the sequence-dependent setup time and the surgery recovery are also considered when making the scheduling decision. The scheduling decisions depend on the availability of the ORs, surgeons, and the recovery beds. The objective is to minimize the total cost by making decision in three aspects, number of ORs to open, surgery assignment to ORs, and surgery sequence in each OR. The problem is solved in two steps. In the first step, we propose a constraint programming model and a mixed integer programming model to solve a deterministic version of the problem. In the second step, we consider the variability of the surgery and recovery durations when making scheduling decisions and build a two stage stochastic programming model and solve it by an L-shaped algorithm. The stochastic nature of the outpatient clinic appointment scheduling system, caused by demands, patient arrivals, and service duration, makes it difficult to develop an optimal schedule policy. Once an appointment request is received, decision makers determine whether to accept the appointment and put it into a slot or reject it. Patients may cancel their scheduled appointment or simply not show up. The no-show and cancellation probability of the patients are modeled as the functions of the indirect waiting time of the patients. The performance measure is to maximize the expected net rewards, i.e., the revenue of seeing patients minus the cost of patients\u27 indirect and direct waiting as well as the physician\u27s overtime. We build a Markov Decision Process model and proposed a backward induction algorithm to obtain the optimal policy. The optimal policy is tested on random instances and compared with other heuristic policies. The backward induction algorithm and the heuristic methods are programmed in Matlab

Operating room planning and scheduling: solving a surgical case sequencing problem..

In dit proefschrift bestuderen we planningsproblemen die zich manifesteren in het operatiekwartier van ziekenhuizen. Planning doelt niet alleen op de afstemming van vraag en aanbod, maar duidt ook op het opstellen van een gedetailleerd tijdschema dat aangeeft wanneer activiteiten, in dit geval operaties, zouden moeten starten en wanneer ze zouden moeten worden beëindigd. Aangezien de gevolgen van deze beslissingen voelbaar zijn doorheen het volledige ziekenhuis, lijkt het aangewezen dit beslissingsproces te optimaliseren en enkele technieken uit het domein van het operationeel onderzoek toe te passen en uit te werken. We kunnen de tekst van het proefschrift als volgt opdelen. In Hoofdstuk 1 wordt het belang van de gezondheidszorg in onze hedendaagse maatschappij onderstreept en tonen we aan hoe ziekenhuizen, en in het bijzonder hun operatiekwartier, een vooraanstaande rol spelen in het toedienen van de zorg. Het toenemende belang van dagchirurgie leidt ons ertoe om voornamelijk vanuit dit perspectief de planningsproblemen in het operatiekwartier te benaderen. Hoofdstuk 2 bestudeert op een gedetailleerde en gestructureerde wijze de recente wetenschappelijke literatuur die verschenen is over operatieplanning (verschenen na 2000). Eén van de conclusies die volgen uit dit literatuuronderzoek is het gebrek aan een consistent classifcatieschema om operatieplanningsproblemen te omschrijven. Dit resulteert in ondermeer een ambigu gebruik van terminologie en onduidelijke probleemformuleringen. Daarom wordt in Hoofdstuk 3 een classificatieschema voorgesteld, gebaseerd op de structuur van het literatuuroverzicht van Hoofdstuk 2, met als doel de probleemformuleringen van toekomstig onderzoek binnen dit domein te verduidelijken. In Hoofdstuk 4 bestuderen we de huidige operatieplanningspraktijken van de ziekenhuizen in Vlaanderen (België) en vatten we de antwoorden van 52 respondenten samen die de elektronische vragenlijst hebben ingevuld. De resultaten hebben betrekking op zowel het opstellen van de operatieplanning als de uiteindelijke realisatie van de vooropgestelde planning. Hoofdstuk 5 introduceert een planningsprobleem dat aangereikt werd door de UZ Leuven, het universitair ziekenhuis van de Katholieke Universiteit Leuven. Het probleem bestaat uit het bepalen van de volgorde van operaties in de operatiezalen van het chirurgisch dagcentrum. Naast een gedetailleerde probleemformulering beschrijft het hoofdstuk verschillende procedures om dit dagelijkse operationeel probleem op te lossen en het beslissingsproces van de planner te ondersteunen. Hoewel de meeste procedures gebaseerd zijn op lineaire programmering, wordt ook een branch-and-bound procedure voorgesteld. We maken gebruik van een artificiële testset, gebaseerd op kwantitatieve data en informatie aangereikt door de planner op basis van ervaring, om de rekenkundige capaciteiten van de procedures te testen. Hoewel de algoritmes performant blijken en bijgevolg geoptimaliseerde operatieschema's aanreiken, kunnen er nog verbeteringen aangebracht worden in het gebruiksgemak en de bijhorende flexibiliteit. Daarom wordt in Hoofdstuk 6 een grafische ondersteuning ontwikkeld die het gebruiksgemak wat betreft het invoeren van de gegevens, de interpretatie van de uiteindelijke resultaten en de flexibiliteit inzake het aanpassen van parameters significant verbetert. De grafische component verhoogt de mogelijkheid voor de eindgebruiker van het programma om verschillende operatieschema's te vergelijken en inzichten te verwerven die anders niet kunnen waargenomen worden. Naast de visualisatie zelf rapporteren we in Hoofdstuk 6 ook over de feitelijke toepassing van de applicatie in het chirurgisch dagcentrum van de UZ Leuven Campus Gasthuisberg. Het proefschrift wordt afgerond met Hoofdstuk 7 waarin de belangrijkste bevindingen van de voorgaande hoofdstukken worden samengevat en waarin enkele suggesties worden aangereikt om het onderzoek rond operatieplanning in de toekomst verder te zetten.

Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment

Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital

SHARED TEAM EXPERIENCES AND TEAM EFFECTIVENESS: UNPACKING THE CONTINGENT EFFECTS OF ENTRAINED RHYTHMS AND TASK CHARACTERISTICS

This study explores the conditions under which shared team task-specific (STTS) experiences in crew-based arrangements may negatively influence team effectiveness.We suggest that the entrained rhythms featured in social entrainment theory act as a dual-edged sword with the potential to generate complacency detriments in addition to the commonly cited synchronization benefits. We argue that the manifestation and influence of the countervailing forces (i.e., synchronization and complacency) on the STTS experience—team effectiveness relationship will depend on salient task characteristics (i.e., frequency and difficulty). More specifically, frequently performed tasks create conditions for complacency tomanifest (generating an inverted-U shaped relationship between STTS experience—team efficiency), whereas infrequently performed tasks do not (generating a positive, linear relationship). We further this distinction by layering on task difficulty that, we posit, acts to amplify the respective negative and positive consequences. Analyses of archival data from 8,236 surgeries performed over one year at a large hospital located in the southwestern region of the United States were consistent with our hypotheses and 30 semi-structured interviews with operating room personnel added richness and precision to our theory. Ancillary analyses on patient post-surgery recovery rate yielded additional insights. Implications and future directions are discussed

SHARED TEAM EXPERIENCES AND TEAM EFFECTIVENESS: UNPACKING THE CONTINGENT EFFECTS OF ENTRAINED RHYTHMS AND TASK CHARACTERISTICS

This study explores the conditions under which shared team task-specific (STTS) experiences in crew-based arrangements may negatively influence team effectiveness.We suggest that the entrained rhythms featured in social entrainment theory act as a dual-edged sword with the potential to generate complacency detriments in addition to the commonly cited synchronization benefits. We argue that the manifestation and influence of the countervailing forces (i.e., synchronization and complacency) on the STTS experience—team effectiveness relationship will depend on salient task characteristics (i.e., frequency and difficulty). More specifically, frequently performed tasks create conditions for complacency tomanifest (generating an inverted-U shaped relationship between STTS experience—team efficiency), whereas infrequently performed tasks do not (generating a positive, linear relationship). We further this distinction by layering on task difficulty that, we posit, acts to amplify the respective negative and positive consequences. Analyses of archival data from 8,236 surgeries performed over one year at a large hospital located in the southwestern region of the United States were consistent with our hypotheses and 30 semi-structured interviews with operating room personnel added richness and precision to our theory. Ancillary analyses on patient post-surgery recovery rate yielded additional insights. Implications and future directions are discussed