An exact approach for relating recovering surgical patient workload to the master surgical schedule

Abstract No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically the ward occupancy distributions, patient admission/discharge distributions, and the distributions for ongoing interventions/treatments is computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room

A review on the relation between simulation and improvement in hospitals

<p>Abstract</p> <p>Background</p> <p>Simulation applications on operations management in hospitals are frequently published and claim to support decision-making on operations management subjects. However, the reported implementation rates of recommendations are low and the actual impact of the changes recommended by the modeler has hardly been examined. This paper examines: 1) the execution rate of simulation study recommendations, 2) the research methods used to evaluate implementation of recommendations, 3) factors contributing to implementation, and 4) the differences regarding implementation between literature and practice.</p> <p>Results</p> <p>Altogether 16 hospitals executed the recommendations (at least partially). Implementation results were hardly reported upon; 1 study described a before-and-after design, 2 a partial before and after design. Factors that help implementation were grouped according to 1) technical quality, of which data availability, validation/verification with historic data/expert opinion, and the development of the conceptual model were mentioned most frequently 2) process quality, with client involvement and 3) outcome quality with, presentation of results. The survey response rate of traceable authors was 61%, 18 authors implemented the results at least partially. Among these responses, evaluation methods were relatively better with 3 time series designs and 2 before-and-after designs.</p> <p>Conclusions</p> <p>Although underreported in literature, implementation of recommendations seems limited; this review provides recommendations on project design, implementation conditions and evaluation methods to increase implementation.</p> <p>Methods</p> <p>A literature review in PubMed and Business Source Elite on stochastic simulation applications on operations management in individual hospitals published between 1997 and 2008. From those reporting implementation, cross references were added. In total, 89 papers were included. A scoring list was used for data extraction. Two reviewers evaluated each paper separately; in case of discrepancies, they jointly determined the scores. The findings were validated with a survey to the original authors.</p

Suitability and managerial implications of a Master Surgical Scheduling approach

Abstract: Operating room (OR) planning and scheduling is a popular and challenging subject within the operational research applied to health services research (ORAHS). However, the impact in practice is very limited. The organization and culture of a hospital and the inherent characteristics of its processes impose specific implementation issues that affect the success of planning approaches. Current tactical OR planning approaches often fail to account for these issues.Master surgical scheduling (MSS) is a promising approach for hospitals to optimize resource utilization and patient flows. We discuss the pros and cons of MSS and compare MSS with centralized and decentralized planning approaches. Finally, we address various implementation issues of MSS and discuss its suitability for hospitals with different organizational foci and culture

Development of a wait list computer simulation model for elective orthopaedic surgery

Data from the wait list management system and hospital databases was used to develop a computer model simulating the resource requirements required during patient flow into, through, and out of orthopaedic surgery for TKR, THR and knee arthroscopy. Results from the simulation model suggested that inpatient beds, rather than operating room time was the constraining resource and an extra twenty-five beds and 30% more OR time would stabilize and subsequently reduce the wait time at the institution. In addition, simulations suggested that pooling surgeon wait lists reduced patient wait time. Simulation models are an effective resource allocation decision-making tool for orthopaedic surgery. \ud To develop and implement a wait list simulation model to analyze the existing system and guide resource allocation decision-making at the QEII Health Sciences Centre. \ud The simulation model suggests an immediate increase in inpatient surgical beds from sixty-six to ninety-one followed by a 30% increase in OR time in thirty months to stabilize and subsequently reduce patient wait times. \u