Surgical unit time utilization review: resource utilization and management implications
Journal of Medical Systems
Using Markov Models to Assess the Performance of a Health and Community Care System
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Modeling emergency departments using discrete event simulation techniques
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation model for improving the operation of the emergency department of special health care
Proceedings of the 38th conference on Winter simulation
Journal of Medical Systems
Enabling ubiquitous patient monitoring: Model, decision protocols, opportunities and challenges
Decision Support Systems
A planning and scheduling problem for an operating theatre using an open scheduling strategy
Computers and Industrial Engineering
Operating theatre scheduling with patient recovery in both operating rooms and recovery beds
Computers and Industrial Engineering
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Over the last few decades, rational health care management and, in particular, operating theater planning, has attracted increased attention from practitioners and from the scientific community. However, although the operating theater environment is clearly stochastic, the impact of this randomness has often been ignored. In practice, simple rules based largely on past experience (such as keeping a safety margin), are most frequently used when making plans for the operating theater. In this paper, we propose an approach to help rationalize, at a strategic decision-making level, the way in which stochasticity can be taken into account in operating theater management, to help in the sizing and in the allocation of capacity. The three main sources of randomness are considered: durations of operations, unexpected emergencies and blocking because of a full recovery unit. Based on the Markov theory, our tool enables several performance measures to be estimated. An operating theater manager can use our approach to make informed decisions and assess, for example, the disruption of the planning by emergencies, the waiting times for emergency patients, the impact of the recovery unit, or the distribution of the working time. In particular, our approach helps determine the number of operations that should be planned in order to keep expected overtime limited. The tool is described in detail, discussed, and applied to the illustrative case of a Belgian hospital.