On a daily basis surgeons, nurses, and managers face cancellation of surgery, peak demands on wards, and overtime in operating rooms. Moreover, the lack of an integral planning approach for operating rooms, wards, and intensive care units causes low resource utilization and makes patient flows unpredictable. An ageing population and advances in medicine are putting the available healthcare budget under great pressure. Under these circumstances, hospitals are seeking innovative ways of providing optimal quality at the lowest costs. This thesis provides hospitals with instruments for optimizing surgical patient planning. We describe a cyclic and integrated operating room planning approach, called master surgical scheduling, and models for efficient planning of emergency operations. Application of these instruments enables the simultaneous optimization of the utilization of operating rooms, ward and intensive care units. Moreover, iteratively executing a master schedule of surgical case types provides steady and thus more predictable patient flows in hospitals. The approach is generic and so can be implemented taking account of specific characteristics of individual hospitals. Prerequisites for successful implementation of logistical models in hospitals comprise sufficient room for last-minute changes as well as keeping the ultimate responsibility for individual patient scheduling with medical specialists. Both are satisfied in the master surgical scheduling approach which has already been successfully implemented in hospitals.

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Erasmus School of Economics (ESE) Erasmus University Rotterdam (EUR) Prof.dr.ir. R. Dekker Prof.dr.ir. J.M.H. Vissers Dr.ir. E.W. Hans Dr. G. Kazemier (co-promotor)
A.P.M. Wagelmans (Albert)
Erasmus University Rotterdam , Erasmus Research Institute of Management
hdl.handle.net/1765/16728
ERIM Ph.D. Series Research in Management
Erasmus Research Institute of Management

van Oostrum, J. (2009, September 4). Applying Mathematical Models to Surgical Patient Planning (No. EPS-2009-179-LIS). ERIM Ph.D. Series Research in Management. Retrieved from http://hdl.handle.net/1765/16728