Waiting times for elective procedures are a major health policy concern in many European countries. Initiatives to control waiting times involve supply-side policies that encompass raising public capacity, and demand-side policies with a prioritization of patients according to need for a better management of waiting lists. On a microeconomic level, complementary approaches to tackle the issue of waiting times include the use of Operational Research techniques. The present paper is in line with these approaches and provides strategies to reduce the waiting time for elective surgery in any speciality requiring multiple constrained resources. In the medium run, the objective is to determine the best admission policy at the tactical level. The resulting tactical plan which is based on a fixed number of patients derived from historical data of arrivals can be adjusted to patients in the queue to provide an operational plan. Several strategies to translate a tactical plan into an operational plan are considered and assessed in terms of hospital performance and patient satisfaction. We propose a new strategy that allows for substantial decrease in waiting time while keeping a high hospital performance. The hospital performance is measured by a weighted sum of several criteria such as additional and cancelled operations, plan changes and deviations of resource consumptions compared to their target levels. Weights in the hospital performance indicator are drawn at random in selected intervals to portray a wide spread of managers’ assessments. Simulation results show that several strategies are dominant whatever the assessment profile. We also identify the best strategies to reach a limited waiting time.
hospital performance, waiting time, assessment profile, dominance, tactical and operational planning, multiple constrained resources.
Adan I., Bekkers J., Dellaert N., Jeunet J., Vissers J. (2011). Improving operational effectiveness of tactical master plans for emergency and elective patients under stochastic demand and capacitated resources. European Journal of Operational Research, Vol. 213, pp. 290-308.
Adan I., Bekkers J., Dellaert N., Vissers J., Yu X. (2009). Patient mix optimization and stochastic resource requirements: A case study in cardiothoracic surgery planning. Health Care Management Science, Vol. 12, pp. 129-141.
Adan I., Vissers J. (2002). Patient mix optimisation in hospital admission planning: a case study. International Journal of Operations and Production Management, Vol. 22, pp. 445-461.
Beliën J., Demeulemeester E. (2007). Building cyclic master surgery schedules with levelled resulting bed occupancy. European Journal of Operational Research, Vol. 176, pp. 1185–1204.
Blake J., Donald J. (2002). Mount sinaï hospital uses integer programming to allocate operating room time. Interfaces, Vol. 32, pp. 63-73.
Cardoen B., Demeulemeester E., Beliën J. (2010). Operating room planning and scheduling: A literature review. European Journal of Operational Research, Vol. 201, pp. 921-932.
Comas M., Castells X., Hoffmeister L., Roman R., Cots F., Mar J. et al. (2008). Discreteevent simulation applied to analysis of waiting lists. evaluation of a prioritization system for cataract surgery. Value in Health, Vol. 7, pp. 1203-1213.
Guerriero F., Guido R. (2011). Operational research in the management of the operating theatre: a survey. Health Care Management Science, Vol. 14, pp. 89-114.
Guinet A., Chaabane S. (2003). Operating theatre planning. International Journal of Production Economics, Vol. 85, pp. 69-81.
Gupta D. (2007). Surgical suites’ operations management. Production and Operations Management, Vol. 16, pp. 689-700.
Houdenhoven M. V., Oostrum J. V., Hans E., Wullink G., Kazemier G. (2007). Improving operating room efficiency by applying bin-packing and portfolio techniques to surgical case scheduling. Anesthesia and Analgesia, Vol. 105, pp. 707-714.
Hulshof J., Boucherie R., Hans E., Hurink J. (2013). Tactical resource allocation and elective patient admission planning in care processes. Health Care Management Science, No. 16, pp. 152-166.
Jebali A., Alouane A. H., Ladet P. (2006). Operating room scheduling. International Journal of Productions Economics, Vol. 99, pp. 52-62.
Lamiri M., Xie X., Dolgui A., Grimaud F. (2008). A stochastic model for operating room planning with elective and emergency demand for surgery. European Journal of Operational Research, Vol. 185, pp. 1026-1037.
Lin R., Sir M., Pasupathy K. (2013). Multi-objective simulation optimization using data envelopment analysis and genetic algorithm: specific application to determining optimal resource levels in surgical services. Omega, No. 41, pp. 881-892.
Ma G., Demeulemeester E. (2013). A multilevel integrative approach to hospital case mix and capacity planning. Computers and Operations Research, Vol. 40, pp. 2198-2207.
McManus M., Long M., Cooper A., Mandell J., Berwick D., Pagano M. (2003). Variability in surgical caseload and access to intensive care services. Anesthesiology, Vol. 98, pp. 1491–1496.
Oostrum J. V., Vanhoudenhoven M., Hurink J., Hans E., Wullink G., Kazemier G. (2008). A master surgery scheduling approach for cyclic scheduling in operating room departments.OR Spectrum, Vol. 30, pp. 355-374.
Patrick J., Puterman M., Queyranne M. (2008). Dynamic multipriority patient scheduling for a diagnostic resource. Operations Research, Vol. 6, pp. 1507-1525.
Persson M., Persson J. (2009). Health economic modelling to support surgery management at a swedish hospital. Omega, No. 37, pp. 853-863.
Testi A., Tanfani E. (2009). Tactical and operational decisions for operating room planning: Efficiency and welfare implications. Health Care Management Science, Vol. 12, pp. 363-373.
Testi A., Tanfani E., Torre G. (2007). A three-phase approach for operating theatre schedules.Health Care Management Science, Vol. 10, pp. 163–172.