An online optimization approach for the Real Time Management of operating rooms. (December 2015)

• Record Type: Journal Article
• Authors: Duma, Davide; Aringhieri, Roberto
• Is Part Of: Operations research for health care. Volume 7(2015:Dec.)
• Page Start: 40
• Page End: 51
• Publication Date: 2015-12
• Subjects: Operating room -- Real Time Management -- Surgical pathway -- Elective surgery -- Emergency surgery; Medical care -- Mathematical models -- Periodicals; Medical policy -- Mathematical models -- Periodicals; Health services administration -- Mathematical models -- Periodicals; Operations research -- Periodicals; Operations Research -- Periodicals; Health Services Research -- Periodicals; Health Policy -- Periodicals; Delivery of Health Care -- Periodicals; 362.106805
• DOI: 10.1016/j.orhc.2015.08.006
• Languages: English
• ISSNs: 2211-6923

Abstract

Abstract: The Real Time Management (RTM) of operating rooms is the decision problem arising during the fulfilment of the surgery process scheduling of elective patients, that is the problem of supervising the execution of such a schedule and, in case of delays, to take the more rational decision regarding the surgery cancellation or the overtime assignment. The main concern of this paper is to propose a model for the RTM and to evaluate its impact on the OR performance assessed by a set of patient- and facility-centred indices. To this end, we consider a generic surgical clinical pathway for elective patients–inspired to a real case study–in which we evaluate the introduction of an online optimization approach for the RTM and some additional optimization modules to deal with the surgery process scheduling problem. To the best of our knowledge, the RTM is not clearly addressed in the literature and this is the first attempt to propose an online approach in the context of surgery process scheduling. We propose a hybrid simulation and optimization model in which simulation is used to model the inherent stochasticity and to replicate the elective patient flow on which the online approach for the RTM and the additional optimization modules operates. We report an accurate computational analysis proving the effectiveness of the proposed approach to the RTM. Finally, we demonstrate the capability and the flexibility of our approach extending our hybrid model to deal with emergency surgeries and different trained surgery teams.