SAMPSON: A HYBRID SIMULATION AND OPTIMIZATION MODEL FOR MANAGING SURGICAL RESOURCES AND REDUCING WAITING TIMES

Purpose: to optimize the selection of surgical patients for treatment in a given time period, subject to the extant resource allocation scenario and constraints on the utilization of those resources.

Method: Sampson is a software tool that uses a quantitative, goal oriented approach to optimize the utilization of health care resources. The Sampson optimization module accepts a surgical waiting list as an input value, and selects a set of patients for treatment in a given time period that will keep waiting times below target Maximum Acceptable Waiting (MAW) times, subject to alternative allocation of health care resources.  These resources include operating room (OR) time, Special Care Unit (SCU) time, OR nursing time, anesthetist time, surgical cost and aftercare cost.  Additional determinants of system performance which are factored into the Sampson patient selection process are changes in staff availability, patient care policies and MAW values by surgical procedure. Evaluation of Sampson’s predictions of system performance under different resource allocation scenarios and policy sets is intended to provide decision support to system managers. Furthermore, the use of the Sampson patient selection set is intended to optimize efficiency within a potentially complex organizational structure, including health regions, hospitals, surgical divisions and surgeons. The surgical waiting list used as an input by the Sampson optimization module may be extracted from a surgical center’s operational data systems, or may be produced by a patient arrivals simulation process.  The arrivals simulation is based on the historical arrival pattern and expected future changes to that pattern.

Results: An example scenario is presented which compares the predicted outcomes of three different resource allocation options for a two hospital surgical system: 1.       No Change: continue with existing resources and ORs, 2.       Expand: add one outpatient-only OR at hospital A, maintain all ORs now at Hospital B, 3.       Reassign: add one outpatient-only OR at hospital A, withdraw one mixed inpatient/outpatient OR, and the associated supporting resources, from hospital B

Conclusion: The results have significant implications for policy makers and health service researchers interested in optimization of resource allocation decisions and minimizing the waiting time for surgical treatment.