Multi-objective Robust Stochastic Planning and Scheduling of Healthcare Service Providers
Industrial Engineers and Management Scientists (Sanjay Mehrotra and Mark S. Daskin), surgeons (Heron Rodriguez), and medical educators (Debra DaRosa) at Northwestern University and the Northwestern Memorial Hospital have collaborated to identify several of the key logistical problems that keep resident-patient continuity of care low and variability in resident surgical experience high. They guided graduate student Jonathan Turner towards developing policy solutions and software applications that will help hospitals solve these problems, and better educate and train future medical surgical residents. In particular, their research has already motivated a policy improvement changing the surgical student rotation time from one to two months in the vascular surgery department.
Teaching medical surgical residents is a very challenging responsibility for academic hospitals because they must balance between many competing objectives. They must incorporate residents in the process of diagnosing patients, give them many opportunities to assist surgeons in a variety of difficult procedures, have them read medical papers and attend lectures, develop their professional and team-based skills, and, perhaps most importantly, create an environment in which they are developing relationships with patients rather than simply acquiring medical knowledge.
Providing these learning experiences to already overworked residents without compromising patient care is a logistical conundrum that overwhelms medical educators. The variability between residents’ surgical experiences is often very high and residents often assist in the surgical procedures of patients with whom they have spent little time. The medical literature often describes these problems and their impact on the development of residents but it seldom goes beyond descriptions of the problem. Research is needed that shows how hospitals can analyze and solve these logistical problems.
Mathematical models with closed form solutions, simulation models and optimization models were used to study the surgical resident education and training process. These models were calibrated with real data from a major academic medical center. Results of these models were used to study policy implications of different approaches to surgical resident education. Bottleneck in the current approach to providing education were identified, and policy recommendations were arrived at. A new surgical resident scheduling system is also developed to facilitate surgical case assignments to the residents and fellows.
Researchers should cite this work as follows:
Sanjay Mehrotra (2011), "Multi-objective Robust Stochastic Planning and Scheduling of Healthcare Service Providers," http://nees.org/resources/2622.