RESUMEN
We consider constructing feasible annual block schedules for residents in a medical training program. We must satisfy coverage requirements to guarantee an acceptable staffing level for different services in the hospital as well as education requirements to ensure residents receive appropriate training to pursue their individual (sub-)specialty interests. The complex requirement structure makes this resident block scheduling problem a complicated combinatorial optimization problem. Solving a conventional integer program formulation for certain practical instances directly using traditional solution techniques will result in unacceptably slow performance. To address this, we propose a partial fixing approach, which completes the schedule construction iteratively through two sequential stages. The first stage focuses on the resident assignments for a small set of predetermined services through solving a much smaller and easier problem relaxation, while the second stage completes the rest of the schedule construction after fixing those assignments specified by the first stage's solution. We develop cut generation mechanisms to prune off the bad decisions made by the first stage if infeasibility arises in the second stage. We additionally propose a network-based model to assist us with an effective service selection for the first stage to work on the corresponding resident assignments to achieve an efficient and robust performance of the proposed two-stage iterative approach. Experiments using real-world inputs from our clinical collaborator show that our approach can speed up the schedule construction at least 5 times for all instances and even over 100 times for some huge-size instances compared to applying traditional techniques directly.
Asunto(s)
Internado y Residencia , Humanos , Admisión y Programación de Personal , Carga de TrabajoRESUMEN
BACKGROUND: Most postgraduate medical education occurs in hospitals in an apprenticeship model with actual patients. Creating a work shift schedule must account for complex factors, including hospital needs, work-hour restrictions, trainee qualifications, and case distribution in order to fairly allocate the resident workload. In this study, we report the first successful implementation of an equitable, computer-generated scheduling system for anesthesiology residents. METHODS: A total of 24 residents at a single, urban training program were surveyed in 2015 to rank work shift difficulty. Shifts were categorized and translated into a weighted point system by program leadership based on the survey results. An automated and modifiable scheduling system was created to incorporate rule-based assignment of prerequisites and evenly distribute points throughout the academic year. Point values were retrospectively calculated in 2014, and prospectively calculated from 2015 to 2018. The equality of variance test was used to evaluate the variation of the SD of monthly average point distributions year-over-year and within each class of trainees. RESULTS: Year-over-year analysis revealed that post-point system implementation, call point distribution trended toward reduced variance in all 4 years, with significant reductions of 63% in 2016 (SD 4.9, P < .01), and 57% in 2017 (SD 5.8, P < .01). Analyzed by class, first-year trainees' SD decreased by 73% in 2016 (SD 2.5, P < .01), by 67% in 2017 (SD 3.1, P < .04), and 65% in 2018 (SD 3.3, P < .02) compared with the pre-point system year in 2014. The second year clinical anesthesia resident class SD decreased by 56% in 2015 (SD 5.9, P < .01), 41% in 2016 (SD 7.9, P < .02), and 49% in 2017 (SD 6.9, P < .01). CONCLUSION: The computerized point system improved work distribution equity year-over-year and within trainee cohort groups.
RESUMEN
Completing a residency program is a requirement for medical students before they can practice medicine independently. Residency programs in internal medicine must undergo a series of supervised rotations in elective, inpatient, and ambulatory units. Typically, a team of chief residents is charged to develop a yearly rotational schedule. This process is complex, as it needs to consider academic, managerial, regulatory, and legal restrictions while also facilitating the provision of patient care, ensuring a diverse educational experience, balancing the workload, and improving resident satisfaction. This study proposes (1) a multi-stage multi-objective optimization approach for generating yearlong weekly resident rotation schedules and (2) the use of Analytical Hierarchy Process (AHP) to compare schedules across multiple criteria to select those that are more equitable and hence to facilitate their adoption and implementation. Furthermore, the proposed approach allows the scheduling of periodic clinic rotation schemes that are commonly used to facilitate continuity of care, such as "4+1" or the "8+2" policies. In the "4+1" policy residents rotate for four consecutive weeks in different units prior to return for a week to a predetermined clinical post. Similarly, in the "8+2" policy, residents rotate eight weeks across multiple units before doing a two week rotation at a predetermined clinic.