Annual block scheduling for family medicine residency programs with continuity clinic considerations

ABSTRACT

This article presents a new model for constructing annual block schedules for family medicine residents based on the rules and procedures followed by the Family Medicine Department at the University of Texas Health Science Center in San Antonio (UTHSC-SA). Such residency programs provide 3 years of specialty training for recent medical school graduates. At the beginning of each academic year, each trainee is given an annual block schedule that indicates his or her monthly assignments. These assignments are called rotations and include a variety of experiences, such as pediatric ambulatory care, the emergency room, and inpatient surgery. An important requirement associated with a subset of the rotations is that the residents spend multiple half-day sessions a week in a primary care clinic treating patients from the community. This is a key consideration when constructing the annual block schedules. In particular, one of the primary goals of most residencies is to ensure that the number of residents in clinic each day is approximately the same, so that the number of patients that can be seen each day is also the same. Uniformity provides for a more efficient use of supervisory and staff resources.

 The difficulty in achieving this goal is that not all rotations allow for clinic duty and that the number of patients that can be seen by a resident each session depends on his or her year of training. When constructing annual block schedules, two high-level sets of variables are available to the program coordinator. The first is the assignment of residents to rotations for each of the 12 blocks, and the second is the (partial) ability to adjust the days on which a resident has clinic duty during each rotation. In approaching the problem, our aim was to redesign the current rotations while giving all residents a 12-month schedule that concurrently (i) balances the number of patients that can be seen in the clinic during each half-day session and (ii) minimizes the number of adjustments necessary to achieve the first objective. The problem was formulated as a mixed-integer program; however, it proved too difficult to solve exactly. As an alternative, several optimization-based heuristics were developed that yielded good feasible solutions. The model and computations are illustrated with data provided by the Family Medicine Department at UTHSC-SA for a typical academic year.

KEYWORDS:

• Resident scheduling
• block scheduling
• medical rotations
• soft constraints
• mixed-integer programming

Funding

This work was supported by grant no. 153239 from the University of Texas Office of the Executive Chancellor for Health Affairs.

Additional information

Notes on contributors

Jonathan F. Bard

Jonathan F. Bard is a Professor of Operations Research & Industrial Engineering in the Mechanical Engineering Department at the University of Texas at Austin. He holds the Industrial Properties Corporation Endowed Faculty Fellowship and serves as the Associate Director of the Center for the Management of Operations and Logistics. He received a D.Sc. in Operations Research from The George Washington University. His research centers on improving healthcare delivery, personnel scheduling, production planning and control, and the design of decomposition algorithms for solving large-scale optimization problems and has appeared in a wide variety of technical journals. Currently, he serves on six editorial boards and previously was a Focused Issue Editor of IIE Transactions and an Associate Editor of Management Science. He is a registered engineer in the State of Texas, a fellow of IIE and INFORMS, and a senior member of IEEE. In the past, he has held a number of offices in each of these organizations.

Zhichao Shu

Zhichao Shu is an algorithm engineer working at Alibaba Group, focusing on personalized recommendation at China's largest E-commerce platform. He received a Ph.D. in Operations Research & Industrial Engineering from the University of Texas at Austin. His research centers on personnel scheduling and patient flow improvement in the healthcare area, and he has published several papers in related technical journals.

Douglas J. Morrice

Douglas J. Morrice holds the Bobbie and Coulter R. Sublett Centennial Professorship in Business. He is also Professor of Operations Management and the Director of the University of Texas' Supply Chain Management Center of Excellence in the McCombs School of Business at The University of Texas at Austin. He has an ORIE Ph.D. from Cornell University. His research interests include simulation design, modeling, and analysis; healthcare delivery management; and supply chain risk management. He is a Senior Editor for Production and Operations Management, an Editor-at-Large for Interfaces, and an Area Editor for IIE Transactions on Healthcare Systems Engineering.

Luci K. Leykum

Luci K. Leykum is a Professor of Medicine in the School of Medicine at the University of Texas Health Science Center at San Antonio. She received her M.D. from the College of Physicians and Surgeons and Columbia University and completed her residency training in Internal Medicine at the New York–Presbyterian Hospital/Columbia University Medical Center. Her research interests are focused on the application of complexity science to health care systems to develop strategies to improve patient outcomes. Her work has been published in a variety of medical journals, and she serves as a Deputy Editor for the Journal of General Internal Medicine.

Ramin Poursani

Ramin S. Poursani is an Associate Professor in the Department of Family and Community Medicine, School of Medicine at the University of Texas Health Science Center at San Antonio. He received his M.D. from the Cerahapasa School at Medicine at Istanbul and completed his residency training in Family Medicine at the University of Texas Health Science Center at San Antonio. His research interests are focused on the application of complexity science to health care systems in order to develop strategies to improve patient outcomes and satisfaction.