Access planning and resource coordination for clinical research operations

Abstract

This research creates an operations engineering and management methodology to optimize a complex operational planning and coordination challenge faced by sites that perform clinical research trials. The time-sensitive and resource-specific treatment sequences for each of the many trial protocols conducted at a site make it very difficult to capture the dynamics of this unusually complex system. Existing approaches for site planning and participant scheduling exhibit both excessively long and highly variable Time to First Available Visit (TFAV) waiting times and high staff overtime costs. We have created a new method, termed CApacity Planning Tool And INformatics (CAPTAIN) that provides decision support to identify the most valuable set of research trials to conduct within available resources and a plan for how to book their participants. Constraints include (i) the staff overtime costs, and/or (ii) the TFAV by trial. To estimate the site’s metrics via a Mixed-Integer Program, CAPTAIN combines a participant trajectory forecasting with an efficient visit booking reservation plan to allocate the date for the first visit of every participant’s treatment sequence. It also plans a daily nursing staff schedule that is optimized together with the booking reservation plan to optimize each nurse’s shift assignments in consideration of participants’ requirements/needs.

Keywords:

• Health care operations
• patient flow modeling
• mixed integer programming
• production planning and scheduling
• stochastic models

Additional information

Funding

Partial funding for this work has been received from NSF Grants CMMI 1233095, 1548201 and NIH CTSA grant UL1TR000433. The funding organizations had no role in the design or conduct of this research. Dr. Deglise-Hawkinson acknowledges partial support from a Seth Bonder Fellowship.

Notes on contributors

Jivan Deglise-Hawkinson

Jivan Deglise-Hawkinson is an OR senior analyst in the Revenue Management (RM) department at American Airlines (AA). His interests include data-driven forecasting methodologies and leveraging them to create optimized decision support tools. His current work is focused on developing and improving the forecasting tools of AA, to make them more dynamic, while using machine learning methodologies. He was Chief Scientist at Lean Care Solutions (2015-2018), a healthcare startup company focused on creating decision support tools for hospital readmissions prediction and prevention. He received his Ph.D. from the University of Michigan Industrial and Operations Engineering department (2010-2015) focusing on admission control and large stochastic queuing networks applied to various healthcare systems.

David L. Kaufman

David Kaufman is an assistant professor at the University of Michigan-Dearborn, College of Business, where he teaches courses in Decision Sciences. He holds a Ph.D. in industrial and operations engineering from the University of Michigan. Prior to joining the faculty at Dearborn, Dr. Kaufman was a lecturer at the University of Michigan, where he taught courses in operations research, corporate finance, and financial engineering. His previous teaching experience also includes a course in stochastic processes at Cornell University. Dr. Kaufman’s previous industry experience includes product development for RiskMetrics Group, a financial risk management company that was acquired by MSCI. His research interests are in stochastic processes and decision models for systems where variability and uncertainty play an important role in design, analysis, and management.

Blake Roessler

Dr. Blake Roessler is Professor Emeritus of Internal Medicine University of Michigan Hospitals-Michigan Medicine. He was also the director of the Michigan Clinical Research Unit (MCRU) at the time of this research. He has developed an ongoing interest in operational engineering of clinical research systems sad well as clinical research applications of cell and gene therapy. As a rheumatologist with specialties in gout and uric acid metabolism, he has been affiliated with multiple hospitals, including Michigan Medicine and the Veterans Affairs Ann Arbor Healthcare System. He received his medical degree from University of Cincinnati College of Medicine and practiced medicine for more than 20 years before his retirement a few years ago.

Mark P. Van Oyen

Mark Van Oyen is a professor of industrial and operations engineering (IOE) at the University of Michigan. His interests include the analysis, design, prediction and control of stochastic systems. His current research emphasizes stochastic systems, optimization, and prescriptive analytics for healthcare operations and medical decision-making. He co-authored papers that won numerous awards. He has served as Associate Editor for Operations Research, Management Science, Naval Research Logistics, and IIE Transactions, and IIE Trans. Healthcare Syst. Engr. and Senior Editor for Flexible Services & Manufacturing. He was a faculty member of the Northwestern Univ. Sch. of Engr. (1993-2005) and Loyola Univ. of Chicago’s Sch. of Bus. Admin. (1999-2005). He has received grant funding from the NSF, ONR, NIH, EPRI, ALCOA, General Motors, and the VA. He received his Ph.D. from Electrical Engr. Systems from the Univ. of Michigan and has worked for GE Corporate R&D.