Case Article—Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations

Dessislava Pachamanova (), Vera Tilson () and Keely Dwyer-Matzky ()
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Dessislava Pachamanova: Babson College, Wellesley, Massachusetts 02457
Vera Tilson: Simon Business School, University of Rochester, Rochester, New York 14627
Keely Dwyer-Matzky: Departments of Medicine and Pediatrics, School of Medicine and Dentistry and Golisano Children’s Hospital, University of Rochester Medical Center, Rochester, New York 14642

INFORMS Transactions on Education, 2022, vol. 22, issue 3, 178-187

Abstract: This case discusses a process improvement project aimed at maximizing the use of hospital capacity as the flu season looms. Dr. Erin Kelly heads the observation unit and turns to predictive models to improve the assignment of patients to her unit. The case covers three major themes: (1) data analytics life cycle and interface of predictive and prescriptive analytics in the context of process improvement, (2) design and ethical application of machine learning models, and (3) effecting organizational change to operationalize the findings of the analysis. Realistic data, R code, and Excel models are provided. The rich context of the case allows for discussing change management in a healthcare organization, analytics problem framing and model mapping, service process capacity analysis and Little’s law, data summaries and visualizations, interpretable machine learning algorithms, evaluations of predictive model performance, algorithmic bias, and dealing with dirty data. The case is appropriate for use in courses in machine learning, business analytics, operations management, and operations research, both at the advanced undergraduate level and at the master’s/MBA level.

Keywords: hospital observation unit operations; data analytics life cycle; machine learning; predictive analytics; prescriptive analytics; ethics; service process capacity analysis; change management (search for similar items in EconPapers)
Date: 2022
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