Characterization, prediction, and mitigation of Code Help events at Massachusetts General Hospital

• Main Author: Adib, Christian(Christian Tanios)
• Other Authors: Retsef Levi and Saurabh Amin.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Sloan School of Management.; Civil and Environmental Engineering.; Leaders for Global Operations Program.
• Online Access: https://hdl.handle.net/1721.1/122580

Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT === Thesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT === Cataloged from PDF version of thesis. === Includes bibliographical references (pages 105-107). === This thesis suggests a method to characterize congestion alarms triggered by the Emergency Department (ED) at Massachusetts General Hospital, attempts to predict the incidence of these alarms using logistic regression, and proposes operational recommendations for the mitigation of congestion events termed Code Help. In order to characterize Code Help alarms, we begin by identifying a set of relevant operational features that allow us to describe them objectively and proceed to clustering Code Help observations using k-means. We regress these features on binary variables indicating Code Help incidence to predict, at 7AM in the morning, whether or not Code Help will occur on a given day. Based on this analysis, we suggest a set of recommendations to operationalize a more effective response to Code Help. Our characterization uncovers three main classes of Code Help: those exhibiting a high level of ED arrivals in the hour preceding the alarm with a relatively low operational utilization of inpatient beds, those exhibiting a low level of ED arrivals in the hour preceding the alarm with a relatively high operational utilization of inpatient beds, and those exhibiting high arrivals and utilization. The logistic regression identifies two statistically significant predictive features: ED Census at 7 AM and the Number of Boarders in the ED at 7 AM, scaled against same time of day and day-of-week observations. Moreover, we identify discharge orders and outpatient pharmacy orders as early discharge indicators that can be used to prioritize Medicine patients in terms of their readiness to be discharged when Code Help is called. === by Christian Adib. === M.B.A. === S.M. === M.B.A. Massachusetts Institute of Technology, Sloan School of Management === S.M. Massachusetts Institute of Technology, Department of Civil and Environmental Engineering