Quantifying the impact of care team discontinuities on medically unnecessary delays in inpatient flow
Author(s)Johnston, Andrew Thomas, S.M. Massachusetts Institute of Technology
Leaders for Global Operations Program.
Retsef Levi and Patrick Jaillet.
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This thesis quantifies the impact of clinical care team discontinuities on inpatient length-of-stay (LOS) and admission wait time within Massachusetts General Hospital's Department of Medicine (DOM). The DOM is the hospital's largest clinical department by inpatient volume and supports a highly diverse patient population. Like many Academic Medical Centers, the DOM is confronted with increasing inpatient volume (>5% annual growth) and is showing symptoms of being capacity constrained, including rising patient wait times for admission from the Emergency Department. With the goal of informing specific interventions to increase patient throughput, this study evaluates the impact of end-of-rotation Attending physician handoffs (HOFs) on LOS and admission wait time on four, resident-staffed, general care floors with similar patient populations, clinical team configurations, and shift patterns. When combined with independently-distributed patient demand and the randomized assignment of patients to floors, the hospital's residency schedule creates natural randomized experiments through which the impact of HOFs can be isolated. It is found that patients admitted to a floor two days before a HOF spend an average of 0.8 days longer in the hospital than otherwise similar patients, while patients admitted one day before a HOF spend 0.8 fewer days in the hospital (Wilcoxon-Mann-Whitney RS, two-sided, a = 0.05). Further, average admission wait time increases by 15%-34% during the last two days of an Attending's rotation (t-test of means, pooled variance, two-sided, a = 0.05). Finally, a series of regression models that utilize only the information available when a patient is first admitted demonstrate that proximity to a future HOF at point of admission is a significant and robust predictor of LOS across major diagnostic categories (Monte Carlo Cross-Validation, [alpha] = 0.05). The dynamics this study uncovers can be used to attenuate the negative impacts of HOFs on patient LOS by informing the design of clinician rotation schedules, care team structures, and new patient assignment practices.
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Cataloged from PDF version of thesis.Includes bibliographical references (pages 91-93).
DepartmentSloan School of Management.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.; Leaders for Global Operations Program.
Massachusetts Institute of Technology
Sloan School of Management., Electrical Engineering and Computer Science., Leaders for Global Operations Program.