Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.

Journal: JAMA network open
Published Date: Dec 2, 2019

Abstract

IMPORTANCE: Inpatient overcrowding is associated with delays in care, including the deferral of surgical care until beds are available to accommodate postoperative patients. Timely patient discharge is critical to address inpatient overcrowding and requires coordination among surgeons, nurses, case managers, and others. This is difficult to achieve without early identification and systemwide transparency of discharge candidates and their respective barriers to discharge.

Authors

  • Kyan C Safavi
    Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston.
  • Taghi Khaniyev
    MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge.
  • Martin Copenhaver
    Department of Perioperative Services, Massachusetts General Hospital, Boston.
  • Mark Seelen
    Department of Perioperative Services, Massachusetts General Hospital, Boston.
  • Ana Cecilia Zenteno Langle
    Department of Perioperative Services, Massachusetts General Hospital, Boston.
  • Jonathan Zanger
    MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge.
  • Bethany Daily
    Department of Perioperative Services, Massachusetts General Hospital, Boston.
  • Retsef Levi
    MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge.
  • Peter Dunn
    Department of Perioperative Services, Massachusetts General Hospital, Boston.

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