Surgery Site Optimization Model

Overview

To expedite the current process of Physician Assistants manually reviewing each patient’s case that is recommended for surgery and assigning a location, MPrOVE built a predictive model that will automatically assign locations for a majority of cases. This predictive model will optimize staff time, expedite the surgery scheduling process, and ultimately lead to improved provider and patient satisfaction.

Value Proposition to Michigan Medicine

By considering patient- and surgery-related factors, the MPrOVE team has developed an operationally-endorsed prediction model that may reduce these delays through partial automation of the review process. 

Principal Investigator(s)

Geoff Burns, PhD

Lisa Dosset, MD, MPH

Clinical Champion

Eve Kerr

Digital Health Innovation Support

Digital Health Innovation is supporting the MPrOVE team in evaluating ·several research questions related to the implementation of this model into clinical workflows. This project will address key usability and workflow issues with broad implications for model implementation. DHI also supported the model deployment in Epic, enabling model predictors to be viewed at the time of score generation.

Partnerships

MM Pre-Op Clinic

MM Ambulatory Surgery

MM Nursing

MM Anesthesia

University of Michigan Medical Group (UMMG)

Health Information Technology and Services  (HITS) 

Clinical Intelligence Committee (CIC)

Intellectual Property

Invention Disclosure #

Patent Issued: