P3: An adaptive modeling tool for post-COVID-19 restart of surgical services

Abstract

Objective: To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic. Materials and Methods: Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs. Results: The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. Conclusions: Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.