"P3": an adaptive modeling tool for post-COVID-19 restart of surgical services. Issue 2 (28th April 2021)

• Record Type: Journal Article
• Authors: Joshi, Divya; Jalali, Ali; Whipple, Todd; Rehman, Mohamed; Ahumada, Luis M
• Is Part Of: JAMIA open. Volume 4:Issue 2(2021)
• Publication Date: 2021-04-28
• Subjects: COVID-19 -- surgical backlog -- predictive analytics -- optimization -- decision support; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooab016
• Languages: English
• ISSNs: 2574-2531

Abstract

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.