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

Divya Joshi, Ali Jalali, Todd Whipple, Mohamed Rehman, Luis M. Ahumada

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numberooab016
JournalJAMIA Open
Issue number2
StatePublished - Apr 1 2021
Externally publishedYes


  • COVID-19
  • decision support
  • optimization
  • predictive analytics
  • surgical backlog

ASJC Scopus subject areas

  • Health Informatics


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