Logo image
Back
Predicting Health Access During Critical Public Health Crises: An Analysis of Medical Office Responses to the COVID-19 Pandemic
Journal article   Peer reviewed

Predicting Health Access During Critical Public Health Crises: An Analysis of Medical Office Responses to the COVID-19 Pandemic

Nancy Zhong, Kirsten Wohlars, Mary Lee-Wong, Robert Promisloff, Niloofar Mirsaidi, Lawrence Amsel and Anthony Szema
Journal of occupational and environmental medicine, v 67(2)
28 Nov 2024
DOI: https://doi.org/10.1097/JOM.0000000000003284
PMID: 39639507

Abstract

COVID-19 pandemic telemedicine personal protective equipment policy machine learning artificial intelligence
This study explores how ambulatory medical practices adapted their policies in response to the global COVID-19 crisis. Practice and provider characteristics were used to build an artificial intelligence model that predicts future medical practice closures during critical events. We surveyed 261 outpatient medical practices and collected information on clinician age, gender, the protective measures implemented, closure status, and utilization of telemedicine services. These data were used to build an artificial intelligence model that predicts practice closure in future critical public health events. Responses varied by specialty, location, and physician characteristics. These factors predicted closure status in 85.45% of test cases. This paper demonstrates that practice characteristics can assist in predicting medical practice responses to public health events.

Metrics

12 Record Views

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
Web of Science research areas
Public, Environmental & Occupational Health
Logo image