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Predicting Health Access During Critical Public Health Crises: An Analysis of Medical Office Responses to the
Nancy Zhong1, Kirsten Wohlars, Mary Lee-Wong
1From the Ward Melville High School InSTAR Program, Stony Brook, New York (N.Z.); Carnegie Mellon University, Pittsburgh, Pennsylvania (N.Z.); Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York (K.W.); Three Village Allergy & Asthma, PLLC, South Setauket, New York (K.W., A.S.); Stony Brook University, Stony Brook, New York (K.W., A.S.); Division of Clinical Immunology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York (M.L.-W.); Division of Allergy and Immunology, Department of Medicine, Maimonides Medical Center, Brooklyn, New York (M.L.-W.); Drexel University College of Medicine, Philadelphia, Pennsylvania (R.P.); Department of Medicine, North Shore University Hospital, Manhasset, New York (N.M.); Department of Psychiatry, Columbia University, New York, New York (L.A.); Divisions of Pulmonary/Critical Care and Allergy/Immunology, Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York (A.S.); and Department of Occupational Medicine, Epidemiology, and Prevention, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York (A.S.).
Objectives:
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.
Methods:
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.
Results:
Responses varied by specialty, location, and physician characteristics. These factors predicted closure status in 85.45% of test cases.
Conclusions:
This paper demonstrates that practice characteristics can assist in predicting medical practice responses to public health events.
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