Heterogeneity of diagnosis and documentation of post-COVID conditions in primary care: A machine learning analysis.

Journal: PloS one
PMID: 40378166

Abstract

BACKGROUND: Post-COVID conditions (PCC) have proven difficult to diagnose. In this retrospective observational study, we aimed to characterize the level of variation in PCC diagnoses observed across clinicians from a number of methodological angles and to determine whether natural language classifiers trained on clinical notes can reconcile differences in diagnostic definitions.

Authors

  • Nathaniel Hendrix
    Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: nhendrix@hsph.harvard.edu.
  • Rishi V Parikh
    Division of Research, Kaiser Permanente Northern California, Oakland.
  • Madeline Taskier
    Center for Professionalism and Value in Health Care, American Board of Family Medicine, Washington, District of Columbia, United States of America.
  • Grace Walter
    Robert Graham Center, American Academy of Family Physicians, Washington, District of Columbia, United States of America.
  • Ilia Rochlin
    Inform and Disseminate Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
  • Sharon Saydah
    Coronavirus and Other Respiratory Viruses Division, National Center for Immunizations and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
  • Emilia H Koumans
    Coronavirus and Other Respiratory Viruses Division, National Center for Immunizations and Respiratory Disease, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
  • Oscar Rincón-Guevara
    Inform and Disseminate Division, Office of Public Health Data, Surveillance, and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
  • David H Rehkopf
    Department of Epidemiology and Population Health, Stanford School of Medicine, Palo Alto, California, United States of America.
  • Robert L Phillips
    Center for Professionalism & Value in Health Care, American Board of Family Medicine Foundation, Lexington, USA.

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