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Using a Machine Learning Algorithm to Predict Online Patient Portal Utilization: A Patient Engagement Study.

Ahmed U Otokiti1, Colleen M Farrelly2, Leyla Warsame3

  • 1Icahn School of Medicine at Mount Sinai Hospital, Internal Medicine and Informatics Department, New York, NY 10029, USA.

Online Journal of Public Health Informatics
|January 23, 2023
PubMed
Summary
This summary is machine-generated.

Patient portal access is predicted by privacy concerns and provider communication. Machine learning models can personalize patient engagement strategies for better online medical record utilization.

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Patient Engagement

Background:

  • Online patient portals offer convenient access to medical records.
  • Low utilization rates of patient portals persist in the U.S.

Purpose of the Study:

  • To predict patient access to online medical records using a machine learning approach.
  • To identify key factors influencing patient portal utilization.

Main Methods:

  • Cross-sectional study using Health Information National Trends datasets (2017-2018).
  • Employed machine learning algorithms including random forest, logistic regression, and evolved decision trees.
  • Analyzed survey data from U.S. adults to determine predictors of online medical record access.

Main Results:

  • Barriers to online access decreased from 26% in 2017 to 14% in 2018.
  • Patient use of portals for medication refills and provider messaging increased significantly from 2017 to 2018.
  • Privacy concerns, portal knowledge, and provider-patient discussions were identified as significant predictors of portal access.

Conclusions:

  • Privacy concerns, knowledge of portals, and provider communication are crucial for predicting patient access.
  • The developed methods can inform personalized patient engagement strategies during registration.
  • Improving patient engagement can enhance online medical record utilization.