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

Authors

  • Ahmed U. Otokiti Icahn School of Medicine at Mount Sinai, NY and Luminate Informatics, LLC https://orcid.org/0000-0002-0723-5721
  • Colleen M. Farrelly 2 Staticlysm, LLC, Palmetto Bay, FL 33157, USA
  • Leyla Warsame Geisinger Health Systems, Internal Medicine and Clinical Informatics Department, Danville, PA 17821, USA
  • Angie Li University at Buffalo, Department of Biomedical Informatics, Buffalo, NY 14203, USA

DOI:

https://doi.org/10.5210/ojphi.v14i1.12851

Abstract

Objective: There is a low rate of online patient portal utilization in the U.S. This study aimed to utilize a machine learning approach to predict access to online medical records through a patient portal.

Methods: This is a cross-sectional predictive machine learning algorithm-based study of Health Information National Trends datasets (Cycles 1 and 2; 2017-2018 samples). Survey respondents were U.S. adults (≥18 years old). The primary outcome was a binary variable indicating that the patient had or had not accessed online medical records in the previous 12 months. We analyzed a subset of independent variables using k-means clustering with replicate samples. A cross-validated random forest-based algorithm was utilized to select features for a Cycle 1 split training sample. A logistic regression and an evolved decision tree were trained on the rest of the Cycle 1 training sample. The Cycle 1 test sample and Cycle 2 data were used to benchmark algorithm performance.

Results: Lack of access to online systems was less of a barrier to online medical records in 2018 (14%) compared to 2017 (26%). Patients accessed medical records to refill medicines and message primary care providers more frequently in 2018 (45%) than in 2017 (25%).

Discussion: Privacy concerns, portal knowledge, and conversations between primary care providers and patients predict portal access.

Conclusion: Methods described here may be employed to personalize methods of patient engagement during new patient registration.

Author Biography

Ahmed U. Otokiti, Icahn School of Medicine at Mount Sinai, NY and Luminate Informatics, LLC

Chief Innovation Officer at Prolific Health,  LLC

Chief Medical Informatics Officer at Luminate Informatics, LLC

Physician-Informaticist at Icahn School of Medicine at Mount Sinai

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Published

2022-12-19

How to Cite

Otokiti, A. U., Farrelly, C. M., Warsame, L., & Li, A. (2022). Using a Machine Learning Algorithm to Predict Online Patient Portal Utilization: A Patient Engagement Study. Online Journal of Public Health Informatics, 14(1). https://doi.org/10.5210/ojphi.v14i1.12851