Predictors and patterns of portal use in patients with multiple chronic conditions

Chronic Illn. 2020 Dec;16(4):275-283. doi: 10.1177/1742395318803663. Epub 2018 Oct 4.

Abstract

Objective: To explore predictors of portal use by patients (registered portal users) with multiple chronic conditions according to demographic characteristics and use of specific features hypothesized to support self-management.

Methods: Two data sources were used in this analysis: electronic health records and 12 months of data from web server log files. Patients (n = 500) included in the analysis were 45 years or older, registered portal users, and diagnosed with at least two chronic conditions. We fit a negative binomial regression model to predict portal use (number of logins) based on practice size and location, demographic characteristics, and use of specific portal features (secure messaging and patient-entered data).

Results: Among patients with one or more logins, age, distance separating the patient from his or her primary care provider, and having a diagnosis of heart failure were significant predictors of portal use (p < .05). No significant differences in portal use were found according to gender, ethnicity, or practice size and location.

Conclusion: Considering the extraordinary investment on implementation and meaningful use of portal technology, low overall use and the large number of registered non-users is especially troubling. Regardless, our results demonstrate potential opportunities to leverage portal technology especially for patients living in rural and underserved areas to improve self-management of chronic illness.

Keywords: Electronic patient portal; chronic illness; patient electronic access; patient engagement; self-management.

MeSH terms

  • Aged
  • Chronic Disease / epidemiology*
  • Comorbidity
  • Electronic Health Records / statistics & numerical data
  • Female
  • Humans
  • Male
  • Middle Aged
  • Patient Acceptance of Health Care / statistics & numerical data*
  • Patient Portals / statistics & numerical data*
  • Self-Management / methods