Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data

Jeana M Holt; Charles Spanbauer; Rachel Cusatis; Aaron N Winn; AkkeNeel Talsma; Onur Asan; Melek Somai; Ryan Hanson; Jennifer Moore; Gregory Makoul; Bradley H Crotty

doi:10.1016/j.ijmedinf.2022.104810

Real-world implementation evaluation of an electronic health record-integrated consumer informatics tool that collects patient-generated contextual data

Int J Med Inform. 2022 Sep:165:104810. doi: 10.1016/j.ijmedinf.2022.104810. Epub 2022 Jun 11.

Authors

Affiliations

¹ University of Wisconsin-Milwaukee, College of Nursing, 2901 E. Hartford Ave, Milwaukee, WI 53201, USA. Electronic address: jmholt@uwm.edu.
² Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
³ Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
⁴ School of Pharmacy, Medical College of Wisconsin, Milwaukee, WI, USA.
⁵ University of Wisconsin-Milwaukee, College of Nursing, 2901 E. Hartford Ave, Milwaukee, WI 53201, USA.
⁶ School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, USA.
⁷ Collaborative for Healthcare Delivery Sciences, Medical College of Wisconsin, Milwaukee, WI, USA.
⁸ NRC Health, Lincoln, Nebraska, USA, Department of Medicine, Yale, School of Medicine, New Haven, CT, USA.

PMID: 35714549
DOI: 10.1016/j.ijmedinf.2022.104810

Abstract

Objective: Use the RE-AIM framework to examine the implementation of a patient contextual data (PCD) Tool designed to share patients' needs, values, and preferences with care teams ahead of clinical encounters.

Materials & methods: Observational study that follows initial PCD Tool scaling across primary care at a Midwestern academic health network. Program invitations, enrollment, patient submissions, and clinician views were tracked over a 1-year study period. Logistic regression modeled the likelihood of using the PCD Tool, accounting for patient covariates.

Results: Of 58,874 patients who could be contacted by email, 9,183 (15.6%) became PCD Tool users. Overall, 76% of primary care providers had patients who used the PCD Tool. Older age, female gender, non-minority race, patient portal activation, and Medicare coverage were significantly associated with increased likelihood of use. Number of office visits, medical issues, and behavioral health conditions also associated with use. Primary care staff viewed 18.7% of available PCD Tool summaries, 1.1% to 57.6% per clinic.

Discussion: The intervention mainly reached non-minority patients and patients who used more health services. Given the requirement for an email address on file, some patients may have been underrepresented. Overall, patient reach and adoption and clinician adoption, implementation, and maintenance of this Tool were modest but stable, consistent with a non-directive approach to fostering adoption by introducing the Tool in the absence of clear expectations for use.

Conclusion: Healthcare organizations must implement effective methods to increase the reach, adoption, implementation, and maintenance of PCD tools across all patient populations. Assisting people, particularly racial minorities, with PCD Tool registration and actively supporting clinician use are critical steps in implementing technology that facilitates care.

Keywords: Consumer Health Informatics; Health Information Technology; Implementation Science; Patient-Centered Care; Professional-Patient Relations.

Publication types

Observational Study
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Electronic Health Records*
Female
Humans
Informatics
Medicare*
Research Design
United States