A pragmatic review of the applicability of existing digital navigator training programs to healthcare settings.

Authors

Mason Granof, Kathryn Ledley, Winna Xia, Keris Jän Myrick, John Torous

Published in

Journal of mental health (Abingdon, England). Pages 1-13. Sep 24, 2026. Epub Sep 24, 2026.

Abstract

The rapid integration of technology into healthcare led to the creation of the "Digital Navigator" role to support patient uptake of digital health tools. The need for this role is particularly acute in mental health care, where digital tools show promise but patient engagement is low without human support and people with serious mental illness face disproportionate barriers to digital inclusion. However, as no survey of active trainings for this role yet exists, our team assessed existing programs to determine the challenges and opportunities for sustainably integrating this training into healthcare, including mental health settings.
To determine whether these training programs are applicable to digital navigators operating in healthcare settings (including mental health care) by answering the following questions: (1) What do these programs offer in terms of healthcare-relevant content and teaching materials? (2) How are trainees evaluated within these programs? (3) How are these programs accessed and delivered?
A pragmatic search on 6 August 2025, was conducted for programs using the first 5 pages of Google, querying ChatGPT, and referring to the National Digital Inclusion Alliance guide.
Our search found 38 unique results, 29 of which were excluded (Appendix B). While all 9 trainings surveyed covered technological support, fewer covered health-care-specific competencies, and only one included trainer-led evaluation of soft skills. Qualitative summaries of all 9 programs are in Appendix C.
We indicate several directions for improving the applicability and sustainability of these programs within mental healthcare, including evaluation of soft skills through roleplay, training durable troubleshooting skills, utilizing Large Language Models for asynchronous training, leveraging existing volunteer pipelines and upskilling healthcare staff.

PMID:
42786903
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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