Accepted for/Published in: JMIR Formative Research
Date Submitted: Jul 19, 2024
Date Accepted: Jul 2, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Innovations in technology-enabled behavioral and mental health services: A preliminary look at the processes and outcomes of a platform using health coaching, benefit navigation, and machine learning
ABSTRACT
Background:
Limited access to quality mental health treatment harms health and quality of life while costing individuals and organizations millions in increased medical spending and reduced productivity at work. Too few qualified professionals, inconsistent quality, inaccessibility, and stigma thwart traditional solutions. Innovations must be scalable, science-based, and timely.
Objective:
Limited access to quality mental health treatment harms health and quality of life while costing individuals and organizations millions in increased medical spending and reduced productivity at work. Too few qualified professionals, inconsistent quality, inaccessibility, and stigma thwart traditional solutions. Innovations must be scalable, science-based, and timely.
Methods:
We report analysis of coaches' response times to member messages, sentiment shift negative or positive (-1 to +1), machine learning-identified conversation topics, motivational interviewing adherence, and pre-post reductions in member-reported unhealthy days, distress, and presenteeism. The sample consisted of 38 members over a 4-month period who had at least four conversations over a minimum of 14 days. The results describe the innovative solution and illustrate the value delivered to members and organizations.
Results:
Data reported includes response time (median <132 sec), sentiment shift (57% positive), motivational interviewing adherence (>95%), as well as pre-post reductions in unhealthy days (25%), and presenteeism (23%).
Conclusions:
There are opportunities to utilize this emerging model of mental health care to address problems associated with traditional models that make them difficult to access and resource-heavy. This study provides data that describes and demonstrates a proof of concept for an innovative technology-enabled service that addresses the problems of scalability, access, quality, and stigma that have long challenged traditional mental health services. Clinical Trial: N/A
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Copyright
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