Equity in Digital Mental Health Interventions: Where to Next? (Preprint)

Abstract

UNSTRUCTURED Healthcare technologies have the ability to bridge or hinder equitable care. Advocates of digital mental health interventions (DMHIs) report that such technologies are poised to reduce the documented gross health care inequities that have plagued generations of people seeking care. This is due to a multitude of factors such as their potential to revolutionize access, mitigate logistical barriers to in-person mental health care, and leverage patient inputs to formulate tailored, responsive, and personalized experiences. While we agree with the potential of DMHIs to advance health equity, we articulate several steps essential to mobilize and sustain meaningful forward progression in this endeavor, reflecting on decades of research and learnings drawn from multiple fields of expertise and real world experience. First, DMHI manufacturers must build diversity, equity, inclusion, and belonging (DEIB) processes into the full spectrum of product evolution itself (e.g., product design, evidence generation) as well as into the fabric of internal company practices (e.g., talent recruitment, communication principles, and advisory boards). Next, awareness of the DEIB efforts – or lack thereof – in DMHI research trials is needed to refine and optimize future study design for inclusivity as well as proactively address potential barriers to doing so. Trials should incorporate thoughtful, inclusive and creative approaches to recruitment, enrollment and measurement of social-determinants of health and self identity, as well as a prioritization of planned and exploratory analyses examining outcomes across various groups of people. Third, mental health care advocacy, research funding policies, and local and federal legislation can advance these pursuits, with directives from the US Preventive Services Task Force, National Institutes of Health, Food and Drug Administration applied as poignant examples. For products with artificial intelligence/machine learning (AI/ML), maintaining a "human in the loop" as well as pre-specified and adaptive analytic frameworks to monitor and remediate potential algorithmic bias can reduce the risk for increasing inequity. Last, but certainly not least, is a call for partnership and transparency within and across ecosystems (academic, industry, payer, provider, regulatory agencies, value based care organizations) to reliably build health equity into real-world DMHI product deployments and evidence generation strategies. All these considerations should also extend into the context of an equity-informed commercial strategy for DMHI manufacturers and healthcare organizations alike. The potential to advance health equity in innovation with DMHI is apparent. We advocate the field's thoughtful and evergreen advancement in inclusivity, thereby redefining the mental health care experience for this generation and those to come.