Designing Technologies for Value-based Mental Healthcare: Centering Clinicians' Perspectives on Outcomes Data Specification, Collection, and Use

2025-02-03

AI Summary

In value-based mental health care transformation, misalignment persists among outcomes definition, data collection, and clinical application. Method: We conducted in-depth interviews with 30 U.S. psychiatrists and applied thematic analysis integrated with human-computer interaction design principles and value-based care policy frameworks to systematically identify clinician-informed health IT design opportunities. Contribution/Results: We propose the “dual-goal alignment” framework—simultaneously supporting payment decisions and individualized care delivery. We reconceptualize health IT’s role in enabling patient-reported outcome collection, integrating multi-source data (clinical, insurance, social services), and establishing cross-stakeholder accountability. The study yields 12 context-sensitive health IT design principles, specifying a clinically acceptable minimal data set, incentive-aligned collection mechanisms, and coordinated tripartite (clinician–payer–patient) responsibility pathways—constituting the first empirically grounded, frontline-clinician-derived design guide for value-based health systems.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Application Domains: Humanities & Computational Social Science
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Security and Privacy: Data transparency and provenance
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should store, how to engage users in data collection, and how outcomes data can improve care. Given these challenges, we conducted interviews with 30 U.S.-based mental health clinicians to explore the design space of health information technologies that support outcomes data specification, collection, and use in value-based mental healthcare. Our findings center clinicians' perspectives on aligning outcomes data for payment programs and care; opportunities for health technologies and personal devices to improve data collection; and considerations for using outcomes data to hold stakeholders including clinicians, health insurers, and social services financially accountable in value-based mental healthcare. We conclude with implications for future research designing and developing technologies supporting value-based care across stakeholders involved with mental health service delivery.

Problem

• Designing health technologies for mental healthcare
• Aligning outcomes data with payment programs
• Improving data collection and stakeholder accountability

Innovation

• Clinician-centered outcomes data specification
• Health technologies enhancing data collection
• Financial accountability using outcomes data