Perspectives on Healthcare AI Policy from Health Equity Professionals

United States-based qualitative interview study with 22 professionals working on health disparities and equity. The research, authored by Kadija Ferryman, explores how health AI policy could be developed to address structural issues contributing to health disparities. The findings were published on figshare in May 2026.

Use Cases

• Analyzing stakeholder perspectives on data governance for health AI based on the 'developing health AI policy for health equity starts with data' theme
• Studying policy strategies for multi-institutional collaboration based on the theme that policy 'must include multiple institutions and strategies'
• Investigating economic considerations for equitable AI policy based on the 'considering economic issues is key' theme

Strengths

• Dataset is based on 22 semi-structured interviews, providing a substantive qualitative foundation
• The study is explicitly focused on a novel and policy-relevant group: health equity professionals
• The interview guide was developed using a recognized framework (Michener's Racial Equity Framework for Assessing Health Policy)

Limitations

• Dataset is a 70.0 KB PDF document containing analysis, not raw data; row count and column-level documentation are absent
• The data consists of summarized themes from interviews, not the primary interview transcripts or coded data
• Freshness should be verified as the last update date is in the future (2026-05-07)

Provenance

Source

figshare

Collection Method

Semi-structured qualitative interviews

Freshness

Last updated 2026-05-07 05:30:52

Geography

United States