Original Article: AI-Assisted Thematic Analysis in a Quality Improvement Evaluation: Replicable Findings and Subtle Misrepresentations

“AI-Assisted Thematic Analysis in a Quality Improvement Evaluation: Replicable Findings and Subtle Misrepresentations.”

August 2026

AJMQ

Elena Broaddus, PhD, MSPH, Assistant Professor in the Department of Family Medicine (DFM) and methodologist in the Adult & Child Center for Outcomes Research & Delivery Science (ACCORDS) qualitative and mixed methods core, is the author of an American Journal of medical Quality publication title, “AI-Assisted Thematic Analysis in a Quality Improvement Evaluation: Replicable Findings and Subtle Misrepresentations.”

Dr. Broaddus explained that, "This study describes the exploratory work that we did using AI for a qualitative analysis and gives some concrete examples of how findings can go sideways without a lot of close human oversight."

From the article:

“Using artificial intelligence to identify themes in interview data from a quality improvement program evaluation produced 4 replicable themes grounded in the data. However, 2 consistently identified themes resulted from subtle misrepresentations and could have easily misled results without thorough data knowledge and output audit by the human research team.”

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