AI-Assisted Thematic Analysis in a Quality Improvement Evaluation: Replicable Findings and Subtle Misrepresentations
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.
Authors
- Neva Howard (ORCID: https://orcid.org/0000-0003-1104-8555)
- Elena Broaddus (ORCID: https://orcid.org/0000-0003-4544-3529)
- Sandra P. Spencer (ORCID: https://orcid.org/0000-0001-5879-3195)
- Steven Lockhart
Institutions
- Outcomes Research Consortium (US)
- Children's Hospital Colorado (US)
- University of Colorado Anschutz Medical Campus (US)
- University of Colorado Denver (US)
Publication Details
- Journal
- American Journal of Medical Quality
- Published
- 2026-08-21
- DOI
- https://doi.org/10.1097/jmq.0000000000000341
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00