Objectives This study presents a protocol that integrates conversational artificial intelligence into qualitative data analysis to support rapid, decision-oriented descriptive analysis in public health settings. The protocol was developed during an applied project with Hamilton County Public Health that analyzed interviews with the next of kin of overdose decedents to inform local strategies. The objective is to describe the protocol, its safeguards for data familiarization and human verification, and its practical application in a real-world case. Methods Evaluators designed and tested manual coding, intentional artificial intelligence-assisted coding, and conversational artificial intelligence within ATLAS.ti, selecting the conversational approach for the protocol. The protocol requires a mandatory pre-analysis familiarization phase that includes reading a stratified subset of transcripts and drafting immersion memos. Analysts then pose structured natural language queries tied to prespecified research questions. All outputs are treated as proposals and undergo required human verification, including confirmation of quoted evidence and contextual review. Theme-level benchmarking compared independent human synthesis with conversational artificial intelligence outputs. Results Conversational artificial intelligence produced rapid descriptive findings anchored to verifiable text, enabling efficient auditing through embedded links. Theme-level comparison showed conceptual overlap between human and artificial intelligence outputs, with transparent documentation of areas of divergence. The protocol supported rapid training of local personnel and sustained in-house analysis capacity. Conclusion The protocol formalizes a pragmatic workflow for question-led, top-down descriptive analysis using conversational artificial intelligence with mandatory human oversight. It is not intended to replace interpretive or theory-generating approaches but offers a transparent and scalable option for time sensitive, decision-focused qualitative work.
Belkin et al. (2026) studied this question.