NCI Data Jamboree (Project Abstract Submission): Submission #56

Presenter Information
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First Name: Nora
Middle Initial: L.
Last Name: Nock
Degree(s): Ph.D., MS, BS, PE, FSBM
Position/Title/Career Status: Professor
Organization: Case Western Reserve University
Organization Address:
Cleveland

Email: nln@case.edu

Additional Authors
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List of Additional Authors:
- First Name: Mireya
  Last Name: Diaz-Insua
  Post-nominal letters: PhD
  Affiliation: Case Western Reserve University
- First Name: Siran
  Last Name: Koroukian
  Post-nominal letters: PhD
  Affiliation: Case Western Reserve University
- First Name: Johnie
  Last Name: Rose
  Post-nominal letters: MD, PhD
  Affiliation: Case Western Reserve University


Abstract Information
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Abstract Category: Evaluating data quality for reproducibility and AI-readiness
Abstract Keywords: AI, qualitative data, lived experience, interviews, focus groups
Abstract Title: Leveraging AI to Augment Human Expertise and Enhance Efficiency in Synthesizing and Analyzing Lived Experience Qualitative Data in Cancer Research
Abstract:
Lived experience obtained from cancer patients, caregivers, families and providers can help integrate cultural context, optimize intervention design, improve clinical outcomes and better understand attitudes, barriers and facilitators to widespread dissemination and implementation efforts. The increased use of qualitative methods in cancer research has created an imminent need to develop more standardized workflows and methods to comprehensively and efficiently synthesize and analyze qualitative data. Leveraging AI to synthesize lived experience qualitative data may help improve efficiency in the arduous and time consuming process of coding and thematic analyses and, potentially uncover new insights not initially observed. We propose to synthesize existing guidebooks and raw transcript text data from interviews and focus groups in cancer patients obtained from multiple sources such as published manuscript supplemental files, National Cancer Institute (NCI)-Designated Comprehensive Cancer Centers patient “story” webpages, and other open access platforms with relevant transcript and guidebook data. We propose to use widely available AI tools (e.g., ChatGPT, Claude) as well as more sophisticated “deep” AI tools (e.g., BERT/BERT-Like) to synthesize transcript data, interview and focus group guides, create codebooks and conduct thematic analyses. We propose to evaluate performance using several measures including but not limited to accuracy and reliability/agreement in revealing codes, subcategories and themes compared to those derived from human experts, reflecting readability, lexical diversity, and coherence. We may also explore utilizing different command prompts from prompt engineering frameworks (e.g., RISEN: Role, Instructions, Steps, End Goal, and Narrowing) if time permits. We hypothesize that human interaction and expert oversight will be needed to add cultural context, judgement, and ethical responsibility. We have assembled a team with expertise in qualitative and mixed methods in cancer research, biostatistics, general AI and large databases but would be interested in having additional member(s), particularly those with advanced/deep AI tool expertise, join our team.