JMIR Publications

ABSTRACT

Background:

Chronic pain is a complex, multidimensional experience inadequately captured by traditional clinical measures. Understanding an individual's lived pain experience (LPE) is crucial for personalized treatment, but current methods for capturing LPEs—such as in-depth qualitative interviews—are resource-intensive and difficult to scale. Large language models (LLMs) offer a promising solution by enabling efficient, reproducible synthesis of personalized LPE narratives (LPE-Stories) from interview data and clinical records.

Objective:

This pilot study tests the feasibility and acceptability of an LLM-facilitated protocol to help individuals with chronic low back pain (cLBP) develop, validate, and refine a detailed, personally useful LPE-Story. Secondary objectives include evaluating whether LLM-empowered workflows can surface latent information not captured by traditional measures, and assessing practical benefits for participants, their healthcare providers, and their social support networks.

Methods:

This single-arm, mixed-methods pilot will recruit up to 30 cLBP participants (in groups of five) who recently completed a large observational study. Following an orientation session, participants complete 3 cycles of semi-structured Zoom interviews, each followed by LLM-assisted workflows. In Cycle 1, a workflow generates "Data-Story" from quantitative and categorical data; the participant critiques the Data-Story and begins sharing their LPE, which LLM workflows use to produce an enriched LPE-Story. Cycles 2 and 3 involve iterative review and refinement. An exit interview assesses accuracy, usefulness, and shareability of the final LPE-Story; feasibility is evaluated via engagement, satisfaction, and a 20-item questionnaire

Results:

We have received Institutional Review Board approval. Recruitment and workflow runs are un-derway; results to follow.

Conclusions:

This pilot will provide preliminary evidence on the feasibility, acceptability, and clinical utility of LLM-empowered workflows for capturing individualized LPEs. If successful, the approach could offer a scalable, reproducible method for generating rich, participant-validated pain narratives that complement traditional clinical measures—informing larger studies aimed at integrating LPE-Stories into clinical practice, enhancing patient-provider communication, and improving personalized pain management.