Currently submitted to: JMIR Research Protocols
Date Submitted: Jun 3, 2026
Open Peer Review Period: Jun 4, 2026 - Jul 30, 2026
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Giving Chronic Pain a Voice: Large Language Model-Assisted Workflows for Synthesizing Individualized Lived Pain Experience Stories
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.
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