Demo: SNAQ: Quantifying Patient Narratives to Strengthen Longitudinal Assessment in Chronic Pain

Abstract

Chronic pain assessment relies heavily on patients' descriptions of their symptoms, yet these narrative reports are difficult for clinicians to interpret consistently and are rarely incorporated into structured monitoring tools. The purpose of this research was to develop a computational system, SNAQ (System of Narrative Aspect Quantification), that translates patient narratives into quantitative indicators that can be tracked over time alongside standard symptom ratings. SNAQ uses natural language processing, specifically aspect-based sentiment analysis, to identify meaningful themes in patients' written descriptions and classify them using the World Health Organization's International Classification of Functioning (ICF). These sentiment-based measures are then combined with normalized 0–10 symptom scales to produce a single Wellness Index ranging from 0 to 100. We evaluated SNAQ using a synthetic dataset modeled on real fibromyalgia narratives and a six-month, 50-entry longitudinal case. SNAQ accurately identified functional themes and emotional tone in narratives, showing strong agreement with human reviewers (F1 = 0.855, κ = 0.673), and produced a stable index that reflected realistic patterns of symptom flare-ups and recovery. SNAQ is now being clinically validated on real patient data in an orthopedic practice, where its index is compared against an established PROM across multiple visits per patients to asses concurrent validity and response to clinical change. The study demonstrates that SNAQ functions as intended: it reliably extracts measurable signals from patient narratives and integrates them with symptom scales into a stable, clinically plausible Wellness Index, offering a transparent, interpretable tool for both patients and clinicians.