AI-Enabled Analysis of Functional Status Documentation Patterns and Decline Trajectories in Lung Cancer

Alaa Albashayreh; Yuya Hagiwara; William Zeitler; Stephanie Gilbertson-White

doi:10.1016/j.jpainsymman.2026.04.180

Background Functional performance scales guide critical decisions in oncology, including prognostication and palliative care referrals [1]. Early identification of functional decline is critical for timely supportive care and improved quality of life, yet inconsistent documentation and limited visibility into decline patterns hinder proactive management [2]. Scalable, AI-enabled extraction from clinical notes may help address these gaps and support needs-based prognostication [3]. Objectives To characterize functional status documentation patterns by clinical service and identify trajectories of decline in lung cancer patients using automated extraction from clinical notes. Methods We applied a validated AI-based extraction system (FuncStatAI, accuracy=83%) to 87,006 clinical notes from 2,342 lung cancer patients, extracting ECOG, KPS, and PPS scores from 56% of notes. We calculated patient-level decline trajectories using linear regression, measured time to severe impairment, and analyzed scale preference by clinical service. Results The cohort included 2,342 patients (mean age 63, 45% female, 41% stage IV). Among 911 patients with trajectory data (median 17 assessments over 1.2 years), 9% showed rapid decline (>0.5 points/day on normalized 0–100 scale), losing median 50 points over 51 days. Among 1,068 patients progressing to severe status, 25% reached this threshold within 29 days (median 174 days). Overall, 47% experienced decline, with 28% declining from independence to severe impairment. Most patients (83%) had multiple scales documented; among 1,522 with longitudinal data, 58% showed consistent trajectories across scales (median slope difference: 0.08 points/day). Scale preference varied by specialty (p< 0.0001): palliative care predominantly used PPS (88%), medical oncology used ECOG (82%), and radiation oncology used KPS (96%). Implications Moderate trajectory consistency (58%) across scales supports normalized comparisons despite specialty-specific documentation preferences. Early identification of rapid decline (9% of patients) and narrow windows to severe impairment (25% within one month) underscore the need for automated trajectory-based surveillance to enable timely, proactive palliative care referrals.

AI-Enabled Analysis of Functional Status Documentation Patterns and Decline Trajectories in Lung Cancer

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