Authors
Daniel I Hoffman, Sydney Moore, Christina Sheu, Mengyuan Ruan, Sabina Iqbal, Kate Sciacca, Masami Tabata-Kelly, Zara Cooper, Charlotta Lindvall
Published in
Journal of the American Geriatrics Society. Aug 12, 2026. Epub Aug 12, 2026.
Abstract
Social isolation is increasingly prevalent among older adults and is associated with adverse health outcomes, yet its impact in surgical populations remains poorly defined. Lack of systematic measurement during surgical care has limited large-scale investigation of social isolation and its associations with postoperative outcomes.
We conducted a prospective validation study of adults ≥ 65 years old admitted to inpatient surgical services at an academic medical center to evaluate the accuracy of a semi-automated, rule-based natural language processing (NLP) algorithm for identifying social isolation from routine electronic health record (EHR) clinical notes from index hospitalizations. NLP performance was validated against a 6-item Social Isolation Index administered in-person during hospitalization. The primary outcome was sensitivity of the NLP algorithm for identifying social isolation.
Among 249 enrolled patients (median age, 74 years; 55% female), 55 (22.1%) were classified as socially isolated by the patient-reported reference standard, and the NLP algorithm identified social isolation in 47 patients (18.9%). Sensitivity was 0.84 (95% CI, 0.71-0.92); specificity was 0.99 (95% CI, 0.97-1.00); positive predictive value was 0.98 (95% CI, 0.89-1.00); negative predictive value was 0.96 (95% CI, 0.92-0.98); accuracy was 0.96 (95% CI, 0.93-0.98); F1 score was 0.90 (95% CI, 0.84-0.96).
In this prospective validation study of older surgical patients, a semi-automated, rule-based NLP algorithm demonstrated strong performance in identifying social isolation using routine EHR documentation. With further automation and external validation, EHR-based identification of social isolation could complement embedded screening tools in perioperative care strategies.
PMID:
42585600
Bibliographic data and abstract were imported from PubMed on 13 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 12
- Comments 0