HSS Presents New Research Leveraging AI to Uncover Insights Related to Pain Risk and Anesthesia Education at ASRA Annual Meeting

Why It Matters

Identifying biological and procedural predictors enables personalized pain‑management strategies, potentially reducing chronic pain incidence after knee replacement. Understanding patients’ online information needs helps clinicians address misconceptions and guide patients to reliable sources, improving shared decision‑making.

Key Takeaways

• ML models identified TARC cytokine as top predictor of chronic knee pain
• XGBoost outperformed other algorithms in predicting post‑operative pain risk
• Study analyzed 160 TKA patients, 318 clinical and biological variables
• AI search analysis showed 55% of top sites are academic sources
• Patient queries focus on risks, complications, and sedation details in regional anesthesia

Pulse Analysis

Artificial intelligence is reshaping peri‑operative care by turning massive clinical datasets into actionable insights. In orthopedics, HSS’s machine‑learning analysis of 160 total knee arthroplasty patients uncovered the chemokine TARC as a consistent harbinger of persistent pain, alongside pre‑operative pain scores and tourniquet duration. By leveraging XGBoost’s superior predictive power, clinicians can soon stratify patients by risk, tailor multimodal analgesia, and potentially curb the one‑in‑five incidence of chronic post‑surgical knee pain.

Patient education is another frontier where AI delivers measurable value. HSS’s systematic crawl of Google’s “People Also Ask” section revealed that 55% of the most cited webpages are academic, yet patients still grapple with nuanced concerns about anesthesia risks, sedation awareness, and nerve‑block specifics. By categorizing 1,400 question‑website pairs, the study highlights a gap between information accuracy and patient comprehension, urging anesthesiologists to pre‑emptively address these topics during limited pre‑operative visits.

The broader implication is a shift toward data‑driven, patient‑centric workflows. As predictive models become integrated into electronic health records, surgeons and anesthesiologists can receive real‑time risk scores, prompting early interventions. Simultaneously, AI‑curated search insights enable the creation of multilingual, tiered educational materials that align with the exact language patients use online. For institutions like HSS, these capabilities reinforce their leadership in musculoskeletal health while setting a benchmark for how AI can enhance both clinical outcomes and the patient experience.