Harper, Sarah B

Introduction/Objectives The rise in screening for social drivers of health (SDoH) in primary care settings has led to questions about effective intervention design. A large primary care practice at Mayo Clinic developed a model centered on a full-time Health Equity Coordinator (HEC) to facilitate clinic-community partnerships in 2022. The goal of this analysis was to assess the model on process outcomes, resolution of SDoH needs, and changes in healthcare utilization. Methods Using a cohort study design, we identified primary care patients with unmet SDoH needs at the intervention clinic and two comparator clinics from March 2022 to December 2023. We assessed process outcomes descriptively and resolution of SDoH needs and healthcare utilization using multivariable regression. Results The HEC contacted 348 patients and conducted 748 meetings with community-based organization staff. While there were decreases for SDoH needs and missed or late cancelled appointments in intervention group, there were no significant difference in resolution of SDoH and healthcare utilization relative to comparator groups. Conclusions The HEC model for multi-stakeholder coordination showed promise in referring patients to services and engaging key stakeholders in a primary care setting. Longer term follow up may be needed to identify changes in SDoH needs and healthcare utilization.

INTRODUCTION: High-quality colonoscopy requires accurate risk stratification for surveillance per the 2020 US Multi-Society Task Force guidelines and adherence to 2024 American College of Gastroenterology/American Society for Gastrointestinal Endoscopy quality benchmarks. Both are operationally challenging in clinical practice. We developed and validated Colon-Pilot, a large language model—powered clinical decision support system using GPT-4o to automate and standardize both functions. METHODS: The system was evaluated in 2 operational modes: (i) a human-in-the-loop clinical decision support validation of surveillance recommendations for 596 colonoscopies, comparing concordance with 2020 US Multi-Society Task Force guidelines against expert consensus, and (ii) an automated administrative audit applying Colon-Pilot to 42,632 colonoscopies across the Mayo Clinic Health System to calculate 2024 American College of Gastroenterology/American Society for Gastrointestinal Endoscopy priority quality indicators. Recommendations were autogenerated unless predefined safety criteria triggered manual review. RESULTS: Colon-Pilot issued recommendations for 522 of 596 cases (87.6%) and flagged 12.4% for manual review. For automated cases, guideline-concordant accuracy was 97.5% (Cohen κ = 0.970) versus 69.7% (κ = 0.781) for original endoscopist recommendations. Discordant artificial intelligence (AI) cases (n = 13) most often recommended longer-than-appropriate intervals (62%). Applied to the enterprise data set, Colon-Pilot calculated performance exceeding 2024 targets: adenoma detection rate 49.8% (≥35%), sessile serrated lesion detection rate 17.7% (≥6%), bowel preparation adequacy 91.8% (≥90%), and cecal intubation rate 97.4% (≥95%). DISCUSSION: Colon-Pilot demonstrated high fidelity in applying surveillance guidelines and automated quality benchmarking, outperforming unassisted endoscopists in guideline adherence. By combining safety protocols with large-scale automated reporting, it offers a scalable solution for improving both efficiency and quality in colorectal cancer prevention.