The Transforming Outcomes for Patients Through Medical Home Evaluation and reDesign (TOPMED) Cluster Randomized Controlled Trial: Cost and Utilization Results.

BACKGROUND: Primary Care Medical Home (PCMH) redesign efforts are intended to enhance primary care's ability to improve population health and well-being. PCMH transformation that is focused on "high-value elements" (HVEs) for cost and utilization may improve effectiveness. OBJECTIVES: The objective of this study was to determine if a focus on achieving HVEs extracted from successful primary care transformation models would reduce cost and utilization as compared with a focus on achieving PCMH quality improvement goals. RESEARCH DESIGN: A stratified, cluster randomized controlled trial with 2 arms. All practices received equal financial incentives, health information technology support, and in-person practice facilitation. Analyses consisted of multivariable modeling, adjusting for the cluster, with difference-in-difference results. SUBJECTS: Eight primary care clinics that were engaged in PCMH reform. MEASURES: We examined: (1) total claims payments; (2) emergency department (ED) visits; and (3) hospitalizations among patients during baseline and intervention years. RESULTS: In total, 16,099 patients met the inclusion criteria. Intervention clinics had significantly lower baseline ED visits (P=0.02) and claims paid (P=0.01). Difference-in-difference showed a decrease in ED visits greater in control than intervention (ED per 1000 patients: +56; 95% confidence interval: +96, +15) with a trend towards decreased hospitalizations in intervention (-15; 95% confidence interval: -52, +21). Costs were not different. In modeling monthly outcome means, the generalized linear mixed model showed significant differences for hospitalizations during the intervention year (P=0.03). DISCUSSION: The trial had a trend of decreasing hospitalizations, increased ED visits, and no change in costs in the HVE versus quality improvement arms.

Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing.

Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.