Patient-aligned Care Team Engagement to Connect Veterans Experiencing Homelessness With Appropriate Health Care.

BACKGROUND: Veterans experiencing homelessness frequently use emergency and urgent care (ED). OBJECTIVE: To examine the effect of a Patient-aligned Care Team (PACT) model tailored to the unique needs of Veterans experiencing homelessness (H-PACT) on frequency and type of ED visits in Veterans Health Administration (VHA) medical facilities. RESEARCH DESIGN: During a 12-month period, ED visits for 3981 homeless Veterans enrolled in (1) H-PACT at 20 VHA medical centers (enrolled) were compared with those of (2) 24,363 homeless Veterans not enrolled in H-PACT at the same sites (nonenrolled), and (3) 23,542 homeless Veterans at 12 non-H-PACT sites (usual care) using a difference-in-differences approach. MEASURE(S): The primary outcome was ED and other health care utilization and the secondary outcome was emergent (not preventable/avoidable) ED visits. RESULTS: H-PACT enrollees were predominantly white males with a higher baseline Charlson comorbidity index. In comparing H-PACT enrollees with usual care, there was a significant decrease in ED usage among the highest ED utilizers (difference-in-differences, -4.43; P<0.001). The decrease in ED visits were significant though less intense for H-PACT enrollees versus nonenrolled (-0.29, P<0.001). H-PACT enrollees demonstrated a significant increase in the proportion of ED care visits that were not preventable/avoidable in the 6 months after enrollment, but had stable rates of primary care, mental health, social work, and substance abuse visits over the 12 months. CONCLUSIONS: Primary care treatment engagement can reduce ED visits and increase appropriate use of ED services in VHA for Veterans experiencing homelessness, especially in the highest ED utilizers.

Translation of Contextual Control Model to chronic disease management: A paradigm to guide design of cognitive support systems.

BACKGROUND: Electronic health records (EHRs) continue to be criticized for providing poor cognitive support. Defining cognitive support has lacked theoretical foundation. We developed a measurement model of cognitive support based on the Contextual Control Model (COCOM), which describes control characteristics of an "orderly" joint system and proposes 4 levels of control: scrambled, opportunistic, tactical, and strategic. METHODS: 35 clinicians (5 centers) were interviewed pre and post outpatient clinical visits and audiotaped during the visit. Behaviors pertaining to hypertension management were systematically mapped to the COCOM control characteristics of: (1) time horizon, (2) uncertainty assessment, (3) consideration of multiple goals, (4) causal model described, and (5) explicitness of plan. Each encounter was classified for overall mode of control. Visits with deviation versus no deviation from hypertension goals were compared. RESULTS: Reviewer agreement was high. Control characteristics differed significantly between deviation groups (Wilcox rank sum p<.01). K-means cluster analysis of control characteristics, stratified by deviation were distinct, with higher goal deviations associated with more control characteristics. CONCLUSION: The COCOM control characteristics appear to be areas of potential yield for improved user-experience design.

Pediatric surgical border health: Supply may not be meeting demand in South Texas communities.

INTRODUCTION: The Rio Grande Valley (RGV) has historically high incidence of congenital defects (CDs) necessitating intervention by pediatric surgical specialties. We examined mortality in this region and related workforce patterns. METHODS: Mortality data related to CDs (2007-2021) and surgical workforce trends/projections (2024-2032) were collected using multiple databases: National Vital Statistics System, Texas Department of State Health Services, Texas Medical Board, Center for Disease Control. RESULTS: Nationally, RGV counties rank as high as 5th in CD mortality rates. Between 2020 and 2024, 3 of 4 studied pediatric surgical specialties experienced stagnant/decreasing workforces ranging from 0 to 66 â€‹%. Furthermore, the RGV is projected to have some of the most marked state-wide disparities in surgical providers over the next 8 years. CONCLUSION: High infant mortality rates along with ongoing and future shortages of surgical specialists is concerning. These results may inform allocation of public health resources and workforce distribution to improve outcomes.

Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure.

OBJECTIVES: Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. MATERIALS AND METHODS: We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. RESULTS: System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). DISCUSSION: An EF value of <40% can be accurately identified in VA echocardiogram reports. CONCLUSIONS: An automated information extraction system can be used to accurately extract EF for quality measurement.