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.

Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis.

OBJECTIVE: This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools. METHODS: We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS. RESULTS: We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9-11.2%), low exclusion (0-0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting. DISCUSSION: Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases. CONCLUSION: Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.

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.

Persistent effects of acute stress on fear and drug-seeking in a novel model of the comorbidity between post-traumatic stress disorder and addiction.

Even following long periods of abstinence, individuals with anxiety disorders have high rates of relapse to drugs of abuse. Although many current models of relapse demonstrate effects of acute stress on drug-seeking, most of these studies examine stressful experiences that occur in close temporal and physical proximity to the reinstatement test. Here, we assess the effects of a stressful experience in one context on fear and drug-seeking in a different context. We adapt the stress-enhanced fear learning procedure to examine impacts on drug-seeking long after the stressful experience occurred. We find massive footshock in a distinct environment produced an acute increase in corticosterone, long-term hyper-responsivity to a single shock in different contexts with extensive histories of drug-seeking behaviors, enhancements in cocaine-induced conditioned place preference in mice, and persistent enhancements in cue-induced reinstatement of methamphetamine-seeking behavior in rats. Together, these experiments demonstrate that an acute trauma causes persistent changes in responsivity to mild stressors and drug-seeking behavior in other contexts, which mirrors aspects of the comorbidity between post-traumatic stress disorder and substance use disorders. These behavioral approaches provide novel procedures for investigating basic mechanisms underlying this comorbidity and they provide powerful tools for testing preclinical pharmacological and behavioral interventions.

Mammalian heart renewal by pre-existing cardiomyocytes.

Although recent studies have revealed that heart cells are generated in adult mammals, the frequency of generation and the source of new heart cells are not yet known. Some studies suggest a high rate of stem cell activity with differentiation of progenitors to cardiomyocytes. Other studies suggest that new cardiomyocytes are born at a very low rate, and that they may be derived from the division of pre-existing cardiomyocytes. Here we show, by combining two different pulse-chase approaches--genetic fate-mapping with stable isotope labelling, and multi-isotope imaging mass spectrometry--that the genesis of cardiomyocytes occurs at a low rate by the division of pre-existing cardiomyocytes during normal ageing, a process that increases adjacent to areas of myocardial injury. We found that cell cycle activity during normal ageing and after injury led to polyploidy and multinucleation, but also to new diploid, mononucleate cardiomyocytes. These data reveal pre-existing cardiomyocytes as the dominant source of cardiomyocyte replacement in normal mammalian myocardial homeostasis as well as after myocardial injury.