The American journal of managed care
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As predictive analytics are increasingly used and developed by health care systems, recognition of the threat posed by bias has grown along with concerns about how providers can make informed decisions related to predictive models. To facilitate informed decision-making around the use of these models and limit the reification of bias, this study aimed to (1) identify user requirements for informed decision-making and utilization of predictive models and (2) anticipate and reflect equity concerns in the information provided about models. ⋯ Health systems should provide key information about predictive models to clinicians and other users to facilitate informed decision-making about the use of these models. Implementation efforts should also expand to routinely incorporate equity considerations from inception through the model development process.
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The tropomyosin receptor kinase (TRK) family of proteins is encoded by neurotrophic tyrosine receptor kinase (NTRK) genes and has a role in the development and normal functioning of the nervous system. NTRK gene fusions have been identified as oncogenic drivers in a wide range of tumors in both adult and pediatric patients. ⋯ The development of drugs that specifically target oncogenic drivers of cancer has led to the emergence of screening technologies to identify the patients most likely to benefit from targeted therapy. This review describes the role of NTRK gene fusions in cancer and outlines the epidemiology of NTRK gene fusions, the therapeutic benefits of targeting TRK fusions with small molecule inhibitors, and recommendations for NTRK gene fusion testing in adult and pediatric patients with cancer, in order to guide treatment decisions.
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Computable social risk factor phenotypes derived from routinely collected structured electronic health record (EHR) or health information exchange (HIE) data may represent a feasible and robust approach to measuring social factors. This study convened an expert panel to identify and assess the quality of individual EHR and HIE structured data elements that could be used as components in future computable social risk factor phenotypes. ⋯ Routinely collected structured data within EHR and HIE systems may reflect patient social risk factors. Identifying and assessing available data elements serves as a foundational step toward developing future computable social factor phenotypes.
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To characterize factors influencing the development and sustainability of data sharing in the Mid-Ohio Farmacy (MOF), a produce referral program implemented in partnership between a community-based organization (the Mid-Ohio Food Collective ["Food Collective"]) and an academic medical center (The Ohio State University Wexner Medical Center [OSUWMC]). ⋯ Our findings suggest that current regulatory frameworks are misspecified to the growing interest in cross-sector partnerships between health care and community-based organizations. Future efforts to support these relationships should consider clarifying rules around data sharing and increasing Medicaid support for nonmedical, health-related social needs.
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To determine the degree of telemedicine expansion overall and across patient subpopulations and diagnoses. We hypothesized that telemedicine visits would increase substantially due to the need for continuity of care despite the disruptive effects of COVID-19. ⋯ Telemedicine expanded rapidly during the COVID-19 pandemic across a broad range of clinical conditions and demographics. Although levels declined later in 2020, telemedicine utilization remained markedly higher than 2019 and 2018 levels. Trends suggest that telemedicine will likely play a key role in postpandemic care delivery.