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https://www.selleckchem.com/EGFR(HER).html Data-driven tools are needed to inform individualized treatment decisions for people with type 2 diabetes (T2D). To show how treatment might be individualized, an interactive outline tool was developed to predict treatment outcomes. Individualized predictions were generated for change in HbA1c and body weight after initiation of newer antidiabetes drugs recommended by current guidelines. These predictions were based on data from randomized controlled trials of glucose-lowering drugs. The data included patient demographics and clinical characteristics (sex, age, body mass index, weight, diabetes duration, HbA1c level, current diabetes treatment and renal function). Predicted outcomes were determined using prespecified statistical models from original trial protocols and estimated coefficients for selected baseline characteristics. This prototype illustrates how evidence-based individualized treatment might be facilitated in the clinic for people with T2D. Further and ongoing development is required to improve the tool's prognostic value, including the addition of disease co-morbidities and patient-orientated outcomes. Patient engagement and data-sharing by sponsors of clinical trials, as well as real-world evidence, are needed to provide reliable predicted outcomes to inform shared patient-physician decision-making.We use administrative data from Medicare to document the massive consolidation of primary care physicians over the last decade and its impact on patient healthcare utilization. We first document that primary care organizations have consolidated all over the United States between 2008 and 2014. We then show that regions that experienced greater consolidation are associated with greater decline in overall healthcare spending. Finally, in our primary exercise, we exploit transitions of patients across organizations that are driven by changes in the organizational affiliations of their primary care physicians to study

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