The American journal of managed care
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To develop a text analytics methodology to analyze in a refined manner the drivers of primary care physicians' (PCPs') electronic health record (EHR) inbox work. ⋯ This study demonstrated that advanced text analytics provide a reliable data-driven methodology to understand the individual physician's EHR inbox management work with a significantly greater level of detail than previous approaches. This methodology can inform decision makers on appropriate workflow redesign to eliminate unnecessary workload on PCPs and to improve cost and quality of care, as well as staff work satisfaction.
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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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To assess the ability of accountable care organizations (ACOs) to use electronic health record (EHR) data for quality. ⋯ ACOs have diverse structures that often result in the usage of multiple EHR systems. This has the potential to cause serious delays when CMS begins requiring ACOs to report their quality measures through their EHRs in 2022.