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      Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

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      New England Journal of Medicine
      Massachusetts Medical Society

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          Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals

          Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun.
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            Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets.

            Determine how varying longitudinal historical training data can impact prediction of future clinical decisions. Estimate the "decay rate" of clinical data source relevance.
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              Author and article information

              Journal
              New England Journal of Medicine
              N Engl J Med
              Massachusetts Medical Society
              0028-4793
              1533-4406
              June 29 2017
              June 29 2017
              : 376
              : 26
              : 2507-2509
              Article
              10.1056/NEJMp1702071
              5953825
              28657867
              40c8a86a-eaca-4dc1-86bf-c1eed57bd241
              © 2017
              History

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