The Latest in Predictive Analytics

Prediction of Incident Hypertension Within the Next Year

As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year.

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Defining and characterizing the critical transition state prior to the type 2 diabetes disease

Summary: Analysis of a patient’s pre-disease clinical history can provide early warning of an impending Type 2 Diabetes diagnosis. This allows providers to take proactive steps to prevent or delay onset of the disease. In this study, HBI used longitudinal EHR data from the Maine State Health Information Exchange to identify a dynamic driver network (DDN) and associated critical transition state six months prior to diagnosis.

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Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing–Based Algorithm With Statewide Electronic Medical Records

Summary: Diabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency.

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Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in Maine Health Information Exchange

Abstract: Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups.

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