Using data from a statewide elderly population (aged 65 years and older), we sought to prospectively validate an algorithm to identify patients at risk for dying in the next year for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment.
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.
The model identified 72.90% of high-risk patients at least 3 months before the confirmatory diagnosis was made by physicians. Of those patients, 41.02% had diabetes or an abnormal glucose test result at the time they were identified at high risk for CKD.
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.
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.
Summary: Estimating patient risk of future emergency department (ED) revisits can guide the allocation of resources, e.g. local primary care and/or specialty, to better manage ED high utilization patient populations and thereby improve patient life qualities.