Project to Use EHRs to Predict Heart Failure in Advance
Sutter Health in California and Geisinger Health System in Pennsylvania will join with IBM Corp. to develop data analytics technology that will pull information from electronic health records systems to detect heart failure years sooner than now possible.
Sutter Health in California and Geisinger Health System in Pennsylvania will join with IBM Corp. to develop data analytics technology that will pull information from electronic health records systems to detect heart failure years sooner than now possible.
The program is funded under a $2 million grant from the National Institutes of Health. Sutter Health and Geisinger Health both use EHRs from Epic Systems Corp. In addition to learning how data within the EHR can help detect patters indicative of heart failure years in advance, the program will identify best practices for providers across the nation to integrate data analytics into primary care and develop personalized treatment plans.
The three organizations began initial research in 2009 and have published findings on predictive modeling using EHR data and the challenges, automated identification of heart failure diagnostic criteria using text analysis of clinical notes, and identifying risk factors using EHR data.
The new funding enables deeper looks into the progression of factors that are predictors of heart failure. Sutter Health and Geisinger will test predictive methods in clinical practice for the next several years. The hope is that this research will lead to using EHR data to similarly predict other chronic conditions.
The program is funded under a $2 million grant from the National Institutes of Health. Sutter Health and Geisinger Health both use EHRs from Epic Systems Corp. In addition to learning how data within the EHR can help detect patters indicative of heart failure years in advance, the program will identify best practices for providers across the nation to integrate data analytics into primary care and develop personalized treatment plans.
The three organizations began initial research in 2009 and have published findings on predictive modeling using EHR data and the challenges, automated identification of heart failure diagnostic criteria using text analysis of clinical notes, and identifying risk factors using EHR data.
The new funding enables deeper looks into the progression of factors that are predictors of heart failure. Sutter Health and Geisinger will test predictive methods in clinical practice for the next several years. The hope is that this research will lead to using EHR data to similarly predict other chronic conditions.
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