Comparison of approaches for heart failure case identification from electronic health record data

Abstract

Accurate, real-time case identification is needed to target interventions to improve quality and outcomes for hospitalized patients with heart failure. Problem lists may be useful for case identification but are often inaccurate or incomplete. Machine-learning approaches may improve accuracy of identification but can be limited by complexity of implementation. Our objective was to develop algorithms that use readily available clinical data to identify patients with heart failure while in the hospital. In this study of 47,119 hospitalizations, inclusion of heart failure on the problem list had a sensitivity of 0.40 and a positive predictive value (PPV) of 0.96. A logistic regression model with clinical data was associated with a sensitivity of 0.68 and PPV of 0.90, whereas a machine-learning algorithm that used free text had a sensitivity of 0.83 and a PPV of 0.90. The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.