UCLA

Background

In 2020, the Veterans' Affairs (VA) healthcare system deployed a heart failure (HF) dashboard for use nationally. The initial version was notably imprecise and unreliable for the identification of HF subtypes. We describe the development and subsequent optimization of the VA national HF dashboard.

Materials and methods

This study describes the stepwise process for improving the accuracy of the VA national HF dashboard, including defining the initial dashboard, improvement of case definitions, utilization of natural language processing for patient identification, and incorporation of an imaging quality hierarchy model. Optimization further included evaluating whether to require concurrent ICD-codes for inclusion in the dashboard and assessing various imaging modalities for patient characterization.

Results

Through multiple rounds of optimization, the dashboard accuracy (defined as the proportion of true results to the total population) was improved from 54.1% to 89.2% for the identification of HF with reduced ejection fraction (HFrEF) and from 53.9% to 88.0% for the identification of HF with preserved ejection fraction (HFpEF). To align with current guidelines, HF with mildly reduced ejection fraction (HFmrEF) was added to the dashboard output with 88.0% accuracy.

Conclusions

The inclusion of an imaging quality hierarchy model and natural language processing algorithm improved the accuracy of the VA national HF dashboard. The revised dashboard informatics algorithm has higher utilization rates and improved reliability for population health management.