Heart Failure Dashboard Design and Validation to Improve Care of Veterans

Abstract

Background  Early electronic identification of patients at the highest risk for heart failure (HF) readmission presents a challenge. Data needed to identify HF patients are in a variety of areas in the electronic medical record (EMR) and in different formats.

Objective  The purpose of this paper is to describe the development and data validation of a HF dashboard that monitors the overall metrics of outcomes and treatments of the veteran patient population with HF and enhancing the use of guideline-directed pharmacologic therapies.

Methods  We constructed a dashboard that included several data points: care assessment need score; ejection fraction (EF); medication concordance; laboratory tests; history of HF; and specified comorbidities based on International Classification of Disease (ICD), ninth and tenth codes. Data validation testing with user test scripts was utilized to ensure output accuracy of the dashboard. Nine providers and key senior management participated in data validation.

Results  A total of 43 medical records were reviewed and 66 HF dashboard data discrepancies were identified during development. Discrepancies identified included: generation of multiple EF values on a few patients, missing or incorrect ICD codes, laboratory omission, incorrect medication issue dates, patients incorrectly noted as nonconcordant for medications, and incorrect dates of last cardiology appointments. Continuous integration and builds identified defects—an important process of the verification and validation of biomedical software. Data validation and technical limitations are some challenges that were encountered during dashboard development. Evaluations by testers and their focused feedback contributed to the lessons learned from the challenges.

Conclusion  Continuous refinement with input from multiple levels of stakeholders is crucial to development of clinically useful dashboards. Extraction of all relevant information from EMRs, including the use of natural language processing, is crucial to development of dashboards that will help improve care of individual patients and populations.