Dashboards to Improve Extractability of Cardiovascular Indicators in a Learning Health Care System: Mixed Methods...

Area of Science:

• Health Informatics
• Cardiovascular Medicine
• Quality Improvement

Background:

• Cardiovascular risk management (CVRM) guidelines aim to manage patients at high cardiovascular risk.
• Adherence to CVRM guidelines is variable, necessitating improved healthcare practices.
• Learning health care systems (LHS) and dashboards are potential tools to enhance adherence and data registration.

Purpose of the Study:

• To evaluate if implementing dashboards in an LHS improved the structured registration of CVRM indicators in electronic health records (EHRs).
• To assess the extractability of key CVRM indicators (BMI, blood pressure, smoking status, CVD history, lipids, HbA1c, hemoglobin, eGFR) in a real-world setting.

Main Methods:

• A mixed-methods study at University Medical Center Utrecht (Jan 2022-Nov 2023) during dashboard implementation.
• Quantitative assessment of CVRM indicator extractability from EHRs, comparing periods with and without dashboards.
• Thematic analysis of semistructured interviews with clinicians (N=5) to understand perceptions and barriers.

Main Results:

• Dashboard implementation did not improve the extractability of CVRM indicators; registration remained low and stable.
• Hemoglobin and estimated glomerular filtration rate were most extractable; CVD history and smoking status were least extractable.
• Compared to a protocolized period, indicator extractability decreased by up to 45%, attributed to unclear responsibilities, EHR limitations, and time constraints.

Conclusions:

• Dashboards failed to enhance structured registration of CVRM indicators in EHRs.
• Organizational, technical, and operational issues impede the effectiveness of dashboards in improving CVRM data capture.
• Findings offer guidance for improving CVRM indicator extractability, benefiting clinical practice and real-world data research.