JMIR Publications

ABSTRACT

Background:

Cardiovascular risk management (CVRM) guidelines have been developed for evaluation and management of all patients at higher cardiovascular risk, being either symptomatic or still asymptomatic. Although these exist already for long time, adherence varies. A learning healthcare system (LHS) could address adherence by continuously analyzing routine care data to inform and improve healthcare practice. Dashboards may be used to inform clinicians on the care provided and potentially improve structured registration of CVRM indicators in electronic health records (EHRs).

Objective:

Our aim was to evaluate whether the implementation of dashboards in our LHS has led to changes in the structured registration of cardiovascular indicators in patients with an increased risk of cardiovascular disease (CVD).

Methods:

In our mixed-methods study, patients who visited the UMC Utrecht between January 2022 and November 2023, the period in which the dashboard was implemented, were included. We assessed the extractability of the CVRM indicators (i.e., body mass index, blood pressure, smoking status, medical CVD history, lipid levels, glycated haemoglobin, haemoglobin, and the estimated glomerular filtration rate), stratified by department. We compared the extractability of the indicators with the extractability before the Utrecht Cardiovascular Cohort – Cardiovascular Risk Management (UCC-CVRM) LHS was initialized and with the period during which the UCC-CVRM was protocolized, yet without use of dashboards. To explain our quantitative findings and to gain a deeper understanding about how the dashboards were viewed and perceived, we conducted semi-structured interviews with clinicians and analyzed these thematically.

Results:

The extractability of CVRM indicators among 8941 first hospital visits remained low and stable during the period the dashboards were used. Compared to the protocolized UCC-CVRM, indicators were up to 45% less extractable meaning that CVRM indicators were less often registered in structured fields of the EHR. Interviews with clinicians (N=5) revealed that the low extractability could be attributed to unclear responsibility for CVRM, lack of harmonized agreements for registration in EHRs, perceived challenges related to the EHR system (e.g., some structured fields were not easily accessible), time constraints, and habits (e.g., maintaining habitual ways of working that are perceived to best suit their workflow).

Conclusions:

We found that dashboards did not improve registration of CVRM indicators in structured fields of the EHR. This was explained by perceived organizational, technical and operational issues. Our findings provide guidance on what aspects to consider for the extractability to be improved, which, in the end, will be beneficial for both clinical practice and scientific research using real-world data.