Impact of electronic clinical decision support on adherence to guideline-recommended treatment for hyperlipidaemia, atrial fibrillation and heart failure: protocol for a cluster randomised trial

Abstract

Introduction: Clinical practice guidelines facilitate optimal clinical practice. Point of care access, interpretation and application of such guidelines, however, is inconsistent. Informatics-based tools may help clinicians apply guidelines more consistently. We have developed a novel clinical decision support tool that presents guideline-relevant information and actionable items to clinicians at the point of care. We aim to test whether this tool improves the management of hyperlipidaemia, atrial fibrillation and heart failure by primary care clinicians.

Methods/analysis: Clinician care teams were cluster randomised to receive access to the clinical decision support tool or passive access to institutional guidelines on 16 May 2016. The trial began on 1 June 2016 when access to the tool was granted to the intervention clinicians. The trial will be run for 6 months to ensure a sufficient number of patient encounters to achieve 80% power to detect a twofold increase in the primary outcome at the 0.05 level of significance. The primary outcome measure will be the percentage of guideline-based recommendations acted on by clinicians for hyperlipidaemia, atrial fibrillation and heart failure. We hypothesise care teams with access to the clinical decision support tool will act on recommendations at a higher rate than care teams in the standard of care arm.

Ethics and dissemination: The Mayo Clinic Institutional Review Board approved all study procedures. Informed consent was obtained from clinicians. A waiver of informed consent and of Health Insurance Portability and Accountability Act (HIPAA) authorisation for patients managed by clinicians in the study was granted. In addition to publication, results will be disseminated via meetings and newsletters.

Trial registration number: NCT02742545.

Keywords: adult cardiology; health informatics; internal medicine.

Conflict of interest statement

Competing interests: None declared.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Figures

Figure 1

MEA alerts. The EMR notifies the clinicians of MEA recommendations in three ways. (1) In the patient appointment section, MEA is seen with the number of recommendations in parentheses next to the patient’s name. (2) MEA also appears as a clinical alert. (3) Within the EMR banner, there is also a MEA alert that opens the MEA screen. EMR, electronic medical record; MEA, MayoExpertAdvisor.

Figure 2

MEA user interface. (A) Care recommendation. Depending on the individual patient’s date in the EMR, MEA makes a recommendation. (B) Vitals. Most recent outpatient vital signs. (C) Relevant patient data. The most relevant demographics, conditions and lab results for managing the given condition. (D) Resources for next steps. Additional condition-specific tools (eg, list of moderate and high-intensity statins) to assist in recommendations. (E) Risk calculators. Condition-specific risk calculators with a patient’s data prefilled for real-time calculations. (F) Decision aids. Mayo-vetted shared decision-making tools. Field values are prefilled with patient data. ACC ASCVD, American College of Cardiology Atherosclerotic Cardiovascular Disease; EMR, electronic medical record; LDL, low-density lipoprotein; MEA, MayoExpertAdvisor.