Audit and Feedback: An Evidence-Based Practice Literature Review of Nursing Report Cards.

BACKGROUND

As more hospitals transition to electronic health records (EHR) and rely on technology to inform practice, what is done with that information is increasingly important. Performance report cards for physicians and nurses are not new, yet there is little recent evidence on nurse-specific audit and feedback.

AIM

The aim of the project was to conduct an evidence-based practice (EBP) review to answer the question, "Does implementing an individualized audit and feedback report tool for nurses improve compliance, adherence, and/or performance of nursing tasks?".

METHODS

Evidence was gathered from several databases. Reviewers read and appraised articles that answered the EBP question using the Johns Hopkins Nursing EBP Model. Data were then collated to synthesize and generate recommendations.

RESULTS

Of the initial 613 unique articles, eight (two research and six quality improvement) were included. Six articles demonstrated improvements while two did not. Articles analyzed nursing documentation (n = 3), tasks or skills (n = 2), and best practice compliance (n = 3). One manuscript utilized an EHR-generated report; all others were completed by hand. Overall, there was not consistent and compelling evidence to support individualized audit and feedback report tools in nursing. However, several themes emerged related to sustainability, timing of feedback, audit, and feedback in the context of quality improvement, and the methods of acquiring and distributing data.

LINKING EVIDENCE TO ACTION

The ubiquity and ease of the EHR make providing automated feedback to nurses tempting, yet it is not supported by the literature. More implementation science research is needed to explore audit and feedback reports in nursing. This article adds to the literature by highlighting a significant lack of consistent and compelling positive results from the well-established quality improvement strategy of audit and feedback in the nursing population. The absence of good data is as telling as its presence.