Systematic review and narrative synthesis of computerized audit and feedback systems in healthcare

Objectives (1) Systematically review the literature on computerized audit and feedback (e-A&F) systems in healthcare. (2) Compare features of current systems against e-A&F best practices. (3) Generate hypotheses on how e-A&F systems may impact patient care and outcomes. Methods We searched MEDLINE (Ovid), EMBASE (Ovid), and CINAHL (Ebsco) databases to December 31, 2020. Two reviewers independently performed selection, extraction, and quality appraisal (Mixed Methods Appraisal Tool). System features were compared with 18 best practices derived from Clinical Performance Feedback Intervention Theory. We then used realist concepts to generate hypotheses on mechanisms of e-A&F impact. Results are reported in accordance with the PRISMA statement. Results Our search yielded 4301 unique articles. We included 88 studies evaluating 65 e-A&F systems, spanning a diverse range of clinical areas, including medical, surgical, general practice, etc. Systems adopted a median of 8 best practices (interquartile range 6-10), with 32 systems providing near real-time feedback data and 20 systems incorporating action planning. High-confidence hypotheses suggested that favorable e-A&F systems prompted specific actions, particularly enabled by timely and role-specific feedback (including patient lists and individual performance data) and embedded action plans, in order to improve system usage, care quality, and patient outcomes. Conclusions e-A&F systems continue to be developed for many clinical applications. Yet, several systems still lack basic features recommended by best practice, such as timely feedback and action planning. Systems should focus on actionability, by providing real-time data for feedback that is specific to user roles, with embedded action plans. Protocol Registration PROSPERO CRD42016048695.

Automated summary

This study systematically reviewed the literature on computerized audit and feedback systems in healthcare, comparing them against best practices and generating hypotheses on their impact. The results indicate that these systems have a positive impact on care quality and patient outcomes, but some systems still lack basic recommended features.