Impact of Clinical Audit Practices on Performance Improvement in Healthcare: A Systematic Review with Quantitative Synthesis
DOI:
https://doi.org/10.7492/dct8t288Abstract
Background: Clinical audit and audit-and-feedback (A&F) are widely used quality improvement (QI) approaches intended to reduce unwarranted variation and improve care processes and outcomes.
Objective: To synthesize recent evidence on how clinical audit practices (including A&F and electronic A&F) influence healthcare performance improvement outcomes.
Methods: A PRISMA-informed systematic search strategy (conceptually aligned with Cochrane methods for A&F) was used to identify recent systematic reviews, trials, and large observational evaluations of clinical audit/A&F in hospital and primary care settings. Evidence was summarized narratively and with simple quantitative summaries where studies reported extractable effect estimates.
Results: Across included evidence, audit/A&F was associated with modest-to-moderate improvements in professional practice and, in some settings, measurable patient-safety gains. A recent imaging-ordering meta-analysis (11 RCTs; 4,311 clinicians/practices) estimated 1.5 fewer imaging orders per 1,000 patients with A&F versus control (95% CI −2.6 to −0.4; p=0.009), while appropriateness effects were uncertain. A large interrupted time series evaluating an electronic A&F system reported a reduction in hospital-acquired complication risk from 8.57% to 4.12% (51.93% reduction). A national clinical audit program in Saudi Ministry of Health hospitals observed improvements in 69.1% of audited measures, with 40.5% showing statistically significant improvement.
Conclusions: Clinical audit practices especially when implemented as iterative cycles with actionable feedback, benchmarking, and local QI support are consistently linked to improvements in care processes and can contribute to performance improvement outcomes. Effects vary by baseline performance, feedback design, and organizational readiness, emphasizing the need for context-sensitive implementation and robust measurement [2].
