Internal Audits as Drivers of Healthcare Quality Improvement: A Cross-Sectional Study on the Role of Artificial Intelligence-Assisted Audit Systems in Healthcare Institutions.

Abstract

Background: Healthcare organisations rely on internal audits to monitor compliance, identify operational gaps, and support quality improvement initiatives. With the increasing adoption of artificial intelligence (AI), audit processes are evolving from periodic manual reviews to more data-driven and responsive systems. Nevertheless, evidence on the contribution of AI-assisted audits to healthcare quality remains limited.
Objective: This study examined the relationship between AI-assisted internal auditing and quality improvement outcomes in healthcare institutions.
Methods: A cross-sectional study was conducted across 124 healthcare institutions, including 68 organisations that utilised AI-supported audit systems and 56 that employed conventional audit methods. Institutional characteristics, quality performance indicators, audit resolution timelines, and audit findings were analysed and compared. Multivariable logistic regression was used to determine factors associated with achieving a high composite quality score.
Results: Institutions using AI-assisted audits demonstrated better performance across all evaluated quality domains than those using traditional audits. Significant improvements were observed in patient safety, medication management, documentation practices, infection control, billing compliance, and staff adherence to protocols (all p < .001). The mean composite quality score was markedly higher in AI-assisted institutions (83.8 ± 5.9) compared with traditional institutions (61.9 ± 7.4). Furthermore, AI-supported organisations resolved audit findings more rapidly, with the average resolution time reduced from 17.7 days to 6.6 days. The pattern of audit findings was comparable between groups, indicating similar audit complexity. After adjustment for institutional characteristics, AI-assisted auditing remained the strongest predictor of superior quality performance (OR = 6.30, 95% CI: 3.43–11.57; p < .001).
Conclusion: The findings suggest that integrating AI into internal audit processes may enhance healthcare quality by strengthening oversight mechanisms, accelerating corrective actions, and supporting continuous quality improvement efforts. Wider adoption of AI-enabled auditing could contribute to safer and more efficient healthcare delivery.