Applications of Artificial Intelligence in Healthcare Management: A Systematic Review of Operational Efficiency and Challenges

AI in Healthcare Management

Abstract

BACKGROUND: Artificial intelligence (AI) is increasingly applied in healthcare administration, yet systematic evidence on its impact remains scarce. While most reviews focus on clinical decision-making, the non-clinical management domain—where inefficiencies in resource allocation, workflow, and finance persist—remains understudied.

OBJECTIVE: To systematically evaluate the role of AI in optimizing healthcare management, to identify implementation barriers, and to propose governance recommendations.

METHODS: We conducted a systematic review in accordance with PRISMA guidelines. PubMed, IEEE Xplore, and Scopus were searched for peer-reviewed studies published between 2015 and 2024. Eligible studies addressed AI applications in non-clinical healthcare management. Data were extracted on AI type, application domain, and outcomes. The final inclusion comprised 80 studies.

RESULTS: AI improved operational efficiency (predictive scheduling reduced wait times by 27%), enhanced financial integrity (fraud detection saved $3.2M annually), and optimized supply chains (robotic inventory systems reduced stockouts by 19%). Barriers included ethical risks (15% of triage algorithms exhibited bias) and interoperability challenges.

CONCLUSIONS: This review identifies three major domains of impact (efficiency, finance, ethics), highlights the implementation gap, and introduces a governance checklist for equitable adoption. AI substantially enhances healthcare management operations. However, regulatory oversight, bias audits, and workforce adaptation are essential to ensure equitable and sustainable integration. Future reviews should expand cross-country analysis and empirical evaluations in low-resource settings.