Artificial Intelligence and Machine Learning in Hospital Quality Management, Patient Safety, and Accreditation...

Area of Science:

• Health Informatics
• Artificial Intelligence in Healthcare
• Machine Learning Applications

Background:

• Healthcare systems are increasingly data-driven, necessitating advanced tools for quality management and patient safety.
• Artificial intelligence (AI) and machine learning (ML) are emerging technologies with potential applications in healthcare quality and accreditation.

Purpose of the Study:

• To systematically review and synthesize evidence on the use of AI and ML in healthcare quality management, patient safety, and accreditation.
• To assess the implementation context and technical performance of AI and ML in healthcare settings.

Main Methods:

• A structured literature search of major biomedical databases was conducted.
• Studies published over the past decade were included and narratively synthesized.
• Twenty-three studies were identified and included in the review.

Main Results:

• AI and ML are most effective for repetitive, well-defined, digitally captured tasks.
• Local validation, acceptance testing, and continuous monitoring are crucial for safe deployment.
• Organizational readiness and governance capacity are as critical as algorithmic performance.

Conclusions:

• AI and ML should serve as augmentative tools within human-led quality governance structures.
• Current evidence is heterogeneous, with limited high-quality comparative studies.
• Safe and effective deployment requires careful consideration of implementation factors beyond algorithmic capabilities.