Integrated Risk Management and Artificial Intelligence in Hospital

Abstract

The topic revolves around the integration of Artificial Intelligence (AI) in Hospital Integrated Risk Management (IRM). AI offers significant advantages in enhancing risk identification, assessment, and mitigation across various areas of hospital operations. It can contribute to patient safety by enabling early detection of critical conditions, improving clinical risk management, and enhancing decisionmaking processes. AI also plays a vital role in information security and privacy, operational risk management, regulatory compliance, and human resources in hospitals. However, the use of AI in Hospital IRM comes with certain disadvantages and risks that need to be mitigated. These include data quality and bias, interpretability and transparency challenges, privacy and security concerns, reduced human oversight, ethical considerations, and implementation challenges. Mitigating these risks requires robust data governance, addressing bias in AI algorithms, ensuring transparency and accountability, implementing strong cybersecurity measures, and upholding ethical guidelines. To achieve successful implementation, hospitals should prioritize employee competencies, such as domain knowledge, data literacy, AI and data science skills, critical thinking, collaboration, adaptability, and ethical awareness. By developing these competencies and adhering to best practices, hospitals can optimize the use of AI in IRM, improve patient outcomes, enhance operational efficiency, and mitigate risks effectively.