Ensuring Patient Safety and Managing Risks in AI-Enabled Healthcare Systems

  • Authors

    • Ghatoth Mishra Independent Researcher, USA. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PI8R3K

    Published 06-07-2024

  • AI Patient Safety, Clinical Risk Management, Healthcare AI Governance, Hazard Analysis, Failure Mode Effects Analysis, Safety Assurance Frameworks, Human Factors Engineering, Clinician AI Interaction, Cybersecurity In Healthcare, Safety By Design, Risk Tolerance Quantification, AI Failure Detection, Continuous Monitoring Systems, Event Reporting And Learning, Regulatory Compliance In AI, Trustworthy Medical AI, Ethical AI Deployment, Workflow Integrated Safety, AI Lifecycle Management, Digital Health Safety

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    G. Mishra, “Ensuring Patient Safety and Managing Risks in AI-Enabled Healthcare Systems”, IJIARE, vol. 7, no. 1, pp. 01–23, Jun. 2024, doi: 10.67228/30715725/IJIARE-2024PI8R3K.

  • Abstract

    Since its arrival, artificial intelligence (AI) has transformed the way healthcare is delivered. With the capability of relieving significant staff workloads, introducing new methods of providing care and improving access for remote communities, AI has proven to shape a safer future for patient diagnosis by attaining levels of accuracy above those of humans. However, the use of AI in healthcare has not been without scrutiny, with a number of potential risks still needing to be mitigated before widespread introduction. Assuring the safety of patient care using AI must therefore remain a focus area, using a systematic risk management methodology that quantifies risk tolerances, identifies hazards and failure modes related to both AI and healthcare, and proposes the necessary risk mitigation strategies for patient safety assurance. Patient safety has become a central pillar in the deployment of AI-enabled healthcare systems. Safety objectives require careful formulation and measurement appropriate to AI systems, should be tightly linked to clinical workflows, and must include cybersecurity, human factors, and clinician interactions with AI. Hazard analysis, failure mode and effect analysis, and event reporting and learning systems enable patient safety to be ensured, informed by a safety-management toolbox dedicated to reducing risk within AI. Continuous training and harmonisation of these operators through culturally aligned safety and risk management processes enable user acceptance and the long-term assurance of patient care within emerging AI-enabled digital-health systems.

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