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. 2022 Aug 24:4:931439.
doi: 10.3389/fdgth.2022.931439. eCollection 2022.

Governance of Clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes

Frank Liao  1   2 , Sabrina Adelaine  2 , Majid Afshar  3   4 , Brian W Patterson  1   2   4   5
Affiliations

Governance of Clinical AI applications to facilitate safe and equitable deployment in a large health system: Key elements and early successes

Frank Liao et al. Front Digit Health. .

Abstract

One of the key challenges in successful deployment and meaningful adoption of AI in healthcare is health system-level governance of AI applications. Such governance is critical not only for patient safety and accountability by a health system, but to foster clinician trust to improve adoption and facilitate meaningful health outcomes. In this case study, we describe the development of such a governance structure at University of Wisconsin Health (UWH) that provides oversight of AI applications from assessment of validity and user acceptability through safe deployment with continuous monitoring for effectiveness. Our structure leverages a multi-disciplinary steering committee along with project specific sub-committees. Members of the committee formulate a multi-stakeholder perspective spanning informatics, data science, clinical operations, ethics, and equity. Our structure includes guiding principles that provide tangible parameters for endorsement of both initial deployment and ongoing usage of AI applications. The committee is tasked with ensuring principles of interpretability, accuracy, and fairness across all applications. To operationalize these principles, we provide a value stream to apply the principles of AI governance at different stages of clinical implementation. This structure has enabled effective clinical adoption of AI applications. Effective governance has provided several outcomes: (1) a clear and institutional structure for oversight and endorsement; (2) a path towards successful deployment that encompasses technologic, clinical, and operational, considerations; (3) a process for ongoing monitoring to ensure the solution remains acceptable as clinical practice and disease prevalence evolve; (4) incorporation of guidelines for the ethical and equitable use of AI applications.

Keywords: AI; AI adoption; Clinical AI; equity; ethics; governance; oversight; predictive analytics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Clinical AI and predictive analytics committee composition with participants by role by respective disciplines.
Figure 2
Figure 2
Clinical AI and predictive analytics committee and sub-committees.
Figure 3
Figure 3
UW Health predictive model value stream.
See this image and copyright information in PMC

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