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

• DOI: 10.3389/fdgth.2022.931439
• Publication Date: 2022-08-24T05:00:30Z
• Authors: Frank Liao; Sabrina Adelaine; Majid Afshar; Brian W. Patterson
• Keywords: R; Clinical AI; QA75.5-76.95; oversight; 3. Good health; predictive analytics; 03 medical and health sciences; 0302 clinical medicine; governance; AI; AI adoption; Electronic computers. Computer science; Medicine; Digital Health; Public aspects of medicine; RA1-1270

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