Chapter 6 Deploying AI in Clinical Settings Stephan Fihn, Hongfang Liu, Mayo, Eneida Mendonca, Seth Hain, Michael Matheny, Nigam Shah, Andy Auerbach Key issues and current best practices related to implementation and maintenance of AI within healthcare systems. Complements Chapter 5 Target audience: those within healthcare systems who are deploying AI or considering doing so

SETTINGS FOR APPLICATION OF AI IN HEALTH CARE Traditional Point of Care – within and outside of EHR Clinical Information Processing and Management – e.g., image and signal processing Enterprise Operations (back office) – e.g., pharmacy, supply chain, staffing, scheduling, authorizations for care, patient flow. Nontraditional – e.g., groceries, pharmacies Population health management Patient/Care-giver support

APPLICATIONS OF AI IN CLINICAL CARE DELIVERY Emphasis on facilitating synthesis of information from medical science and experience of patients to integrate decision support into workflow and support team-based care. Risk Prediction Clinical Decision Support

FRAMEWORK & CRITERIA FOR AI SELECTION & IMPLEMENTATION IN CLINICAL CARE Clinical Environment & State Change Requirements Performance Interpretability Alignment with desired clinical state Data source(s) and inter- operability Algorithm maintenance requirements Other issues: Baseline requirements Flexibility of technologies being considered Data requirements for technology Balancing standardization vs. and customization Human and user-centered design considerations Acquisition Source Cost Considerations Safety Cybersecurity/Privacy Regulatory issues

AI DEPLOYMENT/IMPLEMENTATION FRAMEWORK Organizational Approach Governance

Recommendations Have healthcare system leadership and key clinical stakeholders define the near-term and far-term target state that would facilitate accomplishing concrete improvements in workflow and outcomes. AI is likely to be able to positively impact the healthcare system through efficient integration into EHR, pop health, and ancillary and allied health workflows, if these target states are clearly defined. Carefully determine through stakeholder and user engagement how much transparency is required for AI to operate in a particular use case. Determine cultural resistance and workflow limitations that may dictate key interpretability and actionability requirements be achieved in order to facilitate a reasonable chance at successful deployment. Establish standard processes for AI surveillance and maintenance of performance, and if possible, automate those processes to allow for scalable addition of AI tools for a variety of use cases. Engage healthcare system leadership and establish value statement. Perform decision analysis evaluations to establish the potential cost savings and/or clinical outcome gains from implementation of AI AI development and implementation should follow established best practice frameworks in implementation science AND software development.

Issues for Discussion Gaps? Speech recognition Adaptations of habit/culture More on workflow integration? (harmonize “cases”?) Balance between technologic enthusiasm and nihilism Overlaps between Chapts 3, 5 and 6 Description of AI applications Evaluation of AI applications Feasibility of approach for health care organizations – how will small systems “comply” (digital divide?) Other?