Chapter 6 Deploying AI in Clinical Settings

1. Chapter 6Deploying AI in Clinical Settings • 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 Stephan Fihn, Hongfang Liu, Mayo, EneidaMendonca, Seth Hain, Michael Matheny, Nigam Shah, Andy Auerbach

2. 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

3. 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

4. FRAMEWORK & CRITERIA FOR AI SELECTION &IMPLEMENTATION IN CLINICAL CARE • 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 • Clinical Environment & State Change Requirements • Performance • Interpretability • Alignment with desired clinical state • Data source(s) and inter-operability • Algorithm maintenance requirements

5. AI DEPLOYMENT/IMPLEMENTATION FRAMEWORK • Organizational Approach • Governance

6. 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.

7. 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?