1. 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

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
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

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?