Original Source: Center for Connected Health Policy
In February, the Connective Health Initiative (CHI), a coalition facilitated by the App Association, released a list of policy principles to guide policymakers in navigating the nuances of creating policy associated with the use of artificial intelligence (AI) in healthcare. The principles focus on the use of AI to advance the “quadruple aim” of improving population health. The paper lists these as “improving population health, improving patient health outcomes and satisfaction, increasing value by lowering overall costs; and improving clinician and healthcare team well-being.” The list is included in a three-part series presented by the CHI AI Task Force, which also includes a document explaining the coalition’s position of support for AI in healthcare and a list of important terminology targeted at policymakers. The App Association states that these documents will be updated with continual feedback from the community.
Here are some brief descriptions of each of the guiding principles:
- National Health AI Strategy: Developing a federal healthcare AI strategy to guide the cultural, workforce training and education, data access, and technology-related changes associated with the incorporation of AI in healthcare.
- Research: Support and facilitate the research and development of AI in healthcare with sufficient funding and incentives. Prioritize clinical validation and transparency in research and condition public funding and incentives to advance shared knowledge, access, and innovation.
- Quality Assurance and Oversight: Utilize risk-based approaches to ensure AI in healthcare aligns with standards of safety, efficacy, and equity. Policy addressing liability should distribute and mitigate risk and liability.
- Thoughtful Design: Policy should require AI systems design informed by real-world workflow, human-centered design and usability principles, and end-user needs.
- Access and Affordability: Policy should ensure AI systems in health care are accessible and affordable. AI systems should help transition to value-based delivery models, while incentivizing voluntary adoption and integration within clinical practice.
- Ethics: Promote existing and emerging ethical norms of the medical community for use by technologists, innovators, computer scientists, and those who use such systems.
- Modernized Privacy and Security Frameworks: Policy should address topics of privacy, consent, and modern technological capabilities. Policy should also be scalable and assure that health information is protected while allowing the flow of information.
- Collaboration and Interoperability: Policy should enable data access through a culture of cooperation, trust, and openness among policymakers, health AI technology developers, users, and the public.
- Workforce Issues and AI in Healthcare: Create an appropriate balance between human care and AI-enabled technologies and tools to enable health care providers meet the needs of all patients.
- Bias: Require identification, disclosure, and mitigation of bias and ensure that data bias does not cause harm to patients or consumers.
- Education: Support education for advancements in AI, promote the success of AI in healthcare, and encourage stakeholder engagements including patient and consumer education and academic/medical education curriculum.