The Joint Commission, CHAI Partner on AI Best Practices, Certification
One sign that artificial intelligence use cases are rapidly expanding in healthcare system settings is the push to identify and disseminate best practices and the creation of certification and accreditation programs around it. The Joint Commission and the nonprofit Coalition for Health AI (CHAI) say they have developed a partnership to accelerate the development and adoption of AI best practices and guidance across the U.S. healthcare system.
In April accreditation organization URAC announced that its Health Care AI Accreditation is on track for a third-quarter 2025 launch. Shawn Griffin, M.D., URAC’s president and CEO, recently spoke with Healthcare Innovation about why accreditation in AI is so important.
In addition, the National Academy of Medicine has just published an AI Code of Conduct to serve as a “blueprint for building trust, protecting patients, and ensuring that innovation benefits people.”
The Joint Commission and CHAI said they would use their scale and expertise to co-develop a suite of AI playbooks, tools, and a new certification program rooted in The Joint Commission’s platform for evidence-based standards, and CHAI’s consensus-based best practices for health AI.
CHAI said it was founded to build the broadest possible consensus across the health ecosystem to help ensure health AI is trusted and safe. Its membership has grown to nearly 3,000 organizations, including academic medical centers, regional and rural health systems, healthcare technology leaders and start-ups, government experts and patient advocates.
The first guidance will be available in Fall 2025. AI certification will follow.
“In the decade ahead, nothing has the capacity to change healthcare more than AI in terms of innovation, transformation and disruption,” said Jonathan B. Perlin, M.D., Ph.D., president and CEO of The Joint Commission, in a statement. “While it’s impossible to predict exactly what healthcare will look like over that time, AI’s integration and potential to improve quality patient care is enormous – but only if we do it right. By working with CHAI, we are creating a roadmap and offering guidance for healthcare organizations so they can harness this technology in ways that not only support safety but engender trust among stakeholders.”
“The integration of AI into healthcare presents both significant opportunities and complex challenges. This effort between The Joint Commission and the Coalition for Health AI represents a thoughtful approach to navigating how to best deploy and implement these emerging technologies,” said Michael Pfeffer, M.D., chief information and digital officer for Stanford Health Care, in a statement. “The guidance, tools and certification it aims to provide will help accelerate innovation, mitigate risk, and enable healthcare organizations to fully leverage AI’s potential to improve patient outcomes and clinician workflows.”
New AMA policy
The American Medical Association (AMA) recently adopted policy during the Annual Meeting of its House of Delegates aimed at maximizing trust in and increasing transparency around how these tools arrive at their conclusions. The new policy calls for explainable clinical AI tools that include safety and efficacy data. To be considered explainable, these tools should provide explanations behind their outputs that physicians, and other qualified humans, can access to interpret and act on when deciding on the best possible care for their patients, teh AMA said.
The new policy calls for requiring an independent third party, such as regulatory agencies or medical societies, to determine whether an algorithm is explainable, rather than relying on claims made by its developer.
The policy states that explainability should not be used as a substitute for other means of establishing safety and efficacy of AI tools, such as randomized clinical trials. Additionally, the new policy calls on AMA to collaborate with experts and interested parties to develop and disseminate a list of definitions for key concepts related to medical AI and its oversight.
