Monday, at the HIMSS 2024 Global Health Conference, a new consortium of healthcare leaders announced the creation of the Trustworthy & Responsible AI Network (TRAIN), which aims to operationalize responsible AI principles to improve the quality, safety and trustworthiness of AI in health. Members of the network include AdventHealth, Advocate Health, Boston Children’s Hospital, Cleveland Clinic, Duke Health, Johns Hopkins Medicine, Mass General Brigham, MedStar Health, Mercy, Mount Sinai Health System, Northwestern Medicine, Providence, Sharp HealthCare, University of Texas Southwestern Medical Center, University of Wisconsin School of Medicine and Public Health, Vanderbilt University Medical Center, and Microsoft as the technology enabling partner. Additionally, the network is collaborating with OCHIN, which serves a national network of community health organizations with solutions, expertise, clinical insights and tailored technologies, and TruBridge, a partner and conduit to community healthcare, to help ensure that every organization, regardless of resources, has access to TRAIN’s benefits.
New AI capabilities have the potential to transform the healthcare industry by enabling better care outcomes, improving efficiency and productivity, and reducing costs. From helping screen patients, to developing new treatments and drugs, to automating administrative tasks and enhancing public health, AI is creating new possibilities and opportunities for healthcare organizations and practitioners. As new uses of AI in healthcare continue to unfold and grow, the need for rigorous development and evaluation standards becomes even more important to ensure effective and responsible applications of AI.
Through collaboration, TRAIN members will help improve the quality and trustworthiness of AI by:
- Sharing best practices related to the use of AI in healthcare settings, including the safety, reliability and monitoring of AI algorithms, and the skillsets required to manage AI responsibly. Data and AI algorithms will not be shared between member organizations or with third parties.
- Enabling registration of AI used for clinical care or clinical operations through a secure online portal.
- Providing tools to enable measurement of outcomes associated with the implementation of AI, including best practices for studying the efficacy and value of AI methods in healthcare settings and leveraging of privacy-preserving environments, with considerations in both pre- and post-deployment settings. Tools that allow analyses to be performed in subpopulations to assess bias will also be provided.
- Facilitating the development of a federated national AI outcomes registry for organizations to share among themselves. The registry will capture real-world outcomes related to efficacy, safety and optimization of AI algorithms.
