The healthcare industry is increasingly embracing artificial intelligence (AI) to transform patient care. However, ensuring that this transformation benefits all communities, especially rural and underserved areas, has become a critical focus. The Trustworthy Responsible AI Network (TRAIN), a consortium of healthcare organizations, aims to establish guidelines for implementing responsible AI across diverse settings. This initiative seeks to bridge the gap between well-resourced academic centers and underfunded rural health systems, promoting equitable access to advanced technologies.

Jennifer Stoll, Chief External Affairs Officer at OCHIN, will participate in an upcoming panel at HIMSS25. The session will explore how to operationalize responsible AI, focusing on practical tools and best practices that can be applied in various healthcare settings. Stoll emphasizes the importance of modernized electronic health records, trust in AI models, and robust governance structures to ensure that no community is left behind in the AI revolution.

Building Trust and Infrastructure for AI in Rural Healthcare

Ensuring that rural and underserved communities can fully participate in the AI-driven healthcare transformation requires addressing key infrastructure and trust issues. Modernized electronic health record systems are essential for seamless data exchange, which is crucial for effective AI deployment. Without these systems, providers in rural areas may struggle to integrate AI tools into their workflows, leading to widening disparities in care quality.

To foster innovation and improve health outcomes, it is imperative to provide funding and support for certified electronic health record systems in rural and underserved regions. A trusted partner can help maximize the value of these systems, reducing clinician burden and enhancing patient care. Additionally, building trust in AI models through local validation and lifecycle management is vital. Providers must have confidence in the tools they use, and this trust can only be established by ensuring that AI models are tested and validated using representative data from the communities they serve. This approach not only improves the accuracy of AI predictions but also ensures that the unique needs of rural patients are addressed.

Promoting Inclusive Innovation and Collaboration

Innovating with technology in healthcare must be done thoughtfully to prevent excluding large segments of the population. Rural and medically underserved communities often face unique challenges, such as higher rates of chronic illness and limited access to specialty care. These factors necessitate tailored solutions that consider the specific needs of each community. Local validation of AI tools is crucial to ensure they are effective and do not exacerbate existing health disparities.

Collaboration and partnership are essential for driving inclusive innovation. By working together, stakeholders from academic, medical, technology, nonprofit, and public sectors can create a level playing field for all providers. Greater funding, collaboration, and testing in under-resourced settings will accelerate innovation and benefit patients and providers nationwide. Stoll’s panel session at HIMSS25 will highlight the importance of these efforts, encouraging attendees to advocate for equitable access to AI technologies and fostering a collective commitment to improving healthcare for all communities.