Trubridge Inc.
07/19/2024 | Press release | Distributed by Public on 07/19/2024 11:57
Rural Healthcare Voices on a National Platform
The Trustworthy and Responsible AI Network (TRAIN) was formally announced at HIMSS24 and recently held its first meeting on the current state and evolution of AI in healthcare. The network facilitates collaboration among 16 of the nation's leading health systems including Cleveland Clinic, Johns Hopkins Medicine, and Boston Children's Hospital, along with technology innovators such as Microsoft and TruBridge. TRAIN aims to foster responsible and safe AI development frameworks to ensure all communities can access and benefit from relevant, accessible, affordable AI technology.
Industry experts involved in TRAIN agree that no provider should be left behind in the journey toward automation. This commitment to support all healthcare organizations (HCOs) includes the nation's rural hospitals and health centers. Leaders from Kern Valley Healthcare District in Mountain Mesa, California, agree that rural providers need a voice in the advancement of AI in healthcare and add, "It is great to see TruBridge taking the capabilities of AI responsibly and to have a strong, rural perspective in tech development".
As the primary care location for millions of Americans, rural hospitals play an essential role in serving their communities and providing vital care to patients. "Rural communities want personal connections with their healthcare providers, and they want us to keep care in the community," said Lloyd Sirmons, Executive Director, Georgia Rural Health Association, at a recent Georgia HIMSS Chapter event.
However, rural hospitals typically lack sufficient capital to reinvest in their facilities, and often fall behind urban counterparts in technology and service line perspectives.
Ensuring rural healthcare providers stay engaged and involved in the advancement of AI technology is one of TRAIN's founding principles. This commitment benefits the estimated 57 million Americans who receive care in rural settings, and guarantees data from rural HCOs is included in large language models (LLMs) nationwide to keep the rural voice in AI conversations.
TRAIN update and takeaways
Discussions during TRAIN's recent national meeting focused on three pressing considerations for AI in healthcare today:
Safe development and deployment
Ability to ensure bias-free, equitable models
Access and affordability across the industry
As healthcare innovators focused on serving rural healthcare providers, we are energized by the high level of collaboration, willingness to learn, and dedication to inclusive technology from all TRAIN members. Here are three takeaways from the recent TRAIN meeting.
1. Close monitoring of evolving regulations is vital for development.
Governance is a strong consideration for any healthcare leader when developing, adopting, and implementing technology. Keeping a careful eye on the regulatory environment to ensure compliance is critical for successful implementation. TRAIN members actively oversee regulations coming from industry leaders including the Department of Health and Human Services (HHS) and the Office of the National Coordinator for Health Information (ONC). TRAIN also maintains integration with the Coalition on Healthcare AI (CHAI) to ensure those standards are represented, not duplicated. With these guidelines in place, health tech developers will create better futures for under-represented populations by including diverse perspectives, needs, and experiences in AI models.
2. Bias-free systems are the foundation of an equitable healthcare future.
Tech equity or "techquity" is a core focus for TRAIN and other regulatory and AI development networks. As mentioned above, guidelines from HHS, ONC, and CHAI support inclusion of communities, such as rural Americans, whose needs are often overlooked by the broader healthcare industry. Conversations around techquity bring these communities to the forefront, emphasizing their importance for a healthcare future that represents wide-ranging experiences. Tools such as www.fairlearn.org and other generative AI safeguards provide frameworks to evaluate any potential biases systems may hold.
Techquity is imperative to ensure AI does not exacerbate the gap between "haves" and "have-nots." Representation of the rural perspective offers opportunities to determine how AI tools and governance can be applied to low-resource and low-population-density situations. These circumstances give way to wider, more inclusive considerations throughout the development process. As we continue to create new systems and evaluate how we're incorporating the experiences of all Americans into AI models, we can foster a stronger healthcare future for all communities.
3. Affordable, relevant AI must be accessible across America.
As excitement for this technology continues to buzz throughout the industry, health leaders must understand how to make AI affordable and accessible for all communities. Rural and community health systems often face financial burden and limited resources, and as a result, purchasing and implementing new technology can be difficult.
For widespread AI adoption to be successful, tech developers must find ways to create high-quality, relevant systems that can be incorporated into healthcare organizations in all corners of America. Collaborative learning between specialized health tech experts and representatives of these communities is vital through AI evolution. Systems that are designed with rural communities in mind are critical to gaining broader trust and adoption of AI technology.
TRAIN offers a promising perspective on the future of AI in healthcare. Through continued teamwork, we envision a health system that is supported and enhanced by responsible, relevant, safe AI. Development and evaluation of these systems through the lens of under- represented communities ensures a better, healthier future for all. With a focus on the rural perspective, healthcare leaders keep the voices of diverse communities at the forefront of this emerging era.