Eliminating the AI digital divide by building local capacity

Abstract

Over the past few years, health delivery organizations (HDOs) have been adopting and integrating AI tools, including clinical tools for tasks like predicting risk of inpatient mortality and operational tools for clinical documentation, scheduling and revenue cycle management, to fulfill the quintuple aim. The expertise and resources to do so is often concentrated in academic medical centers, leaving patients and providers in lower-resource settings unable to fully realize the benefits of AI tools. There is a growing divide in HDO ability to conduct AI product lifecycle management, due to a gap in resources and capabilities (e.g., technical expertise, funding, data infrastructure) to do so. In previous technological shifts in the United States including electronic health record and telehealth adoption, there were similar disparities in rates of adoption between higher and lower-resource settings. The government responded to these disparities successfully by creating centers of excellence to provide technical assistance to HDOs in rural and underserved communities. Similarly, a hub-and-spoke network, connecting HDOs with technical, regulatory, and legal support services from vendors, law firms, other HDOs with more AI capabilities, etc. can enable all settings to be well equipped to adopt AI tools. Health AI Partnership (HAIP) is a multi-stakeholder collaborative seeking to promote the safe and effective use of AI in healthcare. HAIP has launched a pilot program implementing a hub-and-spoke network, but targeted public investment is needed to enable capacity building nationwide. As more HDOs are striving to utilize AI tools to improve care delivery, federal and state governments should support the development of hub-and-spoke networks to promote widespread, meaningful adoption of AI across diverse settings. This effort requires coordination among all entities in the health AI ecosystem to ensure these tools are implemented safely and effectively and that all HDOs realize the benefits of these tools.

Fig 1. Hub-and-spoke network diagram for local capacity building.

(A) The diagram above depicts a hub-and-spoke network where the coordinating center forms connections between hubs (professional services, payers, universities and professional societies, vendors, and other HDOs with more expertise in AI adoption and implementation) to spokes (community hospitals, federally qualified health centers, and other HDOs with less expertise in AI adoption and implementation). Black lines represent connection from hubs and spokes to the coordinating center, and orange lines represent connections from hubs to spokes facilitated by the coordinating center. Spokes may independently form partnerships with hubs not facilitated by the coordinating center, which is not illustrated in this diagram. (B) The diagram below demonstrates an example network for a community hospital seeking support in adopting and implementing a sepsis risk prediction tool. HDOs include university-affiliated medical centers as well as non-affiliated ones that have developed thorough expertise from having implemented a sepsis AI tool.