Eliminating the AI digital divide by building local capacity

Freya Gulamali, Jee Young Kim, Kartik Pejavara, Ciera Thomas, Varoon Mathur, Zev Eigen, Mark Lifson, Manesh Patel, Keo Shaw, Danny Tobey, Alexandra Valladares, David Vidal, Jared Augenstein, Ashley Beecy, Sofi Bergkvist, Michael Burns, Michael Draugelis, Jesse M Ehrenfeld, Patricia Henwood, Tonya Jagneaux, Morgan Jeffries, Christopher Khuory, Frank J Liao, Vincent X Liu, Chris Longhurst, Dominic Mack, Thomas M Maddox, David McSwain, Steve Miff, Corey Miller, Sara G Murray, Brian W Patterson, Philip Payne, W Nicholson Price, Ram Rimal, Michael J Sheppard, Karandeep Singh, Abdoul Sosseh, Jennifer Stoll, Corinne Stroum, Yasir Tarabichi, Sylvia Trujillo, Ladd Wiley, Alifia Hasan, Joan S Kpodzro, Suresh Balu and Mark P Sendak

PLOS Digital Health, 2025, vol. 4, issue 10, 1-13

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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0001026

DOI: 10.1371/journal.pdig.0001026

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