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Eliminating the AI digital divide by building local capacity [1]

['Freya Gulamali', 'Duke Institute For Health Innovation', 'Durham', 'North Carolina', 'United States Of America', 'Jee Young Kim', 'Kartik Pejavara', 'Ciera Thomas', 'Varoon Mathur', 'Zev Eigen']

Date: 2026-02 

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.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: MPS is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc., KelaHealth, Inc., Cohere-Med, Inc., and Vega Health, Inc. MPS holds equity in Clinetic, Inc. and Vega Health, Inc. SB is a co-inventor of intellectual property licensed by Duke University to Clinetic, Inc., Cohere-Med, Inc., and Vega Health, Inc. SB holds equity in Clinetic, Inc.

Funding: This work was supported by the Gordon and Betty Moore Foundation (#10849 to MPS, MP, and SB). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Gulamali et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

The emergence of generative AI offers new opportunities to advance the quintuple aim [1], which is defined by improving population health, enhancing the care experience, reducing costs, reducing burnout, and advancing health equity. Healthcare delivery organizations (HDOs) have seized opportunities to achieve these advancements at scale by investing in new partnerships to implement large language models (LLMs) that complement other AI tools.

Healthcare in the United States continues to suffer from low quality, low value care [2]. New investments in AI tools seek to help optimize the delivery of high-value interventions [3]. With record levels of physician burnout, ambient AI scribing solutions—AI tools that transcribe physician-patient interactions and summarize them into encounter notes—hold promise to help clinicians shift their focus from the documentation burden to enhancing patient interaction [4]. LLMs can also summarize information from discharge notes for patient accessibility [5] and retrieve information on patients’ health-related social needs from clinical notes to more effectively deploy resources to support patients [6]. Population health challenges are being addressed by other types of AI tools that can identify patients at risk for rapid disease progression [7] and expand patient access to psychotherapy [8].

Driven by urgency and competitive pressures to address healthcare challenges, many organizations are looking to adopt AI tools. According to one industry report, 76% of major payers and providers surveyed are seeking to establish AI pilots in the next year [9]. HDOs with the expertise and resources to implement administrative and clinical AI tools effectively are optimizing efficiency and improving quality of care. However, many HDOs are poorly equipped to conduct local AI quality management and are either not implementing AI tools at all or conducting limited testing and monitoring. The latter can exacerbate issues AI was designed to solve like improving efficiency and providing high quality care by increasing burden through alert fatigue, worsening inequities with biased predictions, negatively impacting care, and increasing liability on physicians and organizations. Implementing the same AI clinical decision support tool with workflows optimized for local context has been shown to reduce sepsis mortality in one context and be ineffective in another context [10,11]. Recent studies on the ROI of AI scribes are also showing mixed results across settings [12,13]. Substantial expertise is needed to realize the value of these tools, and ineffective AI implementations may negatively impact patients and clinicians while reinforcing existing biases [14] and discourage HDO uptake of beneficial technologies. While HDOs with resources and capabilities to leverage AI responsibly can achieve advancements in care, other HDOs may lag, driving a deeper digital divide and gap in quality of care.

Digital divide in AI adoption The digital divide is typically defined by lack of access to internet services, perpetuating socioeconomic disparities [15]. These disparities may be exacerbated as HDOs lacking personnel with relevant expertise, resources to purchase and localize tools, organizational processes and capabilities to conduct AI product lifecycle management, and IT infrastructure, including electronic health records (EHRs) will be unable to fully benefit from AI [16]. As of 2021, more than 20% of office-based practices did not adopt 2015 Certified EHR Technology, which serves as a backbone for many AI applications [17]. This disparity contributes to an AI divide, where HDOs with certified EHRs may have access to AI tools, while those without it are left at a disadvantage. The World Health Organization 2021 Guidance on AI Ethics and Governance in Health identifies the impact of the digital divide on AI adoption at a global scale and recognizes the potential for AI to improve health outcomes if greater efforts are made to address this divide [18]. The AI divide is particularly evident in one study, which found that only 61% of hospitals conduct local evaluations to assess for accuracy and 44% to assess for bias on most or all implemented AI products [19]. This finding suggests a gap in the resources or capabilities available to different HDOs to properly evaluate AI tools. Considering hospitals more commonly reported local evaluation on inpatient risk tools than outpatient administrative tools, authors of the study suggest one factor contributing to lower evaluation rates may be a misperception of the risk of outpatient tools. The AI divide, furthered by lack of awareness, resources, and capabilities to evaluate AI tools, can lead to unequal access to safe and effective AI use across different demographic subgroups, particularly among at-risk populations. As a result, there is a high risk of ineffective implementations that worsen inequities.

Lessons from previous technological shifts in healthcare Prior technological advancements that transformed healthcare delivery include the adoption and implementation of EHRs and telehealth. Both required federal support at the individual practice level, as well as incentives and infrastructure development to promote adoption. Federal programs that supported these transitions offer valuable examples for how the United States can rapidly facilitate the safe and effective adoption of AI tools. The HITECH Act, enacted in 2009 and implemented in 2011, provided incentive payments to providers who adopted EHR systems. Of providers, 75% report that adopting EHR systems enabled them to deliver better patient care [20]. While these incentives drove adoption overall, small, non-teaching, and rural hospitals continued to lag behind [21]. To address this issue, the Office of the National Coordinator for Health IT (ASTP/ONC) established Regional Extension Centers (RECs) in 2010, aiming to increase EHR adoption in rural and underserved settings [22]. RECs were funded to deliver technical, legal, and financial assistance; education and training; assistance in vendor selection; privacy and security support; and more to help practices achieve meaningful use of EHRs. The SAFER guidelines checklist provided additional resources and information that stakeholders required, enabling practices to track their progress capturing value from EHRs [23]. By 2014, 89% of REC participants adopted all or part of EHRs compared to 58% of non-participants [24]. This demonstrated that financial incentives alone could not drive EHR adoption across all settings. Complementary professional services were essential to support smaller practices, and these services will be needed to promote responsible AI adoption across HDOs as well. Similarly, Telehealth Centers for Excellence (COEs) and the National Consortium of Telehealth Resource Centers (TRCs) were created to provide professional support services, education, and training to facilitate digital health adoption in rural and underserved communities. The COEs, housed at the Medical University of South Carolina and University of Mississippi Medical Center, established telehealth-enabled locations nationwide and evaluated Telehealth Palliative Care and Tele-Behavioral Health programs [25]. The Health Resources and Services Administration (HRSA) provided $600,000 in the first year and $16.25 million over five years to support COEs. The 12 regional and two national TRCs, also funded by HRSA, played a critical role in advancing telehealth adoption during the COVID-19 pandemic [26]. These centers were complemented by infrastructure investments with the 2021 Bipartisan Infrastructure Law [27] and American Rescue Plan [28], which expanded access to broadband. Furthermore, the 2020 1135 Waiver [29] allowed Medicare to cover telehealth services more broadly. Philanthropies like the California Health Care Foundation complemented government funding to further catalyze adoption and evaluate program impact [30]. As a result, the percent of physicians whose practices had telehealth capabilities increased from 25.1% in 2018 to 74.4% in 2022 [31]. This growth in telehealth adoption also required a multi-pronged approach consisting of professional support services, infrastructure investments, and reimbursement changes. In these major technological advancements in healthcare, capacity building programs played a crucial role in diffusing expertise to rural and underserved settings. They often took the form of a hub-and-spoke model, where RECs, COEs, and TRCs served as hubs, providing resources, professional services, and a peer learning community, while HDOs functioned as the spokes, receiving the support. Congress worked closely with federal agencies to empower HDOs to adopt EHR systems and telehealth through infrastructure investments, incentives, and hub-and-spoke networks. These EHR and telemedicine investments created significant value and ROI for a variety of stakeholders [32,33]. It is essential to draw insights from these examples, understanding how public and private entities partnered to promote EHR and telemedicine adoption, when addressing the current technological shift that is AI in healthcare. While the specific capabilities required and challenges involved in adopting AI differ from those posed by EHR systems and telehealth systems, the capacity building approach has proved valuable in past implementations and would be valuable to pursue in this case as well.

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[1] Url: https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001026

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