Eliminating the AI digital divide by building local capacity

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.

Authors

Institutions

Publication Details

Journal
PLOS Digital Health
Published
2025-10-23
DOI
https://doi.org/10.1371/journal.pdig.0001026
Citations
1
Primary Topic
Electronic Health Records Systems
Type
article
Field-Weighted Citation Impact
2.94
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Eliminating the AI digital divide by building local capacity

Jean‐Louis Vincent, Alexandra Valladares, Ashley Beecy, Suresh Balu et al.
1 citations
PLOS Digital Health
Electronic Health Records Systems
2.94
article

Eliminating the AI digital divide by building local capacity

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

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.

PLOS Digital HealthVol. 4(10)
North Carolina Central University (US), Geisinger Medical Center (US), MetroHealth (US), Emory Healthcare (US), Mayo Clinic (US), University of North Carolina Health Care (US), Kaiser Permanente (US), American Medical Association (US), Medical College of Wisconsin (US), Washington University in St. Louis (US), University of Michigan (US), UC San Diego Health System (US), New York Hospital Queens (US), WinnMed (US), Dean Health Plan (US), Presbyterian Hospital (US), San Francisco Department of Public Health (US), California Medical Innovations Institute (US), Institut Pertanian Stiper (ID), Manatt, Phelps & Phillips (United States) (US), Michigan United (US), Duke Institute for Health Innovation (US), Duke Medical Center (US), Parkland Center For Clinical Innovation (US), University of Wisconsin Health (US), Ochin (US), Georgia Department of Public Health (US), Bethel University (US), Aria - Jefferson Health (US), Morehouse School of Medicine (US), Epic Systems (United States) (US), Franciscan Missionaries of Our Lady University (US), Jefferson College (US)
Openalex Percentile: Top 6%
Electronic Health Records Systems
2.94
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.