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Peer-reviewedCC BYHealth affairs scholar

Hospital market concentration and hospital adoption of artificial intelligence and machine learning.

  • Jie Chen1,2,3,4
  • Alice Yan1,2,3
4 affiliations
  1. 1Department of Health Policy and Management, School of Public Health, University of Maryland at College Park, College Park, MD 20742, USA.
  2. 2Hospital And Public health interdisciPlinarY research (HAPPY) Lab, University of Maryland at College Park, College Park, MD 20742, USA.
  3. 3Center on Aging, University of Maryland at College Park, College Park, MD 20742, USA.
  4. 4Center for Seniors Uniting Nationwide to Support Health, INtegrated care, and Economics (SUNSHINE), University of Maryland at College Park, College Park, MD 20742, USA.
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Abstract

We examined associations of hospital market concentration and organizational characteristics with artificial intelligence (AI) and machine learning (ML) use. We analyzed 2025 hospital survey data, measuring concentration with the Herfindahl-Hirschman index (HHI). Primary outcomes measured current use of AI/ML both integrated into and outside electronic health records (EHRs), and function counts requiring tools from both the EHR developer and another source. Alternative measures allowed either setting or source. Standard errors were clustered by hospital referral region. High vs low concentration was associated with a 6.7-percentage-point higher adjusted probability of generative-AI use in both settings (95% CI, 0.2-13.2), but not with any use (0.2; 95% CI, -4.9 to 5.2). Moderate concentration was associated with 0.20 fewer generative functions requiring both sources (95% CI, -0.39 to -0.01). System affiliation and accountable care organization (ACO) participation were positively associated with all 5 outcomes under both definitions. Market concentration was not consistently associated with AI/ML use across technologies and outcome definitions. System affiliation and ACO participation were consistently associated with greater use.

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Cite

BibTeXRIS
BibTeX
@article{chen2026hospital,
  title = {{Hospital market concentration and hospital adoption of artificial intelligence and machine learning.}},
  author = {Jie Chen and Alice Yan},
  journal = {Health affairs scholar},
  year = {2026},
  month = oct,
  doi = {10.1093/haschl/qxag287},
  url = {https://europepmc.org/article/MED/42953653},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Hospital market concentration and hospital adoption of artificial intelligence and machine learning.
AU  - Jie Chen
AU  - Alice Yan
PY  - 2026
DA  - 2026/10/16
JO  - Health affairs scholar
DO  - 10.1093/haschl/qxag287
UR  - https://europepmc.org/article/MED/42953653
AB  - We examined associations of hospital market concentration and organizational characteristics with artificial intelligence (AI) and machine learning (ML) use. We analyzed 2025 hospital survey data, measuring concentration with the Herfindahl-Hirschman index (HHI). Primary outcomes measured current use of AI/ML both integrated into and outside electronic health records (EHRs), and function counts requiring tools from both the EHR developer and another source. Alternative measures allowed either setting or source. Standard errors were clustered by hospital referral region. High vs low concentration was associated with a 6.7-percentage-point higher adjusted probability of generative-AI use in both settings (95% CI, 0.2-13.2), but not with any use (0.2; 95% CI, -4.9 to 5.2). Moderate concentration was associated with 0.20 fewer generative functions requiring both sources (95% CI, -0.39 to -0.01). System affiliation and accountable care organization (ACO) participation were positively associated with all 5 outcomes under both definitions. Market concentration was not consistently associated with AI/ML use across technologies and outcome definitions. System affiliation and ACO participation were consistently associated with greater use.
SN  - 2976-5390
ER  -