Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals: Article examines uses and evaluation of artificial intelligence and predictive models in US hospitals
Abstract
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling. We found that 65 percent of US hospitals used predictive models, and 79 percent of those used models from their electronic health record developer. Sixty-one percent of hospitals that used models evaluated them for accuracy using data from their health system (local evaluation), but only 44 percent reported local evaluation for bias. Hospitals that developed their own predictive models, had high operating margins, and were health system members were more likely to report local evaluation. Policy and programs that provide technical support, tools to assess FAVES principles, and educational resources would help ensure that all hospitals can use predictive models safely and prevent a new organizational digital divide in AI.
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Automated sepsis prediction tools often lack generalizability and are commonly implemented by health systems without internal validation.
“However, such tools often lack generalizability and are commonly implemented by health systems without internal validation,8 potentially compromising sepsis care through missed or delayed treatment.”
From Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model (2026) · cites this paper as [8]
In 2023, slightly less than half of hospitals using predictive AI reported consistently conducting local evaluation of predictive AI, with lower rates of AI adoption and evaluation at independent hospitals compared with health systems.
“In 2023, slightly less than half of hospitals using predictive AI reported consistently conducting this kind of local evaluation, with lower rates of AI adoption and evaluation at independent hospitals compared with health systems.22”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [22]
We observed the continuation of a digital divide across many hospital characteristics in adoption of generative AI.
“We observed the continuation of a digital divide across many hospital characteristics in adoption of generative AI.22”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [22]
Differences in generative AI adoption across hospital characteristics, primary EHR vendor, and experience evaluating predictive AI indicate that the digital divide observed with predictive AI may be replicated in the use of generative AI.
“Differences in generative AI adoption across hospital characteristics, primary EHR vendor, and experience evaluating predictive AI indicate that the digital divide observed with predictive AI may be replicated in the use of generative AI.22”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [22]
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There is no such thing as a validated prediction model
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External validation of AI models in health should be replaced with recurring local validation
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A framework for the oversight and local deployment of safe and high-quality prediction models
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Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals
Everson, J., Nong, P., & Richwine, C. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.49463 · Cited by 33 · see citing works
Self-citationThe landscape of AI implementation in US hospitals
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Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction Model
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A Pragmatic Framework for Federated Learning Risk and Governance in Academic Medical Centers
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“Following the groundbreaking work in artificial intelligence (AI), especially with respect to large language models, there has been an exponential rise in AI model adoption across academic medical centers (AMCs) [1,2] .”
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- Authors
- Nong, P., Adler-Milstein, J., Apathy, N. C., Holmgren, A. J., & Everson, J.
- Affiliations
- University of Minnesota (US)
- Julia Adler-Milstein, University of California San Francisco, San Francisco, California.
- University of Maryland, College Park (US)
- University of California, San Francisco (US)
- Jordan Everson, Office of the Assistant Secretary for Technology Policy, Washington, D.C.
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2025-01-01
- Volume
- 44
- Issue
- 1
- Pages
- 90-98
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- OpenAlex
- W4406110125
- Cited by
- 104
- References
- 9
- Citations by year
- 2026: 69 · 2025: 35
- FWCI
- 25.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Affairs (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence in healthcare, prediction model, model evaluation, algorithmic bias, electronic health records, hospital operating margins, digital divide, AI governance
- MeSH terms
- Machine Learning, Artificial Intelligence, Forecasting, Hospitals, Humans, United States, Electronic Health Records
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