Institution-Level Readiness Modeling for Auditable Clinical LLM Adoption in US Health Systems
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Abstract
The rapid advancement of artificial intelligence, particularly large language models, presents a transformative opportunity for the healthcare sector. However, the deployment of these complex generative systems within clinical environments introduces unprecedented challenges regarding patient safety, regulatory compliance, data privacy, and institutional accountability. This paper proposes a comprehensive institution-level readiness modeling framework designed specifically for the auditable adoption of clinical large language models within United States health systems. By synthesizing principles from organizational theory, health informatics, and regulatory science, the proposed framework delineates the multidimensional prerequisites necessary for safe integration. The study explores the technical infrastructural demands, data governance protocols, workforce competency requirements, and the fundamental necessity for transparent auditing mechanisms capable of continuously monitoring model behavior. Through simulated application across diverse healthcare settings, the research highlights critical gaps in current institutional preparedness and provides strategic recommendations for addressing these vulnerabilities. The findings underscore that successful integration relies not merely on the technical sophistication of the artificial intelligence models but fundamentally on the robust, auditable operational architecture established by the adopting institution. Ultimately, this framework provides healthcare administrators, clinical leaders, and policymakers with a systematic approach to navigate the complexities of generative artificial intelligence adoption while safeguarding clinical integrity.
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References
1. Chen, Z., Gao, P., Lee, Y., Barthelemy, J., Zhou, L., & Wang, L. (2025, June). Optimizing Efficiency and Visual-Textual Alignment for LLM-Based Radiology Report Generation. In 2025 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
2. Xia, X., Wang, X., Li, Q., Li, N., & Li, J. (2013). Essential amino acid enriched high-protein enteral nutrition modulates insulin-like growth factor-1 system function in a rat model of trauma-hemorrhagic shock. PLoS One, 8(10), e77823.
3. Sha, L., Wang, Y., Meng, P., Deng, Y., Chen, T., Zhang, X., ... & Xu, Q. (2025). Pharmacological inhibition of PSPH reduces serine levels and epileptic seizures. Nature Chemical Biology, 21(11), 1742-1753.
4. Zhao, K., Li, G., Li, C., Jiang, T., Wang, W., & Yang, Z. (2026). Identification Markers for Salvia miltiorrhiza and Its Close Relatives Based on Cell Wall Component Characteristics. Engineered Science, 40, 2122.
5. Gao, X. L., & Wang, M. (2026). Enhancing cancer immunotherapy antibody discovery: Genetic algorithms and risk-aware protein modeling. Available at SSRN 6033537.
6. Lyu, M., Torii, R., Liang, C., Zhang, X., Wang, X., Li, Q., ... & Chen, D. (2025). Predictive computational framework to provide a digital twin for personalized cardiovascular medicine. Communications Medicine, 5(1), 370.
7. Lyu, M., Torii, R., Liang, C., Li, Q., Wang, X., Ventikos, Y., & Chen, D. (2025). In silico comparison of two non-invasive pre-procedural virtual coronary revascularisation techniques for personalised cardiovascular medicine. Computer Methods and Programs in Biomedicine, 109046.
8. Yu, X., Zhang, Y., & Wang, H. (2025, July). Subjective Sleep Gains in Empty Nose Syndrome: A Cautionary Note on the Need for Objective Validation. In International Forum of Allergy & Rhinology (Vol. 15, No. 7, pp. 761-762).
9. Verhoven, Bret, et al. "Attenuating Ischemia and Reperfusion Injury Using NAD+-Loaded Nanoparticles in Mouse Kidneys." Transplantation Direct 12.1 (2025): e1890.
10. Yin, Li, et al. "Targeted NAD+ Delivery for intimal Hyperplasia and Re-endothelialization: a novel anti-restenotic therapy approach." bioRxiv (2024): 2024-02.
11. Sinha, D., Tong, Y., Zepeda, M. A. F., Hanstad, G., Nelson, E. C., Theisen, C. O., ... & Gamm, D. M. (2023). Silica nanocapsules as a nonviral delivery platform for iPSC-RPE.Investigative Ophthalmology & Visual Science,64(8), 774-774.
12. Kabra, Meha, et al. "Nonviral base editing of KCNJ13 mutation preserves vision in a model of inherited retinal channelopathy." The Journal of Clinical Investigation 133.19 (2023).
13. Xia, F., Li, B., An, B., Zachman, M. J., Xie, X., Liu, Y., ... & Cheng, Y. (2024). Cooperative Atomically Dispersed Fe–N4 and Sn–N x Moieties for Durable and More Active Oxygen Electroreduction in Fuel Cells. Journal of the American Chemical Society, 146(49), 33569-33578.
14. Hoy Jr, D., Kuang, Y., Asres, A., Liu, C. H., Dormidontova, E., & Nieh, M. P. (2025). Spontaneous Graphene/Lipid Bicellar Co-assembly (NANO2-Graphene): Experiments and Computer Simulations. ACS Applied Materials & Interfaces, 17(49), 66221-66234.
15. Chai, L., Zhang, J., Li, H., Zheng, B., Jiang, J., Cui, C., & Jiang, L. (2020). Investigation for a multi-silique trait in Brassica napus by alternative splicing analysis. PeerJ, 8, e10135.
16. Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning-based sequence model. Nature Methods, 12(10), 931–934.
17. De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., et al. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342–1350.
18. Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26(12), 1367–1372.
19. Yu, L., Li, J., Hong, J., Takashima, Y., Fujimoto, N., Nakajima, M., ... & Liu, L. (2018). Low cell-matrix adhesion reveals two subtypes of human pluripotent stem cells. Stem cell reports, 11(1), 142-156.
20. Wang, Z., Kim, E. H., Oh, S. K., Pedrycz, W., Fu, Z., & Yoon, J. H. (2024). Reinforced fuzzy-rule-based neural networks realized through streamlined feature selection strategy and fuzzy clustering with distance variation. IEEE Transactions on Fuzzy Systems, 32(10), 5674-5686.