Volume 2 Issue 1 | 2025 | View PDF
Paper Id:IJMSM-V2I1P106
doi: 10.71141/30485037/V2I1P106

Automating FHIR Compliance Audits with Large Language Models (LLMs) for Real-Time Healthcare Data Validation

Srinivas Bangalore Sujayendra Rao, Lalitha Amarapalli, Lakshmi Durga Panguluri

Citation:
Srinivas Bangalore Sujayendra Rao, Lalitha Amarapalli, Lakshmi Durga Panguluri, "Automating FHIR Compliance Audits with Large Language Models (LLMs) for Real-Time Healthcare Data Validation" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 1, pp. 53-62, 2025.

Abstract:
The digitization of healthcare records has revolutionized data exchange but introduced challenges in ensuring Fast Healthcare Interoperability Resources (FHIR) compliance, particularly with the heterogeneity of Electronic Health Records (EHRs) and unstructured data. Traditional compliance audits often require significant time and effort, prone to errors and inefficient (given the complexity and volume) healthcare data, but this study derives Large language models to automate FHIR compliance audits to perform real time validation of structured and unstructured healthcare data. The modern models leveraging advanced natural language processing techniques tackle real world problems like data mapping and interoperability testing, as well as regulatory compliance via frameworks such as HIPAA and GDPR. This allows for automation of key processes such as data analysis, anomaly detection, and compliance reporting which in turn helps improve accuracy, scale and efficiency with minimal manual audits but ethically significant check like bias, accountability and data privacy are still the prime concerns. For building trust in automated system it is imperative to create fair and transparent AI driven compliance solutions. The more recent advances in blockchain, edge computing, as well as federated learning appear to be very promising in enhancing the security of data, real time processing and decentralized compliance monitoring for health care organizations. When these advancements are leveraged, health care organizations can build more trustworthy and adaptive compliance frameworks which can improve global health care interoperability. Automated FHIR compliance audits have the ability to transform regulatory adherence in terms of leveraging streamlining, data integrity, and enabling innovation in healthcare data management as this research suggests.

Keywords: FHIR Compliance, Large Language Models (LLMs), Healthcare Interoperability, Electronic Health Records (EHRs).

References:
[1] European Institute of Innovation and Technology, "Future-proofing Europe’s digital health innovation pathway," 2019. [Online]. Available: https://eit.europa.eu/library/round-table-series-recommendations-future-proofing-europe's-digital-health-innovation. [Accessed: Dec. 31, 2023].
[2] M. L. Taylor, E. E. Thomas, and K. Vitangcol, "Digital health experiences reported in chronic disease management: An umbrella review of qualitative studies," J. Telemed. Telecare, vol. 28, no. 10, pp. 705–717, 2022.
[3] E. Chukwu, L. Garg, and V. K. Chattu, "Profiling and validating Fast Healthcare Interoperability Resource for maternal and neonatal child health referrals at primary healthcare level through BlockMom," Intelligent Biomedical Technologies and Applications for Healthcare 5.0, vol. 16, Advances in Ubiquitous Sensing Applications for Healthcare, pp. 1–10, 2025.
[4] HL7, "Fast Health Care Interoperability Resources (FHIR)," 2023. [Online]. Available: https://www.hl7.org/fhir/. [Accessed: Dec. 31, 2023].
[5] J. L. Fernández-Alemán, I. C. Señor, P. Á. O. Lozoya, and A. Toval, "Security and privacy in electronic health records: A systematic literature review," J. Biomed. Inform., vol. 46, pp. 541–562, 2013.
[6] D. Heisey-Grove, L.-N. Danehy, M. Consolazio, K. Lynch, and F. Mostashari, "A national study of challenges to electronic health record adoption and meaningful use," Med. Care, vol. 52, pp. 144–148, 2014.
[7] P. N. Ahmad, A. M. Shah, and K. Lee, "A review on electronic health record text-mining for biomedical name entity recognition in healthcare domain," Healthcare, vol. 11, no. 1268, 2023.
[8] Office for Civil Rights, "HIPAA for Professionals," 2021. [Online]. Available: https://www.hhs.gov/hipaa/for-professionals/index.html.
[9] ONC, "21st Century Cures Act: Interoperability, Information Blocking, and the ONC Health IT Certification Program," 2020. [Online]. Available: https://www.healthit.gov/curesrule/.
[10] HL7 International, "FHIR Overview," 2023. [Online]. Available: https://www.hl7.org/fhir/overview.html.
[11] J. C. Mandel, D. A. Kreda, K. D. Mandl, I. S. Kohane, and R. B. Ramoni, "SMART on FHIR: A standards-based, interoperable apps platform for electronic health records," J. Am. Med. Inform. Assoc., vol. 23, no. 5, pp. 899–908, 2016.
[12] D. Bender and K. Sartipi, "HL7 FHIR: An agile and RESTful approach to healthcare information exchange," in Proc. 26th IEEE Int. Symp. on Computer-Based Medical Systems, 2013.
[13] D. McGraw, K. D. Mandl, and P. Cerrato, "Privacy and security in the era of FHIR," J. Am. Med. Inform. Assoc., vol. 26, no. 2, pp. 105–107, 2019.
[14] T. Benson and G. Grieve, Principles of Health Interoperability: SNOMED CT, HL7 and FHIR, Springer, 2016.
[15] A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems, vol. 30, 2017.
[16] T. B. Brown et al., "Language models are few-shot learners," in Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020.
[17] L. Y. Jiang et al., "Health system-scale language models are all-purpose prediction engines," Nature, vol. 619, no. 7969, pp. 357–362, 2023.
[18] D. McGraw, K. D. Mandl, and P. Cerrato, "Privacy and security in the era of FHIR," J. Am. Med. Inform. Assoc., vol. 26, no. 2, pp. 105–107, 2019.
[19] E. J. Topol, "High-performance medicine: The convergence of human and artificial intelligence," Nature Med., vol. 25, no. 1, pp. 44–56, 2019.
[20] Y. Wang et al., "Clinical information extraction applications: A literature review," J. Biomed. Inform., vol. 77, pp. 34–49, 2020.
[21] A. Palanica, P. Flaschner, A. Thommandram, M. Li, and Y. Fossat, "Physicians' perceptions of chatbots in health care: Cross-sectional web-based survey," J. Med. Internet Res., vol. 21, no. 4, e12887, 2019.
[22] Florida Agency for Health Care Administration (AHCA), "HIPAA Compliance Audit Report," 2023. [Online]. Available: https://www.floridahealth.gov.
[23] Massachusetts Health Data Consortium (MHDC), "Pilot Projects on LLMs for Healthcare Interoperability," 2023. [Online]. Available: https://www.mahealthdata.org.
[24] Texas Medical Association (TMA), "Coding Challenges in Texas Healthcare Systems," 2023. [Online]. Available: https://www.texmed.org.