AI-Powered FHIR Interoperability and Risk Analytics
Keywords:
Generative AI, FHIR interoperability, real-time exchange, risk-aware analytics, semantic alignment, data standardization, uncertainty quantification, explainability.Abstract
Healthcare information belongs to individuals and communities, and it is imperative to govern control of data stewardship. At the same time, for healthcare analytics, it is paramount to obtain accurate patient data in real time from multiple data custodians. The current data-integration approach usually focuses on the data-accessing aspect; thus, data are retrieved but not truly exchanged or shared. Supporting technologies inherently incorporate privacy and risk governance principles. Integration of natural-language-processing and ontology-matching techniques can enable a semantically aware approach to automate the detection of schema mapping rules—all aspects involved in FHIR-compliant exchange. The healthcare data-integrity perspective insists that data must be accurate, current, and reliable at all times.
Working toward achieving this forecasted objective, a novel risk framework that aims not only to enable real-time exchange but also to ensure that data are correct, consistent, and reliable is introduced. The methodology identifies data-quality-attribute issues in MongoDB repositories as an external risk for patient-safety-related healthcare-decisions-support. Six main healthcare-support-enabling qualities of data are considered during risk. These quality attributes—privacy, data availability, data accuracy, data robustness, system timeliness, and data content-supporting quality-sensitive-analysis decisions—are evaluated, and thresholds are defined to prioritize risks. Risk indicators become part of patient safety management by auditing and monitoring data sources; thus, healthcare analytics can provide real-time analyses that are risk-aware.
References
1. Ayaz, M., Pasha, M. F., Alahmadi, T. J., Abdullah, N. N. B., & Alkahtani, H. K. (2023). Transforming healthcare analytics with FHIR: A framework for standardizing and analyzing clinical data. Healthcare, 11(12), 1729.
2. Balch, J. A., Ruppert, M. M., Loftus, T. J., Guan, Z., Ren, Y., Upchurch, G. R., Ozrazgat-Baslanti, T., Rashidi, P., & Bihorac, A. (2023). Machine learning–enabled clinical information systems using Fast Healthcare Interoperability Resources data standards: Scoping review. JMIR Medical Informatics, 11, e48297.
3. Williams, E., Kienast, M., Medawar, E., Reinelt, J., Merola, A., Klopfenstein, S. A. I., Flint, A. R., Heeren, P., Poncette, A.-S., Balzer, F., Beimes, J., von Bünau, P., Chromik, J., Arnrich, B., Scherf, N., & Niehaus, S. (2023). A standardized clinical data harmonization pipeline for scalable AI application deployment (FHIR-DHP): Validation and usability study. JMIR Medical Informatics, 11, e43847.
4. Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
5. Kramer, M. A., & Moesel, C. (2023). Interoperability with multiple Fast Healthcare Interoperability Resources (FHIR®) profiles and versions. JAMIA Open, 6(1), ooad001.
6. Nan, J., & Xu, L.-Q. (2023). Designing interoperable health care services based on Fast Healthcare Interoperability Resources: Literature review. JMIR Medical Informatics, 11, e44842.
7. Tseng, T.-W., Su, C.-F., & Lai, F. (2022). Fast Healthcare Interoperability Resources for inpatient deterioration detection with time-series vital signs: Design and implementation study. JMIR Medical Informatics, 10(10), e42429.
8. Vorisek, C. N., Lehne, M., Klopfenstein, S. A. I., Mayer, P. J., Bartschke, A., Haese, T., & Thun, S. (2022). Fast Healthcare Interoperability Resources (FHIR) for interoperability in health research: Systematic review. JMIR Medical Informatics, 10(7), e35724.
9. Gruendner, J., Gulden, C., Kampf, M., Mate, S., Prokosch, H.-U., & Zierk, J. (2021). A framework for criteria-based selection and processing of Fast Healthcare Interoperability Resources (FHIR) data for statistical analysis: Design and implementation study. JMIR Medical Informatics, 9(4), e25645.
10. Ayaz, M., Pasha, M. F., Alzahrani, M. Y., Budiarto, R., & others. (2021). The Fast Health Interoperability Resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities. JMIR Medical Informatics, 9(7), e21929.
11. Wettstein, R., Hund, H., Fegeler, C., & Heinze, O. (2021). Data sharing in distributed architectures—Concept and implementation in HiGHmed. Studies in Health Technology and Informatics, 281, 91–95.
12. Klopfenstein, S. A. I., Vorisek, C. N., Shutsko, A., Lehne, M., Sass, J., Löbe, M., Schmidt, C. O., & Thun, S. (2021). Fast Healthcare Interoperability Resources (FHIR) in a FAIR metadata registry for COVID-19 research. Studies in Health Technology and Informatics, 281, 111–115.
13. Aravind, R., Surabhi, S. N. D., & Shah, C. V. (2023). Remote Vehicle Access: Leveraging Cloud Infrastructure for Secure and Efficient OTA Updates with Advanced AI. EuropeanEconomic Letters (EEL), 13 (4), 1308–1319. Retrieved fromhttps.
14. Sun, H., De Praetere, K., von Dossow, V., Hulde, N., Szymanowsky, R., & others. (2021). A scalable approach for developing clinical risk prediction applications in different hospitals. Journal of Biomedical Informatics, 117, 103783.
15. Schrempf, M., Kramer, D., Jauk, S., Veeranki, S. P. K., Leodolter, W., & Rainer, P. P. (2021). Machine learning based risk prediction for major adverse cardiovascular events. Studies in Health Technology and Informatics, 279, 136–143.
16. Huang, C., Li, S.-X., Caraballo, C., Masoudi, F. A., Rumsfeld, J. S., Spertus, J. A., Normand, S.-L. T., Mortazavi, B. J., & Krumholz, H. M. (2021). Performance metrics for the comparative analysis of clinical risk prediction models employing machine learning. Circulation: Cardiovascular Quality and Outcomes, 14(10), e007526.
17. González-Del-Hoyo, M., & Rossello, X. (2021). Challenges and promises of machine learning-based risk prediction modelling in cardiovascular disease. European Heart Journal—Acute Cardiovascular Care, 10(8), 866–868.
18. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
19. Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5(1), 1–19.
20. Bompelli, A., Wang, Y., Wan, R., Singh, E., Zhou, Y., Xu, L., Oniani, D., Agnikula Kshatriya, B. S., Balls-Berry, J. E. E., & Zhang, R. (2021). Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review. Computational and Structural Biotechnology Journal, 19, 9759–9767.
21. Lei, H., Zhang, M., Wu, Z., Liu, C., Li, X., Zhou, W., Long, B., Ma, J., Zhang, H., Wang, Y., Wang, G., Gong, M., Hong, N., Liu, H., & Wu, Y. (2022). Development and validation of a risk prediction model for venous thromboembolism in lung cancer patients using machine learning. Frontiers in Cardiovascular Medicine, 9, 845210.
22. Shetty, M. K., Kunal, S., Girish, M. P., Qamar, A., Arora, S., Hendrickson, M., Mohanan, P. P., Gupta, P., Ramakrishnan, S., Yadav, R., Bansal, A., Zachariah, G., Batra, V., Bhatt, D. L., Gupta, A., & Gupta, M. (2022). Machine learning based model for risk prediction after ST-Elevation myocardial infarction: Insights from the North India ST elevation myocardial infarction (NORIN-STEMI) registry. International Journal of Cardiology, 362, 6–13.
23. Pal, M., Parija, S., Panda, G., Dhama, K., & Mohapatra, R. K. (2022). Risk prediction of cardiovascular disease using machine learning classifiers. Open Medicine, 17(1), 1100–1113.
24. Coombs, L., Orlando, A., Wang, X., Shaw, P., Rich, A. S., Lakhtakia, S., Titchener, K., Adamson, B., Miksad, R. A., & Mooney, K. (2022). A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology. npj Digital Medicine, 5, 117.
25. Aitha, A. R. (2021). Dev Ops Driven Digital Transformation: Accelerating Innovation In The Insurance Industry. Journal of International Crisis and Risk Communication Research.
26. Tashman, A. P. (2022). Practical implementation and challenges of artificial intelligence-driven electronic health record evaluation: Protected health information. Advances in Chronic Kidney Disease, 29(5), 427–430.
27. Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2022). Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues. Cluster Computing, 25, 1–41.
28. Salim, M. M., & Park, J. H. (2023). Federated learning-based secure electronic health record sharing scheme in medical informatics. IEEE Journal of Biomedical and Health Informatics, 27(2), 617–624.
29. Jung, J., Lee, H., Jung, H., & Kim, H. (2023). Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review. Heliyon, 9(5), e16110.
