Scaling Secure Healthcare Data Modernization: A Programmatic Approach
Main Article Content
Abstract
Programmatic and secure strategy of data system modernization of a healthcare organization is discussed in this paper. The article by using four qualitative case studies establishes the significance of leadership, governance, compliance and clinical collaboration on the outcomes of modernization. The findings demonstrate that the successful modernization depends on the transparent models of governance, standardization of the technical functioning as well as the effective system of the validation with clinicians and compliance teams. The other question that is brought up in the research is building trust and learning that takes place with constant cooperation. It offers a model upon which the healthcare facilities can apply to achieve the upgrade of data systems in a manner that is secure to ensure efficiency, auditability, and organizational maturity over the long term
Article Details
Section
How to Cite
References
[1] Lenert, L. A., Ilatovskiy, A. V., Agnew, J., Rudisill, P., Jacobs, J., Weatherston, D., & Deans, K. R., Jr. (2021). Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research. Journal of the American Medical Informatics Association, 28(8), 1605–1611. https://doi.org/10.1093/jamia/ocab108
[2] Dunn, W. D., Cobb, J., Levey, A. I., & Gutman, D. A. (2016). REDLetr: Workflow and tools to support the migration of legacy clinical data capture systems to REDCap. International Journal of Medical Informatics, 93, 103–110. https://doi.org/10.1016/j.ijmedinf.2016.06.015
[3] Adler-Milstein, J., Adelman, J. S., Tai-Seale, M., Patel, V. L., & Dymek, C. (2019). EHR audit logs: A new goldmine for health services research? Journal of Biomedical Informatics, 101, 103343. https://doi.org/10.1016/j.jbi.2019.103343
[4] Pageler, N. M., G’Sell, M. J. G., Chandler, W., Mailes, E., Yang, C., & Longhurst, C. A. (2016). A rational approach to legacy data validation when transitioning between electronic health record systems. Journal of the American Medical Informatics Association, 23(5), 991–994. https://doi.org/10.1093/jamia/ocv173
[5] Berges, I., Bermudez, J., Goñi, A., & Illarramendi, A. (2010). Semantic interoperability of clinical data. Semantic Interoperability of Clinical Data, 10–14. https://doi.org/10.1145/1866272.1866275
[6] Pasupuleti, M. K. (2023). Policy-as-Code AI: Auto-Remediation and FinOps Risk Guardrails with Cloud Custodian. International Journal of Academic and Industrial Research Innovations(IJAIRI), 03(06), 211–225. https://doi.org/10.62311/nesx/rp-30623-211-225
[7] Cascini, F., Santaroni, F., Lanzetti, R., Failla, G., Gentili, A., & Ricciardi, W. (2021). Developing a Data-Driven approach in order to improve the safety and quality of patient care. Frontiers in Public Health, 9, 667819. https://doi.org/10.3389/fpubh.2021.667819
[8] Meystre, S. M., Lovis, C., Bürkle, T., Tognola, G., Budrionis, A., & Lehmann, C. U. (2017). Clinical data reuse or secondary use: current status and potential future progress. Yearbook of Medical Informatics, 26(01), 38–52. https://doi.org/10.15265/iy-2017-007
[9] Petersen, C., Berner, E. S., Cardillo, A., Hollis, K. F., Goodman, K. W., Koppel, R., Korngiebel, D. M., Lehmann, C. U., Solomonides, A. E., & Subbian, V. (2022). AMIA’s code of professional and ethical conduct 2022. Journal of the American Medical Informatics Association, 30(1), 3–7. https://doi.org/10.1093/jamia/ocac192
[10] Lenert, L. A., Ilatovskiy, A. V., Agnew, J., Rudsill, P., Jacobs, J., Weatherston, D., & Deans, K. (2021). Automated Production of Research Data Marts from a Canonical Fast Healthcare Interoperability Resource (FHIR) Data Repository: Applications to COVID-19 Research. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2021.03.11.21253384