Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms
Abstract
Evidence-based, formal, and objective synthesis focusing on data governance, platform architecture, data domains, workflows, and use cases to advance cohesive medical insights.
Three healthcare intelligence functions—member intelligence, provider intelligence, and claims intelligence—operate in near isolation, often leading to suboptimal quality, outcomes, and costs; inconsistency and redundancy in operating procedures; and missed regulatory requirements. Architecting a data-driven healthcare operations ecosystem demands that these three data sources be closely integrated to deliver richer intelligence than they can provide separately. Interoperability frameworks help clarify which components need to be aligned: data criteria, workflows and use cases, documents, reporting formats, distribution lists, technologies, and standards. An implementation roadmap with clear milestones, major dependencies, and cross-functional integration points enhances stakeholder communication and commitment.
Critical constituent elements of the intelligence solutions are mapped to an integrated architectural design for voice-of-the-customer (VOC) alignment, a core mapping of common healthcare business capability domains, and a template for VOC blueprinting for these domains, together covering the full breadth of Population Health, Care Management, Care Delivery, Value-Based Care, Cost Accounting for Care Delivery, Reimbursement, External Business Intelligence, and Internal Business Intelligence. The six considerations recommended for any architecture design apply to the three main intelligence modules—and, by extension, the entire health-system architecture.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 5 No. 5 (2022): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
8511-8521 |
Published |
October 3, 2022 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Venkata Akhilesh Ranga Reddy (%2022). Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms. International Journal of Science, Research and Technology , Vol. 5 No. 5 (2022): International Journal of Science, Research and Technology (IJSRAT) , pp. 8511-8521. https://doi.org/10.15662/IJSRAT.2022.0505004 |
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[22] Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1-14.
[23] Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 54.
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[36] Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
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[48] Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1), 1–23.
[49] Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
[50] Haux, R. (2010). Medical informatics: Past, present, future. International Journal of Medical Informatics, 79(9), 599–610.
[51] Friedman, C. P., Allee, N. J., Delaney, B. C., Flynn, A. J., Silverstein, J. C., Sullivan, K., & Brantley, K. L. (2020). The science of learning health systems: Foundations for a new journal. Learning Health Systems, 4(1), e10203.
[52] Hripcsak, G., Duke, J. D., Shah, N. H., Reich, C. G., Huser, V., Schuemie, M. J., Suchard, M. A., Park, R. W., Wong, I. C. K., Rijnbeek, P. R., van der Lei, J., Pratt, N., Norén, G. N., Li, Y. C., Stang, P. E., Madigan, D., & Ryan, P. B. (2015). Observational Health Data Sciences and Informatics (OHDSI): Opportunities for observational researchers. Studies in Health Technology and Informatics, 216, 574–578.
[53] Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1-19.
[54] Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243.
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[56] Provost, F., & Fawcett, T. (2020). Data science for business (2nd ed.). O’Reilly Media.
[57] Keesara, S., Jonas, A., & Schulman, K. (2020). Covid-19 and health care’s digital revolution. New England Journal of Medicine, 382(23), e82.
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[59] Bodenreider, O. (2004). The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Research, 32(Database Issue), D267–D270.
[60] Kho, A. N., Cashy, J. P., Jackson, K. L., Pah, A. R., Goel, S., Boehnke, J., Humphries, J. E., Kominers, S. D., Hota, B., Sims, S. A., Malin, B. A., French, D. D., & Meltzer, D. O. (2015). Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. Journal of the American Medical Informatics Association, 22(5), 1072–1080.
[61] Vest, J. R., & Gamm, L. D. (2010). Health information exchange: Persistent challenges and new strategies. Journal of the American Medical Informatics Association, 17(3), 288–294.
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