Modernizing 834 Outbound Processing: Leveraging Incremental ETL Frameworks to Improve Health Plan Data Exchanges
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V3I2P103Keywords:
834 EDI, Incremental ETL, Health Insurance, Healthcare Data Exchange, Data Integration, Real-time ProcessingAbstract
834 EDI files are critical to the administration of health insurance enrollments. Traditional outbound processing methods are batch-based and often fail to deliver the real-time or near-real-time responsiveness needed by modern healthcare applications. This paper introduces an incremental extract, transform, and load (ETL) framework designed to optimize the outbound flow of 834 data. We present a scalable architecture, analyze performance improvements across latency, accuracy, and cost, and demonstrate applicability through real-world datasets from major health plans. Results show a 65% improvement in processing latency and a 30% cost reduction over batch-based models.
References
[1] M. Alakraa, “Development of an interoperable exchange, aggregation and analysis platform for health and environmental data,” 2017. [Online]. Available: https://epub.technikum-wien.at/obvftwhsmmig/ content/titleinfo/9753834/full.pdf
[2] S. K. R. Thumburu, “A comparative analysis of etl tools for large-scale edi data integration,” Journal of Innovative Technologies, vol. 3, no. 1, 2020. [Online]. Available: https://acadexpinnara.com/index.php/ JIT/article/view/399
[3] C. Soviany, “Ai-powered surveillance for financial markets and transactions,” Journal of Digital Banking, vol. 3, no. 4, pp. 319–329, 2019. [Online]. Available: https://www.ingentaconnect.com/content/hsp/ jdb001/2019/00000003/00000004/art00004
[4] E. Mehmood and T. Anees, “Challenges and solutions for processing real-time big data stream: A systematic literature review,” IEEE Access, vol. 8, pp. 119 123–119 143, 2020.
[5] M. Ouhssini, K. Afdel, M. Idhammad, and E. Agherrabi, “Distributed intrusion detection system in the cloud environment based on apache kafka and apache spark,” in 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), 2021, pp. 1–6.
[6] V. Chang and M. Ramachandran, “Towards achieving data security with the cloud computing adoption framework,” IEEE Transactions on Services Computing, vol. 9, no. 1, pp. 138–151, 2016.
[7] C. Lekkala, “The role of kubernetes in automating data pipeline operations: From development to monitoring,” Journal of Scientific and Engineering Research, vol. 8, no. 3, pp. 240–248, 2021. [Online]. Available: https://papers.ssrn.com/sol3/papers.cfm?abstractid=490837D. D. Sa´nchez-Gallegos, A. Galaviz-Mosqueda, J. L. Gonzalez- Compean, S. Villarreal-Reyes, A. E. Perez-Ramos, D. Carrizales- Espinoza, and J. Carretero, “On the continuous processing of health data in edge-fog-cloud computing by using micro/nanoservice composition,” IEEE Access, vol. 8, pp. 120 255–120 281, 2020.
[8] V. Safran, D. Hari, U. Ario¨z, and I. Mlakar, “Persist sensing network: A multimodal sensing network architecture for collection of patient- generated health data in the clinical workflow,” in 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 2021, pp. 1–6.X.-C.
[9] Chai, Q.-L. Wang, W.-S. Chen, W.-Q. Wang, D.-N. Wang, and Y. Li, “Research on a distributed processing model based on kafka for large-scale seismic waveform data,” IEEE Access, vol. 8, pp. 39 971– 39 981, 2020.
[10] S. K. R. Thumburu, “Large scale migrations: Lessons learned from edi projects,” Journal of Innovative Technologies, vol. 3, no. 1, 2020. [Online]. Available: https://acadexpinnara.com/index.php/ JIT/article/view/398
[11] J. Clark, “Verifying serializability protocols with version order recovery,” Master’s thesis, ETH Zurich, 2021. [Online]. Available: https://www.research-collection.ethz.ch/bitstream/handle/20. 500.11850/507577/2/Clark Jack.pdf
[12] M. Anwar and A. Imran, “Access control for multi-tenancy in cloud- based health information systems,” in 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing, 2015, pp. 104– 110.
[13] I. G. Cohen, S. Gerke, and D. B. Kramer, “Ethical and legal implications of remote monitoring of medical devices,” The Milbank Quarterly, vol. 98, no. 4, pp. 1257–1289, 2020. [Online]. Available: https://doi.org/10.1111/1468-0009.12481
[14] J. Liu, E. Braun, C. Du¨pmeier, P. Kuckertz, D. S. Ryberg, M. Robinius,
[15] D. Stolten, and V. Hagenmeyer, “A generic and highly scalable frame- work for the automation and execution of scientific data processing and simulation workflows,” in 2018 IEEE International Conference on Software Architecture (ICSA), 2018, pp. 145–14 510.
