Continuous Claims Event Streaming for Healthcare Adjudication and Quality Analytics
Keywords:
Real-Time Healthcare Analytics, Event-Driven Data Streaming, Healthcare Claims Adjudication, Real-Time Claims Processing, Healthcare Quality Measurement, Clinical and Administrative Decision Support, Data Provenance and Instrumentation, Micro-Batch Streaming Architectures, Healthcare Performance Indicators, Closed-Loop Quality Improvement, Scalable Health Data Platforms, Anomaly Detection in Healthcare KPIs, Data Governance in Healthcare Analytics, Low-Latency Health Information Systems, Population-Scale Quality Monitoring.Abstract
Healthcare analytics is increasingly expected to operate in real-time. The demand for real-time queries and feedback has propelled the move toward event-driven data streaming architectures, which have been adopted in many industries—commerce, finance, social networking—but have yet to be fully realized in healthcare. Although challenges remain in isolating, modeling, and sharing the right data, there are clear motivations for both clinical care—with the potential for detecting and acting on sudden changes in patient health—and administrative use cases, with the prospect of providing proactive operations dashboards that alert business analysts to deepening problems that they can address before they escalate into costly crises. This paper focuses on two specific use cases: the real-time processing of healthcare claims as they are submitted and a framework for measuring, monitoring, and improving healthcare quality in real-time and at scale. The first addresses data provenance and instrumentation needs for the timely execution of the rules governing claims submission and adjudication. The second defines the requirements—timeliness, accuracy, completeness, and so on—for quality measurement at scale based on New York State Department of Health and Centers for Medicare & Medicaid Services performance indicators and establishes a framework for providing real-time feedback on adherence to these requirements.
Although a number of high-level components of a comprehensive real-time analytics architecture have been previously proposed or described, decision support, data , governance, and provisioning are still in their formative stages. Building from previous work outlining an event-driven, micro-batch-enabled architecture for real-time data streaming and analytics at scale, the present discussion focuses on claims adjudication and quality measurement use cases. Other descriptive aspects complete the taxonomy of real-time analytics building blocks. Technical requirements are then explicitly addressed for a claims processing framework that analyzes a claims life by identifying the key events, states, and timing constraints that drive the process, and for a real-time quality measurement system that detects anomalies across key performance indicators and provides closed-loop feedback for quality improvement.
References
1. Cheng, A. C., Duda, S. N., Taylor, R., Delacqua, F., et al. (2021). REDCap on FHIR: Clinical Data Interoperability Services. Journal of Biomedical Informatics, 121, 103871.
2. Chowdhury, M., Gasca Cervantes, E., Chan, W.-Y., & Seitz, D. P. (2021). Use of machine learning and artificial intelligence methods in geriatric mental health research involving electronic health record or administrative claims data: A systematic review. Frontiers in Psychiatry, 12, 738466.
3. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
4. Lo-Ciganic, W.-H., Donohue, J. M., Hulsey, E. G., Barnes, S., Li, Y., Kuza, C. C., Yang, Q., Buchanich, J., Huang, J. L., Mair, C., Wilson, D. L., & Gellad, W. F. (2021). Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach. PLOS ONE, 16(3), e0248360.
5. MacKay, E. J., Stubna, M. D., Chivers, C., Draugelis, M. E., Hanson, W. J., Desai, N. D., & Groeneveld, P. W. (2021). Application of machine learning approaches to administrative claims data to predict clinical outcomes in medical and surgical patient populations. PLOS ONE, 16(6), e0252585.
6. Tran, L., Chi, L., Bonti, A., Abdelrazek, M., & Chen, Y.-P. P. (2021). Mortality prediction of patients with cardiovascular disease using medical claims data under artificial intelligence architectures: Validation study. JMIR Medical Informatics, 9(4), e25000.
7. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
8. Richter, A., Truthmann, J., Chenot, J.-F., & Schmidt, C. O. (2021). Predicting physician consultations for low back pain using claims data and population-based cohort data—An interpretable machine learning approach. International Journal of Environmental Research and Public Health, 18(22), 12013.
9. van der Galiën, O. P., Hoekstra, R. C., Gürgöze, M. T., Manintveld, O. C., van den Bunt, M. R., Veenman, C. J., & Boersma, E. (2021). Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: A machine learning approach. BMC Medical Informatics and Decision Making, 21, 303.
10. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
11. Tseng, Y.-J., et al. (2021). Machine-learning-based predictions of direct-acting antiviral therapy duration for patients with hepatitis C. International Journal of Medical Informatics, 153, 104562.
12. Vimont, A., Leleu, H., & Durand-Zaleski, I. (2022). Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France. The European Journal of Health Economics, 23(2), 211–223.
13. Inala, R. (2023). AI-powered investment decision support systems: Building smart data products with embedded governance controls. Journal for ReAttach Therapy and Developmental Diversities, 6(10), 2251-2266.
14. Yasar, O., Long, P., Harder, B., Marshall, H., Bhasin, S., Lee, S., Delegge, M., Roy, S., Doyle, O., Leavitt, N., & Rigg, J. (2022). Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH). BMJ Health & Care Informatics, 29(1), e100510.
15. Lo-Ciganic, W.-H., et al. (2022). Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: A prognostic modelling study. The Lancet Digital Health, 4(6), e455–e465.
16. de Hond, T. A. P., Niemantsverdriet, M. S. A., van Solinge, W. W., Oosterheert, J. J., Haitjema, S., & Kaasjager, K. A. H. (2022). Sepsis labels defined by claims-based methods are ill-suited for training machine learning algorithms. Clinical Microbiology and Infection, 28(8), 1170–1171.
17. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950-5958.
18. Iqbal, M. S., Abd-Alrazaq, A., & Househ, M. (2022). Artificial intelligence solutions to detect fraud in healthcare settings: A scoping review. Studies in Health Technology and Informatics, 295, 20–23.
19. Settipalli, L., & Gangadharan, G. R. (2023). WMTDBC: An unsupervised multivariate analysis model for fraud detection in health insurance claims. Expert Systems with Applications, 215, 119259.
20. Langenberger, B., Schulte, T., & Groene, O. (2023). The application of machine learning to predict high-cost patients: A performance-comparison of different models using healthcare claims data. PLOS ONE, 18(1), e0279540.
21. Xie, F., Beukelman, T., Sun, D., Yun, H., & Curtis, J. R. (2023). Identifying inpatient mortality in MarketScan claims data using machine learning. Pharmacoepidemiology and Drug Safety, 32(11), 1299–1305.
22. KollIntegration. South Eastern European Journal of Public Health, 248–260.
23. Kural, K. C., Mazo, I., Walderhaug, M., Santana-Quintero, L., Karagiannis, K., Thompson, E. E., Kelman, J. A., & Goud, R. (2023). Using machine learning to improve anaphylaxis case identification in medical claims data. JAMIA Open, 6(4), ooad090.
24. Yang, S., Varghese, P., Stephenson, E., Tu, K., & Gronsbell, J. (2023). Machine learning approaches for electronic health records phenotyping: A methodical review. Journal of the American Medical Informatics Association, 30(2), 367–381.
25. Caruana, A., Bandara, M., Musial, K., Catchpoole, D., & Kennedy, P. J. (2023). Machine learning for administrative health records: A systematic review of techniques and applications. Artificial Intelligence in Medicine, 144, 102642.
26. 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.
27. Lewis, A. E., Weiskopf, N., Abrams, Z. B., Foraker, R., Lai, A. M., Payne, P. R. O., & Gupta, A. (2023). Electronic health record data quality assessment and tools: A systematic review. Journal of the American Medical Informatics Association, 30(10), 1730–1740.
28. Ozonze, O., Scott, P. J., & Hopgood, A. A. (2023). Automating electronic health record data quality assessment. Journal of Medical Systems, 47, 23.
29. Sendak, M., Sirdeshmukh, G., Ochoa, T., Premo, H., Tang, L., Niederhoffer, K., Reed, S., Deshpande, K., Sterrett, E., Bauer, M., Snyder, L., Shariff, A., Whellan, D., Riggio, J., Gaieski, D., Corey, K., Richards, M., Gao, M., Nichols, M., Heintze, B., Knechtle, W., Ratliff, W., & Balu, S. (2022). Development and validation of ML-DQA—A machine learning data quality assurance framework for healthcare. Proceedings of the 7th Machine Learning for Healthcare Conference, 182, 741–759.
30. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. Zenodo.
31. Lee, B.-H., et al. (2022). Blood transfusion, all-cause mortality and hospitalization period in COVID-19 patients: Machine learning analysis of national health insurance claims data. Diagnostics, 12(12), 2970.
32. Leiner, J., Pellissier, V., König, S., Hohenstein, S., Ueberham, L., Nachtigall, I., Meier-Hellmann, A., Kuhlen, R., Hindricks, G., & Bollmann, A. (2022). Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: Analysis of claims data from the German-wide Helios hospital network. Respiratory Research, 23, 264.
33. Krasowski, A., Krois, J., Kuhlmey, A., Meyer-Lueckel, H., & Schwendicke, F. (2022). Predicting mortality in the very old: A machine learning analysis on claims data. Scientific Reports, 12, 17464.
34. Yang, S., et al. (2022). Machine learning approaches for electronic health records phenotyping: A methodical review. Journal of the American Medical Informatics Association, 30(2), 367–381.
35. a, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data
36. Settipalli, L., & Gangadharan, G. R. (2022). A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology. Digital Applications in Archaeology and Cultural Heritage, 26, e00241.
37. Wu, B., et al. (2022). Improved prediction of body mass index in real-world administrative healthcare claims databases. Pharmacoepidemiology and Drug Safety, 31(8), 848–858.
38. Saito, T., Suzuki, H., & Kishi, A. (2022). Predictive modeling of mental illness onset using wearable devices and medical examination data: Machine learning approach. Frontiers in Digital Health, 4, 861808.
39. Payne, P. R. O., Gupta, A., Lewis, A. E., Weiskopf, N., Abrams, Z. B., Foraker, R., & Lai, A. M. (2023). Electronic health record data quality assessment and tools: A systematic review. Journal of the American Medical Informatics Association, 30(10), 1730–1740.
