Engineering Scalable AI Analytics for Electronic Health Records
Keywords:
EHR Analytics, Precision Care, Health Data, Data Ingestion, Data Pipelines, Data Preprocessing, Data Lakes, Cloud Systems, On Premise, Clinical Analytics, Patient Stratification, Treatment Optimization, Machine Learning, Model Retraining, Data Sharing, Reproducibility, Population Health, Atrial Fibrillation, Emergency Care, Clinical Decision.Abstract
Research aims, methods, key results, and implications for scalable EHR analytics and precision care. Electronic Health Records (EHRs) are a historical, comprehensive, and continually updated source of health data for patients. Although EHRs have been widely used for retrospective population studies, research prototypes, and clinical decision support systems, the actual uptake of AI-driven technologies in clinical practice and patient care remains limited. Automatic ingestion of clinical data, standardized data lakes, and periodic model retraining are essential for seamless operation and scalable usage. The objectives of this research are to design data ingestion and preprocessing pipelines that run automatically, to support cloud- and on-premises-based clinical analytics, and to apply patient stratification and personalized treatment optimization methods.
The proposed technologies are scalable, reproducible, and support precision healthcare delivery, with a global public health perspective. The results of the clinical data engineering effort are publicly available. Reproducibility was achieved by adhering to open-source principles, data-sharing best practices, and the use of common data-collection and storage formats. Population analyses demonstrated predefined hypotheses in the domain of obesity and revealed new insights into atrial fibrillation. In acute care and emergency medicine, AI-supported rapid data ingestion and the clinical application of complex models during patient triage were successfully demonstrated. Although deployment-specific bias was identified in these specific domains, adequate technical control of both cloud-based and on-premises configurations was supported.
References
1. Fridgeirsson, E. A., Sontag, D., & Rijnbeek, P. (2023). Attention-based neural networks for clinical prediction modelling on electronic health records. BMC Medical Research Methodology, 23(1), 285.
2. Mukherjee, P., Humbert-Droz, M., Chen, J. H., & Gevaert, O. (2023). SCOPE: Predicting future diagnoses in office visits using electronic health records. Scientific Reports, 13, 11005.
3. Bhavani, B. D., SR, S., Loganathan, R., & Nagaraj, S. (2026, April). Evolutionary Gravitational Neocognitron Neural Network, Snow Leopard Optimization and Deep Graph Reinforcement Learning for Routing Protocol in WSN. In 2026 2nd International Conference on Intelligent Systems and Computational Networks (ICISCN) (pp. 1-8). IEEE.
4. Yoon, J., Mizrahi, M., Ghalaty, N. F., et al. (2023). EHR-Safe: Generating high-fidelity and privacy-preserving synthetic electronic health records. npj Digital Medicine, 6(1), 141.
5. Bhaskhar, N., Ip, W., Chen, J. H., & Rubin, D. L. (2023). Clinical outcome prediction using observational supervision with electronic health records and audit logs. Journal of Biomedical Informatics, 147, 104522.
6. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
7. Hossain, E., Rana, R., Higgins, N., et al. (2023). Natural language processing in electronic health records in relation to healthcare decision-making: A systematic review. Journal of Biomedical Informatics, 146, 104471.
8. 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, 102641.
9. Kumar, S. S., Garapati, R. S., Segireddy, A. R., Kalisetty, S., Inala, R., & Nagabhyru, K. C. (2026). Hybrid Deep Neural Network–DevOps Pipeline Optimization for Risk Prediction in Cloud-Native Workers’ Compensation Platforms. In 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON) (pp. 1–6). IEEE. 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON). https://doi.org/10.1109/i3ctcon68242.2026.11507219
10. Lee, S., Park, H., & Kim, J. (2023). Scalable transformer architectures for longitudinal EHR sequence modeling. IEEE Journal of Biomedical and Health Informatics, 27(11), 5408–5419.
11. Wang, Y., Xu, H., & Jiang, M. (2023). Large-scale clinical language models for EHR analytics. Journal of the American Medical Informatics Association, 30(12), 2190–2201.
12. Zhao, T., Liu, Q., & Honavar, V. (2023). Deep temporal representation learning for scalable healthcare analytics. IEEE Access, 11, 87654–87668.
13. Kumar, R., Singh, A., & Patel, M. (2023). Distributed analytics for large-scale healthcare data systems. Future Generation Computer Systems, 145, 115–128.
14. Sudha Rani, P. R., Amistapuram, K., Pamisetty, V., Singireddy, S., Kummari, D. N., & Sheelam, G. K. (2025). Hybrid Knowledge Graph–Deep Learning Framework for Automated Exception Handling and Investigation in Complex Insurance Claims. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1–6). IEEE. 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN). https://doi.org/10.1109/gcwcn66157.2025.11448301
15. Zhang, L., Chen, Y., & Xu, W. (2023). Federated learning for secure EHR intelligence. Computers in Biology and Medicine, 161, 107016.
16. Li, X., Sun, J., & Wang, F. (2023). Explainable AI for clinical risk prediction from EHR data. Artificial Intelligence in Medicine, 140, 102548.
17. Dinsa, E. F., Das, M., & Abebe, T. U. (2024). AI-enabled electronic health record analytics: Challenges and future directions. Healthcare Analytics, 5, 100231.
18. Krishnan, M., Aitha, A. R., Amistapuram, K., Nandan, B. P., Kaulwar, P. K., & Singireddy, J. (2025). Human-in-the-Loop Hybrid Neuro-Symbolic AI Model for Reliable Data Engineering in High-Stakes Industrial Systems. In 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN) (pp. 1–7). IEEE. 2025 IEEE 3rd Global Conference on Wireless Computing and Networking (GCWCN). https://doi.org/10.1109/gcwcn66157.2025.11448516
19. Brown, T., Miller, S., & Anderson, K. (2024). Scalable cloud-native architectures for medical analytics pipelines. IEEE Cloud Computing, 11(2), 33–45.
20. Ahmed, S., Khan, M., & Noor, R. (2024). Real-time EHR analytics using event-driven architectures. Software: Practice and Experience, 54(4), 812–829.
21. Patel, N., Shah, D., & Verma, A. (2024). Streaming analytics for ICU patient monitoring. Sensors, 24(5), 2114.
22. Gupta, D. K., Purushotham, K., Dheer, G., P, S., Gottimukkala, V. R. R., & Kapoor, S. (2025). Semantic Feature Learning Using Transformer-Based Deep Neural Networks. In 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–6). IEEE. 2025 IEEE 5th International Conference on ICT in Business Industry & Government (ICTBIG). https://doi.org/10.1109/ictbig68706.2025.11323734
23. Green, J., Taylor, M., & Lopez, R. (2024). AI-based predictive healthcare using multimodal patient records. Nature Digital Medicine, 7(1), 92.
24. Gupta, P., Reddy, V., & Sharma, K. (2024). Scalable feature engineering for clinical machine learning. Expert Systems with Applications, 240, 122351.
25. Chen, H., Li, J., & Sun, Y. (2024). Distributed graph neural networks for healthcare knowledge extraction. Information Sciences, 672, 120613.
26. Rao, S., Iyer, P., & Menon, S. (2024). Big data engineering for population-scale health analytics. Journal of Big Data, 11(1), 88.
27. Lakhanpal, S., Tunjungsari, H. K., Raj, S., Chukka, A., Dawadi, D., & Mangalampalli, B. M. (2026, April). The Digital Transformation of Healthcare: Balancing Opportunities, Risks, and Ethical Considerations. In 2026 International Conference on Multidisciplinary Innovations For Smart & Sustainable Future (MISSF) (pp. 1-6). IEEE.
28. Wilson, G., Moore, L., & Davis, C. (2024). MLOps pipelines for clinical AI deployment. Machine Learning with Applications, 15, 100522.
29. Thomas, E., Baker, J., & Collins, P. (2024). Data lake architectures for healthcare intelligence. Future Internet, 16(3), 91.
30. Garcia, M., Perez, A., & Santos, L. (2024). Privacy-preserving EHR data sharing with differential privacy. Health Informatics Journal, 30(2), 1–18.
31. Lin, Z., Huang, Y., & Wu, Q. (2024). Edge AI for hospital analytics systems. IEEE Internet of Things Journal, 11(9), 15122–15134.
32. Park, J., Kim, D., & Choi, S. (2024). GPU-accelerated large-scale medical analytics. Concurrency and Computation, 36(8), e7924.
33. Raj, S., Prashanthi, P., Kolla, S. K., Bandaru, S., & Bhardwaj, N. (2026, April). Lightweight Cryptographic Schemes for Resource-Constrained IoT and Healthcare Devices. In 2026 International Conference on Multidisciplinary Innovations For Smart & Sustainable Future (MISSF) (pp. 1-6). IEEE.
34. Singh, R., Narayanan, A., & Das, S. (2024). Explainable multimodal AI in healthcare records. Artificial Intelligence Review, 57(4), 176.
35. Martin, P., Lewis, J., & Hall, B. (2024). Benchmarking scalable EHR analytics platforms. ACM Computing Surveys, 57(1), 14.
36. Zhou, Y., Xu, L., & Wang, R. (2024). NLP-based clinical summarization using foundation models. Journal of Biomedical Informatics, 151, 104612.
37. Mangala, S. B. G. N., Kolla, S. K., Segireddy, A. R., & Yandamuri, U. S. (2026). Designing Intelligent, Scalable Data Ecosystems for Precision Healthcare: A Cloud-Native Approach to Predictive and Automated Decision Systems. Advanced Engineering Sciences, 58(1), 2138-2152.
38. Johnson, T., Walker, H., & Young, R. (2024). Scalable AI governance for healthcare systems. Health Policy and Technology, 13(2), 100801.
39. Evans, C., Brown, K., & Reed, D. (2024). Resource-efficient transformer training for clinical datasets. Neural Computing and Applications, 36(14), 7889–7904.
40. Ren, W., Zhu, J., Liu, Z., Zhao, T., & Honavar, V. (2025). A comprehensive survey of electronic health record modeling: From deep learning approaches to large language models. ACM Computing Surveys, 58(2), 41.
41. Mattaparthi, R. (2022). From Raw Sensor to Business Signal: An Azure Databricks Framework for Diesel Engine Performance Analytics Across Global Fleet Operations. Journal of Artificial Intelligence & Cloud Computing, 1(4), 1. https://doi.org/10.47363/jaicc/2022(1)529
42. Domingo-Aldama, A. G., Prado, M. M., et al. (2025). Leveraging electronic health records for cohort generation. Health Information Science and Systems, 13(1), 24.
43. Miller, D., Brown, S., & Carter, J. (2025). Foundation models for scalable clinical analytics. Nature Machine Intelligence, 7(3), 210–223.
44. Zhang, H., Li, C., & Wang, Y. (2025). Large language models for clinical reasoning over EHRs. NPJ Health Systems, 2(1), 19.
45. Sharma, K., Gupta, P., & Verma, R. (2025). Semantic vector databases for healthcare retrieval systems. Information Processing & Management, 62(1), 103812.
46. Lee, K., Song, M., & Han, J. (2025). Retrieval-augmented generation for medical records. Artificial Intelligence in Medicine, 151, 102903.
47. Mangalampalli, B. M., & Kolla, T. (2026). FHIR-Based Interoperability Frameworks For Real-Time Healthcare Data Exchange: Architecture Patterns And Performance Optimization. International Journal Of Advances in Signal and Image Sciences, 1514-1536.
48. Robinson, J., Smith, L., & Parker, H. (2025). Agentic AI workflows in clinical analytics. IEEE Intelligent Systems, 40(2), 88–99.
49. Chen, F., Zhao, X., & Liu, H. (2025). Self-supervised learning for longitudinal patient trajectories. Pattern Recognition, 157, 110912.
50. Singh, V., Ramesh, N., & Patel, D. (2025). Scalable healthcare lakehouse architectures. Journal of Cloud Computing, 14(1), 31.
51. White, A., Hall, D., & Reed, B. (2025). AI observability in regulated healthcare systems. Software Quality Journal, 33(2), 505–526.
52. Kolla, S. H., & Peddi, R. K. (2024). Designing Governance-Aligned GenAI Pipelines Using Small Language Models for Enterprise Workflow Intelligence. International Journal of Science, Research and Technology, 7(6), 13256-13268.
53. Torres, M., & Silva, J. (2025). Synthetic data generation for EHR model scalability. Data Mining and Knowledge Discovery, 39(1), 55–82.
54. Kumar, S., & Jain, V. (2025). Real-time anomaly detection in hospital data streams. Expert Systems, 42(1), e13820.
55. Peterson, G., & Clark, E. (2025). Scalable GPU clusters for healthcare foundation models. Future Generation Computer Systems, 159, 200–214.
56. Kulkarni, K. (2026). Building Context-Aware Enterprise Intelligence: A Secure and Scalable Architecture for AI-Augmented Data Systems. Minnesota Journal of Business Law and Entrepreneurship, (1), 1315-1336.
57. Rao, P., & Iyer, M. (2025). Federated transformers for hospital collaboration. IEEE Transactions on Knowledge and Data Engineering, 37(4), 1910–1923.
58. Lewis, M., & Brown, J. (2025). Secure multimodal analytics over EHR and imaging datasets. Computers in Biology and Medicine, 181, 109342.
59. Dinsa, E. F., Das, M., & Abebe, T. U. (2026). Advances in electronic health records enabled by artificial intelligence and natural language processing. Discover Applied Sciences, 8(3), 250.
60. Sivarajkumar, S., Zhang, H., Ji, Y., et al. (2026). A multimodal generative model for structured and unstructured electronic health records. npj Health Systems, 3, 46.
61. Rani, S., Thavara, S. S., Kr, A., Naguband, A. R., Garg, A., & Vajpayee, A. Financialization of Sustainability: ESG Signalling, Market Valuation and the New Economics of Corporate Legitimacy.
62. Gowramma, P. B., Kumar, K., Prema, M. S., et al. (2026). AI-powered chatbot integration for enhanced accessibility of electronic health records. Scientific Reports, 16, 13287.
63. Hassan, A., Noor, M., & Ali, S. (2026). Autonomous analytics pipelines for EHR intelligence. IEEE Access, 14, 32011–32029.
64. Taylor, D., Morgan, S., & Bell, J. (2026). Healthcare data mesh for enterprise AI analytics. Journal of Systems Architecture, 150, 103067.
65. Kim, S., Park, H., & Lee, D. (2026). Scalable vector search for clinical retrieval. Knowledge-Based Systems, 312, 113112.
66. Madhavi, B., Kolla, S. K., & Gari, V. A. K. R. R. (2026). Comment on" Using mobile applications for body composition analysis: A technical review of an artificial intelligence-based tool". Clinical nutrition ESPEN, 103365.
67. Roberts, E., & Cooper, M. (2026). Clinical foundation models at scale. Nature Biomedical Engineering, 10(1), 44–59.
68. Wang, Q., & Zhou, Y. (2026). Multimodal transformers for patient journey intelligence. Pattern Recognition Letters, 192, 65–79.
69. Evans, L., Brown, T., & Jones, M. (2026). AI observability frameworks for healthcare LLM systems. Artificial Intelligence Review, 59(2), 88.
70. Patel, A., & Shah, R. (2026). Distributed embeddings for hospital-scale search systems. Information Systems, 129, 102511.
71. Martin, L., Hall, P., & Lewis, D. (2026). LLM orchestration for electronic medical intelligence. IEEE Software, 43(2), 71–80.
72. Zhao, Y., Li, J., & Sun, X. (2026). Retrieval-enhanced clinical reasoning systems. Journal of Biomedical Informatics, 172, 105019.
73. Mangala, N. (2026). Responsible AI Data Architecture: Embedding GDPR and PII Compliance into MLOps Pipelines at Enterprise Scale. Canadian Journal of Marketing Research, 16(1), 107-124.
74. Clark, H., Adams, P., & Young, S. (2026). Agentic decision support over healthcare data platforms. Decision Support Systems, 188, 114410.
75. Singh, N., & Patel, K. (2026). Event-driven AI engineering for hospital analytics. Software: Practice and Experience, 56(4), 934–950.
76. Morris, G., & White, C. (2026). Real-time scalable inferencing for clinical AI workloads. Future Internet, 18(1), 14.
77. Bandi, V. D. V. K. Autonomous Data Platforms: Converging AI, MLOps, and Cloud Engineering for Digital.
78. Kumar, A., & Reddy, S. (2026). Semantic healthcare knowledge graphs for predictive medicine. Applied Intelligence, 56(3), 2751–2768.
79. Green, S., Baker, L., & Collins, M. (2026). Cloud-native governance for medical AI systems. Health Informatics Journal, 32(1), 1–22.
80. Krishnan, M., Bandi, V. D. V. K., Mangala, N., Kolla, S. H., & Mangalampalli, B. M. Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry.
81. Brown, J., Smith, K., & Wilson, D. (2026). Explainability pipelines for clinical foundation models. Machine Learning with Applications, 20, 100611.
82. Ahmed, R., Hussain, M., & Khan, T. (2026). High-throughput data engineering for AI healthcare systems. Journal of Big Data, 13(1), 44.
83. Li, Z., Wang, H., & Xu, M. (2026). Multi-agent orchestration for hospital analytics workflows. Expert Systems with Applications, 273, 126311.
84. Garcia, J., Torres, P., & Silva, R. (2026). Privacy-aware federated analytics in EHR ecosystems. Computers & Security, 142, 103910.
85. Inala, R., Garapati, R. S., Aitha, A. R., Komaragiri, V. B., Gottimukkala, V. R. R., Recharla, M., ... & Varri, D. B. S. (2026). U.S. Patent Application No. 19/389,108.
86. Wilson, M., Reed, A., & Harris, P. (2026). AI-native healthcare data platforms. IEEE Computer, 59(5), 54–63.
87. Chen, X., Liu, Z., & Zhao, H. (2026). Clinical retrieval systems using hybrid semantic search. Knowledge and Information Systems, 68(2), 833–856.
88. Roberts, N., Hall, T., & Green, K. (2026). Scalable patient digital twins using multimodal AI. Nature Communications Medicine, 4(1), 102.
89. Davuluri, P. N. (2026). Autonomous Compliance Systems: AI, Event Streaming, and the Future of Financial Crime Prevention. Journal of Informatics Education and Research.
90. Thomas, P., Young, L., & Baker, M. (2026). Engineering resilient AI analytics for next-generation EHR systems. ACM Transactions on Computing for Healthcare, 7(1), 9.
