Renewable-Powered Edge Intelligence for Smart Health Reimbursement

Authors

  • Jens Lehmann Author

Keywords:

Edge-Assisted Healthcare Intelligence, Renewable Energy in Healthcare, Health Transaction Processing, Claims Data Fusion, Real-Time Authorization, Reimbursement Workflows, Data Anomaly Detection, Explainable AI, Healthcare Data Security, Automated Claims Auditing.

Abstract

Efforts to incorporate renewable energy into healthcare delivery address three interconnected goals: financial sustainability, reduction of the carbon footprint across operations and supply chains, and the maintenance of uninterrupted care. Yet unstable power supply continues to undermine the quality of health services and poses a particular risk for the growing population of Remote Native Patients, who depend on remote supervision and assistance. Compounding this problem, the absence of edge-assisted intelligence in health transaction processes limits the degree to which renewable energy adoption can meaningfully inform reimbursement workflows.

This gap can be addressed by fusing data from claims systems, clinical repositories, policy servers, and energy meters to enable real-time verification of service eligibility and authorization. With clear, modular logic embedded in reimbursement design and execution, such integration supports latency-sensitive decisions across several functions: pre-authorization of care, routing of claims for payment, scheduling of renewable-powered services, detection of data anomalies, and generation of explainable outputs. By strengthening transaction speed, security, and trust, this functionality reduces the delays and risks associated with manual intervention, enhancing the overall value of edge-assisted solutions in health transaction environments. The resulting improvements in cost and throughput, in turn, support broader adoption of edge-assisted health transaction systems across diverse use cases—while expanding the data available for future algorithm training and policy evaluation.

For edge-assisted intelligence to make a genuinely beneficial contribution to health financing policy, three areas warrant particular attention: (i) aligning eligibility rules with reimbursement rationale; (ii) designing validation and auditing tests for automated controls; and (iii) establishing criteria to detect operation outside expected parameters. Renewable-powered care delivery offers a particularly relevant application domain for exploring these questions.

References

1. Akter, M., Moustafa, N., Lynar, T., & Razzak, I. (2022). Edge intelligence: Federated learning-based privacy protection framework for smart healthcare systems. IEEE Journal of Biomedical and Health Informatics, 26(12), 5805–5816.

2. Amponsah, A. A., Adekoya, A. F., & Weyori, B. A. (2022). A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology. Decision Analytics Journal, 4, 100122.

3. Mondeddula, S. R. (2022). Machine Learning-Based Credit Risk Assessment For Intelligent Lending and Default Prediction in Financial Services. Journal of International Crisis and Risk Communication Research , 5(1), 113–121. https://doi.org/10.63278/jicrcr.v5i1.3820

4. Amin, S. U., & Hossain, M. S. (2021). Edge intelligence and Internet of Things in healthcare: A survey. IEEE Access, 9, 45–59.

5. Bhatia, M. (2022). Energy efficient IoT-based informative analysis for edge computing environment. Transactions on Emerging Telecommunications Technologies, 33(9), e4527.

6. Rohit Gorle. (2023). Machine Learning-Based Access Risk Detection in Cloud Identity and Access Management Systems. Journal of International Crisis and Risk Communication Research , 241–252. Retrieved from https://jicrcr.com/index.php/jicrcr/article/view/3833

7. Fu, Y., Yang, X., Yang, P., Wong, A. K. Y., & Shi, Z. (2021). Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical Internet-of-Things systems. EURASIP Journal on Wireless Communications and Networking, 2021, Article 26.

8. Ge, P., Zhao, J., Zhang, H., Zou, D., & Wang, M. (2023). Green hybrid energy harvesting for intelligent mobile edge computing in Internet of Things. Physical Communication, 61, 102171.

9. Mashetty, S. (2023). A Comparative Analysis of Patented Technologies Supporting Mortgage and Housing Finance. Educational Administration: Theory and Practice.

10. Idrees, A. K., Alhussein, D. A., & Harb, H. (2023). Energy-efficient multisensor adaptive sampling and aggregation for patient monitoring in edge computing based IoHT networks. Intelligent Decision Technologies, 15(3).

11. Kumaraswamy, N., Markey, M. K., Barner, J. C., & Rascati, K. (2022). Feature engineering to detect fraud using healthcare claims data. Expert Systems with Applications, 210, 118433.

12. Li, S., Zhang, N., Jiang, R., Zhou, Z., Zheng, F., et al. (2022). Joint task offloading and resource allocation in mobile edge computing with energy harvesting. Journal of Cloud Computing, 11, Article 17.

13. Gorle, R. (2023). Machine Learning-Based Access Risk Detection in Cloud Identity and Access Management Systems. Journal of International Crisis and Risk Communication Research, 241-252.

14. Liu, Z., Jiang, K., Wu, X., & Zeng, X. (2022). Offloading cost optimization in multiserver mobile edge computing systems with energy harvesting devices. Wireless Communications and Mobile Computing, 2022, Article 2279362.

15. Nabrawi, E., & Alanazi, A. (2023). Fraud detection in healthcare insurance claims using machine learning. Risks, 11(9), 160.

16. Rana, A., Chakraborty, C., Sharma, S., Dhawan, S., Pani, S. K., & Ashraf, I. (2022). Internet of Medical Things-based secure and energy-efficient framework for health care. Big Data, 10(1), 18–33.

17. Renold, A. P., & Kumar, K. V. R. (2022). Design of Internet of Things enabled personalized healthcare device for vital signs monitoring. Intelligent Decision Technologies, 14(5).

18. 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.

19. Gadi, A. L. (2023). The Role of AI-Driven Predictive Analytics in Automotive R&D: Enhancing Vehicle Performance and Safety. Nanotechnology Perceptions.

20. Sharif, Z., Jung, L. T., Ayaz, M., Yahya, M., & Pitafi, S. (2023). Priority-based task scheduling and resource allocation in edge computing for health monitoring system. Journal of King Saud University Computer and Information Sciences, 35, 544–559.

21. Singh, A., & Chatterjee, K. (2023). Edge computing based secure health monitoring framework for electronic healthcare system. Cluster Computing, 26(2), 1205–1220.

22. Suesserman, M., Gorny, S., Lasaga, D., Helms, J., Olson, D., Bowen, E., & Bhattacharya, S. (2023). Procedure code overutilization detection from healthcare claims using unsupervised deep learning methods. BMC Medical Informatics and Decision Making, 23, Article 196.

23. Potturi, P. (2023). Cloud-Driven Business Intelligence Modernization for Scalable Enterprise Data Integration and Decision Support. International Journal of Intelligent Systems and Applications in Engineering, 11, 387.

24. Wang, R., Lai, J., Zhang, Z., Li, X., Vijayakumar, P., & Karuppiah, M. (2023). Privacy-preserving federated learning for Internet of Medical Things under edge computing. IEEE Journal of Biomedical and Health Informatics, 27(2), 854–865.

25. Zheng, J., et al. (2021). Mobile edge computing enabled efficient communication based on federated learning in Internet of Medical Things. Wireless Communications and Mobile Computing, 2021, Article 4410894.

26. Sasidhar Reddy Mondeddula. (2022). AI-Driven Fraud Detection Using Ensemble Machine Learning for Secure Digital Banking Transactions. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 597–605. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8515

27. Zhu, S., Ota, K., & Dong, M. (2022). Green AI for IIoT: Energy efficient intelligent edge computing for Industrial Internet of Things. IEEE Transactions on Green Communications and Networking, 6(1), 79–88.

28. Zhou, Z.-H., Shojafar, M., Abawajy, J., Yin, H. J., & Lu, H. Y. (2022). ECMS: An edge intelligent energy efficient model in mobile edge computing. IEEE Transactions on Green Communications and Networking, 6(1), 238–247.

29. Reddy, V. A. R. (2023). API-First Design As A Strategy For Healthcare System Interoperability. South Eastern European Journal of Public Health, 224–247. https://doi.org/10.70135/seejph.vi.7128

30. Addanke, S., Anandan, R., & Krishna, P. V. (2022). IoT-enabled smart healthcare infrastructure maximises energy efficiency. International Journal on Recent and Innovation Trends in Computing and Communication, 10(8), 88–95.

31. Aarati, K., Shirisha, A., & Bindhu, K. (2023). Identifying health insurance claim frauds using machine learning concept. Journal of Science & Technology, 8(7), 45–57.

32. Penaka, B. (2023). AI-Driven Predictive Analytics for Early Detection of High-Risk Patients in Cloud-Based Healthcare Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 118-124.

Additional Files

Published

2024-12-12

How to Cite

Renewable-Powered Edge Intelligence for Smart Health Reimbursement. (2024). The American Online Journal of Science and Engineering (AOJSE), 2(04). https://aojse.org/index.php/aojse/article/view/83

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