Mitigating Financial Waste in the U.S. Healthcare System: An AI-Driven Framework for Real-Time Fraud Detection in Medicare and Medicaid Claims

Abstract

The financial waste associated with fraud, abuse, and inefficient claims processing remains a significant challenge to the sustainability of the United States healthcare system, specifically in Medicare and Medicaid programs. The traditional fraud detection systems are mostly reactive, and they are unable to cope up with the increasing volume and complexity of the healthcare claims data. The recent developments in the field of artificial intelligence (AI) present the potential of improving the process of detecting fraud by applying real-time surveillance and predictive analytics.

This paper will discuss the perception of stakeholders on the problem of financial waste in the U.S. healthcare system and evaluate how AI-based, real-time fraud detection systems are perceived to address the issue of fraud in Medicare and Medicaid claims.

The research design used was cross-sectional, quantitative and it was based on a structured survey instrument. The sample included 250 healthcare stakeholders who included administrators, billing and coding specialists, health IT professionals, policymakers and researchers. The survey involved the assessment of the awareness of financial waste, AI adoption, perceived efficiency of AI-driven fraud detection models, the challenges related to it, and ethics. Reliability analysis and descriptive statistics were used to analyze the data.

The statistics indicate that the awareness rate of the respondents regarding financial waste in healthcare is high. There was also high agreement among the respondents about the use of AI and its success in reducing financial waste particularly on real-time fraud detection. Reliability analysis indicated a high level of internal consistency among measurement constructs. Despite the predominantly positive attitudes towards AI effectiveness, the respondents mentioned the challenges related to ethics, regulation, and implementation of AI in respect of data privacy, transparency, and system integration.

The paper concludes that AI-informed real-time fraud detection systems are seen by many people as a powerful tool to decrease the financial waste on Medicare and Medicaid claims. Nevertheless, a powerful ethical governance, sufficient infrastructure, and readiness of organizations are necessary to implement it successfully to provide sustainable and responsible adoption.

REFERENCES

[1] P. K. Pemmasani, M. Osaka, and D. Henry, “AI-powered fraud detection in healthcare systems: A data-driven approach,” The Computertech, pp. 18–23, 2021.

[2] G. Frimpong, A. C. Peter-Anyebe, and O. M. Ijiga, “Artificial intelligence driven compliance automation improving audit readiness and fraud detection within healthcare revenue cycle management systems,” Global Journal of Engineering, Science & Social Science Studies, vol. 9, no. 9, 2023.

[3] M. Restrepo and L. Córdoba, “The role of artificial intelligence in transforming financial management and cost optimization strategies in healthcare organizations,” Journal of Computational Intelligence for Hybrid Cloud and Edge Computing Networks, vol. 7, no. 10, pp. 1–13, 2023.

[4] K. Kapadiya et al., “Blockchain and AI-empowered healthcare insurance fraud detection: An analysis, architecture, and future prospects,” IEEE Access, vol. 10, pp. 79606–79627, 2022.

[5] M. T. Hasan, “Graph neural network models for detecting fraudulent insurance claims in healthcare systems,” American Journal of Advanced Technology and Engineering Solutions, vol. 2, no. 1, pp. 88–109, 2022.

[6] Q. U. A. Nazir, “AI-powered radiology: Innovations and challenges in medical imaging,” Journal of Engineering and Computational Intelligence Review, vol. 1, no. 1, pp. 7–13, 2023.

[7] M. Shakeel, K. Khan, and A. Latif, “Role of financial inclusion, institutional quality and human capital in economic growth: An empirical analysis from selected Asian countries,” Journal of Policy Research, vol. 9, no. 2, pp. 274–278, 2023.

[8] A. Latif, S. Haider, and S. Farid, “Family social support and community engagement as a buffer against stress among youth,” Journal of Policy Research, vol. 8, no. 4, pp. 498–502, 2022.

[9] M. Shahinuzzaman, T. A. Shiva, M. S. Sumon, and K. Saifuddin, “Mental health of women breast cancer survivor at different stages of the disease,” Jagannath University Journal of Earth and Life Sciences, vol. 5, no. 1, pp. 1–12, 2019.

[10] K. Khan and S. Alvi, “A comprehensive review of blockchain applications across diverse domains,” Journal of Engineering and Computational Intelligence Review, vol. 1, no. 1, pp. 23–38, 2023.

[11] A. Abernethy et al., “The promise of digital health: Then, now, and the future,” NAM Perspectives, 2022, Art. no. 10-31478.

[12] A. K. Wagner, P. A. Ubel, and J. F. Wharam, “Financial pollution in the US health care system,” JAMA Health Forum, vol. 2, no. 3, Art. no. e210195, Mar. 2021.

[13] N. R. Sahni, P. Gupta, M. Peterson, and D. M. Cutler, “Active steps to reduce administrative spending associated with financial transactions in US health care,” Health Affairs Scholar, vol. 1, no. 5, Art. no. qxad053, 2023.

[14] M. A. Sayem et al., “AI-driven diagnostic tools: A survey of adoption and outcomes in global healthcare practices,” International Journal of Recent Innovations in Trends in Computing and Communication, vol. 11, no. 10, pp. 1109–1122, 2023.

[15] J. Geyman, “Investor-owned health care: The hidden blight on America’s ‘system’,” International Journal of Health Services, vol. 51, no. 4, pp. 494–500, 2021.

[16] L. N. Srinivasagopalan, “AI-enhanced fraud detection in healthcare insurance: A novel approach to combatting financial losses through advanced machine learning models,” European Journal of Advances in Engineering and Technology, vol. 9, no. 8, pp. 82–91, 2022.

[17] S. Bayyapu, R. R. Turpu, and R. R. Vangala, “Advancing healthcare decision-making: The fusion of machine learning, predictive analytics, and cloud technology,” International Journal of Computer Engineering and Technology, vol. 10, no. 5, pp. 157–170, 2019.

[18] S. Rani and A. Mittal, “Securing digital payments: A comprehensive analysis of AI-driven fraud detection with real-time transaction monitoring and anomaly detection,” in Proc. 6th Int. Conf. Contemporary Computing and Informatics (IC3I), Sep. 2023, pp. 2345–2349.

[19] V. Dunka, “AI-driven claims fraud detection using hybrid deep learning models: Integrating convolutional neural networks and recurrent neural networks for real-time fraud detection in insurance claims,” Essex Journal of AI Ethics and Responsible Innovation, vol. 3, pp. 276–311, 2023.

[20] H. Rehan, “Leveraging AI and cloud computing for real-time fraud detection in financial systems,” Journal of Science & Technology, vol. 2, no. 5, p. 127, 2021.

[21] M. Z. Islam, S. K. Shil, and M. R. Buiya, “AI-driven fraud detection in the US financial sector: Enhancing security and trust,” International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, vol. 14, no. 1, pp. 775–797, 2023.

[22] Aurangzeb, M. Asif, and M. K. Amin, “Resources Management and SME’s Performance,” Humanities & Social Sciences Reviews, vol. 9, no. 3, pp. 679–689, 2021. doi: 10.18510/hssr.2021.9367.

[23] Aurangzeb, T. Mushtaque, M. N. Tunio, Zia-ur-Rehman, and M. Asif, “Influence of administrative expertise of human resource practitioners on the job performance: Mediating role of achievement motivation,” International Journal of Management, vol. 12, no. 4, pp. 408–421, 2021. doi: 10.34218/IJM.12.4.2021.035.

[24] M. Asif and M. S. Sandhu, “Social media marketing revolution in Pakistan: A study of its adoption and impact on business performance,” Journal of Business Insight and Innovation, vol. 2, no. 2, pp. 67–77, 2023.