Journal of Primeasia
Integrative Disciplinary Research | Online ISSN 3064-9870 | Print ISSN 3069-4353
0
Citations
121.9k
Views
114
Articles
Volume 5 Number 1 2024
RESEARCH ARTICLE (Open Access)
Real-Time Predictive Detection of Healthcare Fraud, Waste, and Abuse Using Heterogeneous Machine Learning Models
Md Nazmuddin Moin Khan1*
Journal of Primeasia 5 (1) 1-8 https://doi.org/10.25163/primeasia.5110483
Submitted: 02 October 2024 Revised: 25 November 2024 Accepted: 01 December 2024 Published: 03 December 2024
Abstract
Background: Healthcare waste and abuse are persistent challenges that significantly increase costs and reduce efficiency in the U.S. system. The study focuses on locating major system inefficiencies and fraud points to build a real-time detection system which combines different machine learning methods with human survey data.
Methods: The study used a mixed-method approach which combined survey responses from 275 healthcare professionals with structured claim information. We were trained four machine learning models which included Random Forest and XGBoost and Support Vector Machine and Artificial Neural Network using normalized and preprocessed datasets. The model evaluation process used four different metrics which included accuracy and precision and recall and F1-score. The analysis used binary logistic regression to determine which variables predict abnormal activity.
Results: The survey results show that redundant testing at 28% and administrative delays at 21% and inflated billing at 30% stand as the main causes of waste and abuse. Machine learning models reached outstanding predictive results through XGBoost which achieved 94.6% accuracy and 0.92 F1-score and Random Forest which achieved 93.2% accuracy. The analysis showed two main predictors in Claim Frequency Ratio (β = 0.61, p = 0.046) and Average Billing Deviation (β = 0.54, p = 0.037) and Provider Experience showed a weak negative relationship.
Conclusion: The study shows that combining survey data with different machine learning models creates an effective system for detecting healthcare waste and abuse in real-time. The method helps organizations operate in a moral way while making their financial transactions more efficient.
Keywords: Fraud detection, Healthcare waste, Predictive analytics, Real-time monitoring, Machine learning
References
Alkhazendar, I., Zubair, M., & Qidwai, U. (2022). Smart hardware Trojan Detection System. In Lecture notes in networks and systems (pp. 791–806). https://doi.org/10.1007/978-3-031-16075-2_58
Andrade, R. O., Yoo, S. G., Tello-Oquendo, L., Flores, M., & Ortiz, I. (2022). Integration of AI and IoT approaches for evaluating cybersecurity risk on Smart City. In Internet of things (pp. 305–333). https://doi.org/10.1007/978-3-030-87059-1_12
Arora, D., Gupta, S., & Anpalagan, A. (2022). Evolution and adoption of next generation IoT-Driven health care 4.0 systems. Wireless Personal Communications, 127(4), 3533–3613. https://doi.org/10.1007/s11277-022-09932-3
Babar, M., Arif, F., & Irfan, M. (2019). Internet of Things–Based Smart City Environments Using Big Data Analytics: A survey. In EAI/Springer Innovations in Communication and Computing (pp. 129–138). https://doi.org/10.1007/978-3-319-99966-1_12
Bandyopadhyay, D., & Sen, J. (2011). Internet of Things: Applications and Challenges in Technology and standardization. Wireless Personal Communications, 58(1), 49–69. https://doi.org/10.1007/s11277-011-0288-5
Batko, K., & Slezak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data, 9(1), 3. https://doi.org/10.1186/s40537-021-00553-4
Bayerstadler, A., Van Dijk, L., & Winter, F. (2016). Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance. Insurance Mathematics and Economics, 71, 244–252. https://doi.org/10.1016/j.insmatheco.2016.09.013
Cho, H. N., Ahn, I., Gwon, H., Kang, H. J., Kim, Y., Seo, H., Choi, H., Kim, M., Han, J., Kee, G., Jun, T. J., & Kim, Y. (2022). Heterogeneous graph construction and HinSAGE learning from electronic medical records. Scientific Reports, 12(1), 21152. https://doi.org/10.1038/s41598-022-25693-2
Deepa, N., & Prabadevi, B. (2020). Advanced Machine Learning for enterprise IoT modeling. In EAI/Springer Innovations in Communication and Computing (pp. 99–121). https://doi.org/10.1007/978-3-030-44407-5_5
Ekin, T., Ieva, F., Ruggeri, F., & Soyer, R. (2018). Statistical Medical Fraud Assessment: exposition to an Emerging field. International Statistical Review, 86(3), 379–402. https://doi.org/10.1111/insr.12269
Gangavarapu, T., Jaidhar, C. D., & Chanduka, B. (2020). Applicability of machine learning in spam and phishing email filtering: review and approaches. Artificial Intelligence Review, 53(7), 5019–5081. https://doi.org/10.1007/s10462-020-09814-9
Gourisaria, M. K., Agrawal, R., Singh, V., Rautaray, S. S., & Pandey, M. (2022). AI and IoT Enabled Smart Hospital Management Systems. In Studies in big data (pp. 77–106). https://doi.org/10.1007/978-981-19-5154-1_6
Gupta, A., & Gupta, S. K. (2022). Flying through the secure fog: A complete study on UAV-Fog in heterogeneous networks. International Journal of Communication Systems, 35(13). https://doi.org/10.1002/dac.5237
Höchtl, J., Parycek, P., & Schöllhammer, R. (2015). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169. https://doi.org/10.1080/10919392.2015.1125187
Jahan, T. (2021). Machine Learning with IoT and Big Data in Healthcare. In EAI/Springer Innovations in Communication and Computing (pp. 81–98). https://doi.org/10.1007/978-3-030-67051-1_5
Kumar, Y., Sood, K., Kaul, S., & Vasuja, R. (2019). Big data analytics and its benefits in healthcare. In Studies in big data (pp. 3–21). https://doi.org/10.1007/978-3-030-31672-3_1
Mathew, S. S., Hayawi, K., Dawit, N. A., Taleb, I., & Trabelsi, Z. (2022). Integration of blockchain and collaborative intrusion detection for secure data transactions in industrial IoT: a survey. Cluster Computing, 25(6), 4129–4149. https://doi.org/10.1007/s10586-022-03645-9
Mehta, S., Bhushan, B., & Kumar, R. (2022). Machine Learning Approaches for Smart City Applications: Emergence, challenges and opportunities. In Intelligent systems reference library (pp. 147–163). https://doi.org/10.1007/978-3-030-90119-6_12
Mondal, K. K., & Roy, D. G. (2021). IoT Data Security with Machine Learning Blckchain: Risks and Countermeasures. In Signals and communication technology (pp. 49–81). https://doi.org/10.1007/978-981-16-6186-0_3
Monteiro, A. C. B., França, R. P., Arthur, R., & Iano, Y. (2021). An Overview of Artificial Intelligence Technology Directed at Smart Sensors and Devices from a Modern Perspective. In Studies in big data (pp. 3–26). https://doi.org/10.1007/978-3-030-77214-7_1
Rao, N. T., Bhattacharyya, D., & Joshua, E. S. N. (2022). An extensive discussion on utilization of data security and big data models for resolving healthcare problems. In Elsevier eBooks (pp. 311–324). https://doi.org/10.1016/b978-0-323-90032-4.00001-8
Rehman, A., Naz, S., & Razzak, I. (2021). Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities. Multimedia Systems, 28(4), 1339–1371. https://doi.org/10.1007/s00530-020-00736-8
Saha, A., Chowdhury, C., Jana, M., & Biswas, S. (2020). IoT sensor data analysis and fusion applying machine learning and Meta-Heuristic approaches. In Studies in computational intelligence (pp. 441–469). https://doi.org/10.1007/978-3-030-52067-0_20
Sakly, H., Said, M., Seekins, J., & Tagina, M. (2022). Big data and artificial intelligence for E-Health. In Integrated science (pp. 525–544). https://doi.org/10.1007/978-3-030-96814-4_23
Saravanan, K., Julie, E. G., & Robinson, Y. H. (2018). Smart Cities & IoT: Evolution of Applications, Architectures & Technologies, Present Scenarios & Future Dream. In Intelligent systems reference library (pp. 135–151). https://doi.org/10.1007/978-3-030-04203-5_7
Sharma, D. K., Bhargava, S., & Singhal, K. (2020). Internet of Things applications in the pharmaceutical industry. In Elsevier eBooks (pp. 153–190). https://doi.org/10.1016/b978-0-12-821326-1.00006-1
Singh, N., Lai, K., Vejvar, M., & Cheng, T. C. E. (2019). Data-driven auditing: A predictive modeling approach to fraud detection and classification. Journal of Corporate Accounting & Finance, 30(3), 64–82. https://doi.org/10.1002/jcaf.22389
Tantalaki, N., Souravlas, S., & Roumeliotis, M. (2019). A review on big data real-time stream processing and its scheduling techniques. International Journal of Parallel Emergent and Distributed Systems, 35(5), 571–601. https://doi.org/10.1080/17445760.2019.1585848
Tomar, A., Malik, H., Kumr, P., & Iqbal, A. (2022). Editorial: Machine Learning, Advances in Computing, Renewable Energy and Communication (MARC). Lecture Notes in Electrical Engineering, 1–19. https://doi.org/10.1007/978-981-19-2828-4_1
Vidhyalakshmi, A., & Priya, C. (2020). Medical big data mining and processing in e-health care. In Elsevier eBooks (pp. 1–30). https://doi.org/10.1016/b978-0-12-821326-1.00001-2
Voda, A. I., & Radu, L. (2019). How can artificial intelligence respond to smart cities challenges? In Elsevier eBooks (pp. 199–216). https://doi.org/10.1016/b978-0-12-816639-0.00012-0
Wang, R., Luo, M., Wen, Y., Wang, L., Choo, K. R., & He, D. (2021). The applications of blockchain in artificial intelligence. Security and Communication Networks, 2021, 1–16. https://doi.org/10.1155/2021/6126247
Zafari, B., Ekin, T., & Ruggeri, F. (2021). Multicriteria decision frontiers for prescription anomaly detection over time. Journal of Applied Statistics, 49(14), 3638–3658. https://doi.org/10.1080/02664763.2021.1959528