Challenges and Innovations in Healthcare Fraud and Waste Detection Systems: A Systematic Review and Proposed Framework
Resumo
Fraud and abuse in the healthcare sector cause considerable financial losses to public and private entities.This article conducted a systematic literature review to identify the challenges in fraud and waste detection systems. Ten articles were identified in the review that covered the IEEE, PubMed, Scopus, and Web of Science bases. Based on these studies, we developed a framework in which we categorized the works by anomaly point, contextual, and collective. We also identified the unsupervised learning techniques used and identified the following challenges dimensionality reduction, conceptual deviations caused by changes in fraudsters’ behavior to bypass audit systems, and the need for real-time detection support.
Referências
Abdallah, A., Maarof, M. A., and Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68:90–113.
Bauder, R., da Rosa, R., and Khoshgoftaar, T. (2018). Identifying medicare provider fraud with unsupervised machine learning. In 2018 IEEE international conference on information Reuse and integration (IRI), pages 285–292. IEEE.
Bhaskar, A., Pande, S., Malik, R., Khamparia, A., et al. (2021). An intelligent unsupervised technique for fraud detection in health care systems. Intelligent Decision Technologies, 15(1):127–139.
Bolton, R. J., Hand, D. J., et al. (2001). Unsupervised profiling methods for fraud detection. Credit scoring and credit control VII, pages 235–255.
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58.
Chen, S. and Gangopadhyay, A. (2013). A novel approach to uncover health care frauds through spectral analysis. In 2013 IEEE International Conference on Healthcare Informatics, pages 499–504. IEEE.
Davidow, M. and Matteson, D. S. (2022). Factor analysis of mixed data for anomaly detection. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4):480–493.
Dik, A., Jebari, K., and Ettouhami, A. (2018). An improved robust fuzzy algorithm for unsupervised learning. Journal of Intelligent Systems, 29(1):1028–1042.
Dos Santos, H. D., Ulbrich, A. H. D., Woloszyn, V., and Vieira, R. (2018). Ddc-outlier: preventing medication errors using unsupervised learning. IEEE journal of biomedical and health informatics, 23(2):874–881.
Dresch, A., Lacerda, D. P., and Junior, J. A. V. A. (2020). Design science research: método de pesquisa para avanço da ciência e tecnologia. Bookman Editora.
Ekin, T., Ieva, F., Ruggeri, F., and Soyer, R. (2018). Statistical medical fraud assessment: exposition to an emerging field. International Statistical Review, 86(3):379–402.
Gautam, A. (2017). Tackling wasteful spending on health. OECD.
Houle, M. E. (2013). Dimensionality, discriminability, density and distance distributions. In 2013 IEEE 13th International Conference on Data Mining Workshops, pages 468–473. IEEE.
Kemp, J. (2023). Unsupervised learning for anomaly detection in Australian medical payment data. PhD thesis, UNSW Sydney.
Kemp, J., Barker, C., Good, N., and Bain, M. (2022). Sequential pattern detection for identifying courses of treatment and anomalous claim behaviour in medical insurance. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 3039–3046. IEEE.
Kumaraswamy, N., Markey, M. K., Ekin, T., Barner, J. C., and Rascati, K. (2022). Healthcare fraud data mining methods: A look back and look ahead. Perspectives in health information management, 19(1).
Massi, M. C., Ieva, F., and Lettieri, E. (2020). Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases. BMC medical informatics and decision making, 20:1–11.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., et al. (2010). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Int J Surg, 8(5):336–341.
Musal, R. M. (2010). Two models to investigate medicare fraud within unsupervised databases. Expert Systems with Applications, 37(12):8628–8633.
Nagata, K., Tsuji, T., Suetsugu, K., Muraoka, K., Watanabe, H., Kanaya, A., Egashira, N., and Ieiri, I. (2021). Detection of overdose and underdose prescriptions—an unsupervised machine learning approach. PloS one, 16(11):e0260315.
Putina, A., Sozio, M., Rossi, D., and Navarro, J. M. (2020). Random histogram forest for unsupervised anomaly detection. In 2020 IEEE International Conference on Data Mining (ICDM), pages 1226–1231. IEEE.
Settipalli, L. and Gangadharan, G. (2021). Healthcare fraud detection using primitive sub peer group analysis. Concurrency and Computation: Practice and Experience, 33(23):e6275.
Shin, H., Park, H., Lee, J., and Jhee, W. C. (2012). A scoring model to detect abusive billing patterns in health insurance claims. Expert Systems with Applications, 39(8):7441–7450.
Tennyson, S. and Salsas-Forn, P. (2002). Claims auditing in automobile insurance: fraud detection and deterrence objectives. Journal of Risk and Insurance, 69(3):289–308.
Bauder, R., da Rosa, R., and Khoshgoftaar, T. (2018). Identifying medicare provider fraud with unsupervised machine learning. In 2018 IEEE international conference on information Reuse and integration (IRI), pages 285–292. IEEE.
Bhaskar, A., Pande, S., Malik, R., Khamparia, A., et al. (2021). An intelligent unsupervised technique for fraud detection in health care systems. Intelligent Decision Technologies, 15(1):127–139.
Bolton, R. J., Hand, D. J., et al. (2001). Unsupervised profiling methods for fraud detection. Credit scoring and credit control VII, pages 235–255.
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):1–58.
Chen, S. and Gangopadhyay, A. (2013). A novel approach to uncover health care frauds through spectral analysis. In 2013 IEEE International Conference on Healthcare Informatics, pages 499–504. IEEE.
Davidow, M. and Matteson, D. S. (2022). Factor analysis of mixed data for anomaly detection. Statistical Analysis and Data Mining: The ASA Data Science Journal, 15(4):480–493.
Dik, A., Jebari, K., and Ettouhami, A. (2018). An improved robust fuzzy algorithm for unsupervised learning. Journal of Intelligent Systems, 29(1):1028–1042.
Dos Santos, H. D., Ulbrich, A. H. D., Woloszyn, V., and Vieira, R. (2018). Ddc-outlier: preventing medication errors using unsupervised learning. IEEE journal of biomedical and health informatics, 23(2):874–881.
Dresch, A., Lacerda, D. P., and Junior, J. A. V. A. (2020). Design science research: método de pesquisa para avanço da ciência e tecnologia. Bookman Editora.
Ekin, T., Ieva, F., Ruggeri, F., and Soyer, R. (2018). Statistical medical fraud assessment: exposition to an emerging field. International Statistical Review, 86(3):379–402.
Gautam, A. (2017). Tackling wasteful spending on health. OECD.
Houle, M. E. (2013). Dimensionality, discriminability, density and distance distributions. In 2013 IEEE 13th International Conference on Data Mining Workshops, pages 468–473. IEEE.
Kemp, J. (2023). Unsupervised learning for anomaly detection in Australian medical payment data. PhD thesis, UNSW Sydney.
Kemp, J., Barker, C., Good, N., and Bain, M. (2022). Sequential pattern detection for identifying courses of treatment and anomalous claim behaviour in medical insurance. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 3039–3046. IEEE.
Kumaraswamy, N., Markey, M. K., Ekin, T., Barner, J. C., and Rascati, K. (2022). Healthcare fraud data mining methods: A look back and look ahead. Perspectives in health information management, 19(1).
Massi, M. C., Ieva, F., and Lettieri, E. (2020). Data mining application to healthcare fraud detection: a two-step unsupervised clustering method for outlier detection with administrative databases. BMC medical informatics and decision making, 20:1–11.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., et al. (2010). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Int J Surg, 8(5):336–341.
Musal, R. M. (2010). Two models to investigate medicare fraud within unsupervised databases. Expert Systems with Applications, 37(12):8628–8633.
Nagata, K., Tsuji, T., Suetsugu, K., Muraoka, K., Watanabe, H., Kanaya, A., Egashira, N., and Ieiri, I. (2021). Detection of overdose and underdose prescriptions—an unsupervised machine learning approach. PloS one, 16(11):e0260315.
Putina, A., Sozio, M., Rossi, D., and Navarro, J. M. (2020). Random histogram forest for unsupervised anomaly detection. In 2020 IEEE International Conference on Data Mining (ICDM), pages 1226–1231. IEEE.
Settipalli, L. and Gangadharan, G. (2021). Healthcare fraud detection using primitive sub peer group analysis. Concurrency and Computation: Practice and Experience, 33(23):e6275.
Shin, H., Park, H., Lee, J., and Jhee, W. C. (2012). A scoring model to detect abusive billing patterns in health insurance claims. Expert Systems with Applications, 39(8):7441–7450.
Tennyson, S. and Salsas-Forn, P. (2002). Claims auditing in automobile insurance: fraud detection and deterrence objectives. Journal of Risk and Insurance, 69(3):289–308.
Publicado
09/06/2025
Como Citar
SCHNEIDER, Gelson André; KORZENOWSKI, André Luis.
Challenges and Innovations in Healthcare Fraud and Waste Detection Systems: A Systematic Review and Proposed Framework. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 284-292.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7061.
