Authors : Sarah Paul; Adebayo O. Sosanya; Edward Akuffo Addo Dankwa

Volume/Issue : Volume 11 - 2026, Issue 10 - October

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Google Scholar : https://tinyurl.com/3ctajnum

DOI : https://doi.org/10.38124/ijisrt/26Oct219

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Few U.S. patients account for most healthcare spending. This explains why they are easily identified by machine learning (ML). This review synthesizes empirical evidence on the performance of cost-prediction models, their application, and overall implications in the U.S.

Keywords : Machine Learning, Cost-Prediction Models, High-Burden Patients, U.S. Insurance Markets.

References :

1. Bruyneel, A., Larcin, L., Martins, D., van den Bulcke, J., Leclercq, P., & Pirson, M. (2023). Cost comparisons and factors related to cost per stay in intensive care units in Belgium. BMC Health Services Research, 23, 986.
2. Chang, C. F., Mirvis, D. M., & Mahmood, A. (2026). The high cost of American health care: Understanding the deeper roots of the crisis. Health Care Analysis, 34(1), 1–12.
3. Coots, M., Linn, K. A., Goel, S., Navathe, A. S., & Parikh, R. B. (2025). Racial bias in clinical and population health algorithms: A critical review of current debates. Annual Review of Public Health, 46, 507–523.
4. Crowley, R., Daniel, H., Cooney, T. G., Engel, L. S., & Health and Public Policy Committee of the American College of Physicians. (2020). Envisioning a better US health care system for all: Coverage and cost of care. Annals of Internal Medicine, 172(2, Suppl.), S7–S32.
5. Delanerolle, G., Bouchareb, Y., Katsikopoulos, K. V., & Phiri, P. (2026). Data science in healthcare: A complete guide. CRC Press.
6. Dickman, S. L., Gaffney, A., McGregor, A., Himmelstein, D. U., McCormick, D., Bor, D. H., & Woolhandler, S. (2022). Trends in health care use among Black and White persons in the US, 1963–2019. JAMA Network Open, 5(6), e2217383.
7. Dombrowski, M., Prenner, A., & Kainz, B. (2024). Bias assessment and data drift detection in medical image analysis: A survey (arXiv:2409.17800). arXiv. https://arxiv.org/abs/2409.17800
8. Hajat, C., Siegal, Y., & Adler-Waxman, A. (2021). Clustering and healthcare costs with multiple chronic conditions in a US study. Frontiers in Public Health, 8, 607528.
9. Hutchins, E. J. (2025). Evaluating the additive value of machine learning in clinical risk prediction: A comparative study across three patient cohorts [Doctoral dissertation, University of California, Los Angeles].
10. Jones, C. H., & Dolsten, M. (2024). Healthcare on the brink: Navigating the challenges of an aging society in the United States. npj Aging, 10(1), 22.
11. Langenberger, B., Schulte, T., & Groene, O. (2023). The application of machine learning to predict high-cost patients: A performance comparison of different models using healthcare claims data. PLOS ONE, 18(1), e0279540.
12. Maisog, J. M., Li, W., Xu, Y., Hurley, B., Shah, H., Lemberg, R., ... & Gutfraind, A. (2019). Using massive health insurance claims data to predict very high-cost claimants: a machine learning approach. arXiv preprint arXiv:1912.13032.
13. Mo, C. (2026). Optimizing care management targeting: A machine learning comparison of regression, classification, and ranking approaches for identifying future high-cost patients.
14. Morid, M. A., & Sheng, O. R. L. (2025). Healthcare cost prediction for heterogeneous patient profiles using deep learning models with administrative claims data. Information Systems Research, 36(4), 1968–1992.
15. Mwakinyali, F., Kateule, R., & Tunga, M. (2024). Forecasting pediatric medical expenses using machine learning: A case study of the Toto Afya Card in Tanzania. Journal of ICT Systems, 2(2), 55–66.
16. Nakano, F. K., Van Aerde, N., Coppens, G., Vanhorebeek, I., Vens, C., Van den Berghe, G., & Güiza Grandas, F. (2025). Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness. Intensive Care Medicine Experimental, 13, 98.
17. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
18. Olatunji, A. O. (2025). Machine learning in business process optimization: A framework for efficiency and decision-making. Journal of Basic and Applied Research International, 31(2), 18–28.
19. Osawa, I., Goto, T., Yamamoto, Y., & Tsugawa, Y. (2020). Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data. npj Digital Medicine, 3, 148.
20. Pietrobon, R., Machiavelli, A., Paulsen Rodrigues, L., Agrey, A., Nkeangnyi, L., Zechia, G., ... Teixeira, L. (2025). The bias assessment method: Evaluating racial inequities in clinical risk prediction models (motivated by Obermeyer et al.'s analysis of cost-driven labeling in healthcare algorithms). SSRN. https://ssrn.com/abstract=5861522
21. Rahman, Y., & Dua, P. (2025). A machine learning framework for predicting healthcare utilization and risk factors. Healthcare Analytics, 8, 100411.
22. Rivera-Hernandez, M., Kumar, A., Chou, L. N., Keeney, T., Ferdows, N., Karmarkar, A., & Ottenbacher, K. (2022). Healthcare utilization and costs among high-need and frail Mexican American Medicare beneficiaries. PLOS ONE, 17(1), e0262079.
23. Shockney, L. D. (2021). Chronic disease and complex care navigators: A scoping review.
24. Zhang, P., Zhu, B., Chen, X., & Wang, L. (2025). Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods. Scientific Reports, 15, 16006.
25. Zink, A., Obermeyer, Z., & Pierson, E. (2024). Race adjustments in clinical algorithms can help correct for racial disparities in data quality. Proceedings of the National Academy of Sciences, 121(34), e2402267121.