Advanced Predictive Algorithms for Outcome-Based Healthcare Financing and Payment Structures

Shwetha S

Authors

Shwetha S B.Sc Artificial Intelligence & Machine Learning, Department of AIML and Software Systems, Sri Krishna Arts and Science College, Coimbatore, Tamil Nadu, India Author

Keywords:

Predictive Algorithms, Outcome-Based Healthcare Financing, Payment Models, Healthcare Costs, Patient Outcomes, Data Quality, Healthcare Policy, Advanced Analytics

Abstract

Advanced predictive algorithms into outcome-based healthcare financing and payment structures offers a transformative approach to enhancing the efficiency and effectiveness of healthcare delivery. This paper explores the potential of predictive algorithms to improve patient outcomes and optimize healthcare costs by accurately forecasting risks and tailoring payment models to actual patient needs. Through a review of current literature and analysis of case studies, the study examines the key components of these algorithms, their implementation challenges, and their impact on healthcare systems. The findings suggest that while predictive algorithms hold significant promise, careful consideration must be given to data quality, algorithmic transparency, and regulatory frameworks to ensure their successful integration.

References

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—Addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.

Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98.

Eijkenaar, F. (2013). Pay for performance in health care: An international overview of initiatives. Medical Care Research and Review, 70(5), 494-520.

Bayyapu, S. (2021). Bridging the gap: Overcoming data, technological, and human roadblocks to AI-driven healthcare transformation. Journal of Management (JOM), 8(1), 7-14.

Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., ... & Topol, E. (2019). Deep learning-enabled medical computer vision. NPJ Digital Medicine, 2(1), 1-9.

Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G. Z. (2019). XAI—Explainable artificial intelligence. Science Robotics, 4(37), eaay7120.

James, J. (2012). Health policy brief: Pay-for-performance. Health Affairs.

Bayyapu, S., Turpu, R.R., Vangala, R.R. (2022). Algorithmic ethics and beyond: Upholding patient rights in the cloud frontier. International Journal of Information Technology (IJIT), 3(1), 9-16.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—Big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215.

Shah, N. H., Milstein, A., & Bagley, S. C. (2019). Making machine learning models clinically useful. Journal of the American Medical Association, 322(14), 1351-1352.

Bayyapu, S., Turpu, R.R., Vangala, R.R. (2021). Banking in the digital age: Navigating transformations in business models, customer journeys, and operational excellence. International Journal of Advanced Research in Management (IJARM), 12(1), 110-118.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Van Herck, P., De Smedt, D., Annemans, L., Remmen, R., Rosenthal, M. B., & Sermeus, W. (2013). Systematic review: Effects, design choices, and context of pay-for-performance in health care. BMC Health Services Research, 10(1), 247.

Bayyapu, S. (2022). Optimizing IT sourcing in healthcare: Balancing control, cost, and innovation. International Journal of Computer Applications, 3(1), 14-20.

Adams, R., Bhargava, V., & Bhalotra, R. (2020). Leveraging AI and machine learning for healthcare innovation. Health Information Science and Systems, 8(1), 15-24.

Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7), 2328-2331.

Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine—Beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507-2509.