Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis
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Keywords

Medicare
broker commissions
enrollment trends
consumer costs
Medicare Advantage
Medicare Part D
healthcare policy

How to Cite

[1]
J. Reddy Machireddy, “Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis”, Journal of AI in Healthcare and Medicine, vol. 2, no. 1, pp. 501–518, Feb. 2022, Accessed: Nov. 23, 2024. [Online]. Available: https://healthsciencepub.com/index.php/jaihm/article/view/113

Abstract

The evolving landscape of the U.S. healthcare system necessitates a comprehensive understanding of the various factors influencing Medicare enrollment trends and consumer costs, particularly in relation to broker commissions. This paper conducts a detailed data-driven analysis of the impact that Medicare broker commissions have on enrollment patterns and associated consumer expenditures. Utilizing extensive datasets derived from Medicare Advantage and Medicare Part D enrollments, the study explores the intricate relationships between broker incentives, consumer behavior, and overall healthcare costs. Through robust statistical methods, the analysis seeks to identify significant correlations between the financial incentives offered to brokers and the decisions made by beneficiaries during the enrollment process.

The findings underscore the critical role of broker commissions in shaping the choices of Medicare beneficiaries, revealing that higher commission structures are often associated with increased enrollment in specific plans, irrespective of the actual value or suitability of those plans for consumers. This paper illuminates how such dynamics contribute to a misalignment between broker incentives and consumer welfare, potentially leading to increased out-of-pocket costs for beneficiaries. Furthermore, the research examines demographic variations in enrollment trends, providing insights into how broker influence varies across different populations, including low-income individuals, minority groups, and those with chronic health conditions.

In addition to empirical analysis, the paper also engages in a qualitative examination of broker practices, assessing how the communication strategies and marketing approaches employed by brokers can further complicate consumer decision-making. The role of transparency in the enrollment process emerges as a significant theme, suggesting that lack of clear information can hinder beneficiaries from making informed choices that align with their healthcare needs and financial circumstances.

To address these critical issues, this research advocates for targeted policy interventions aimed at enhancing transparency and ensuring that broker commissions are structured in a manner that prioritizes consumer welfare. Recommendations include the establishment of standardized disclosure requirements for brokers regarding their commission structures and potential conflicts of interest, alongside measures to promote consumer education about Medicare options. By aligning broker incentives with the best interests of consumers, it is posited that overall satisfaction with Medicare enrollment and subsequent healthcare experiences can be improved.

This paper contributes to the growing body of literature on Medicare policy by providing an empirical assessment of the influence of broker commissions on enrollment trends and consumer costs. The implications of these findings extend beyond mere academic inquiry, presenting actionable insights for policymakers, regulators, and healthcare stakeholders aiming to optimize the Medicare enrollment process and improve outcomes for beneficiaries. The overarching goal is to foster a more equitable and effective healthcare system that truly serves the needs of all Medicare beneficiaries.

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References

S. L. Yu and G. U. Hu, “Healthcare Fraud Detection Using Machine Learning: A Review of Current Trends and Future Directions,” Journal of Healthcare Engineering, vol. 2021, Article ID 7819351, 2021.

Hughes-Cromwick, P., Root, S. & Roehrig, C. Consumer-Driven Healthcare: Information, Incentives, Enrollment, and Implications for National Health Expenditures. Bus Econ 42, 43–57 (2007).

A. Thomas and J. K. Adams, “Enhancing Predictive Analytics in Health Insurance through Data Integration,” International Journal of Information Technology & Decision Making, vol. 19, no. 1, pp. 69-91, 2020.

Karaca-Mandic P., Feldman R., Graven P. (2018). The role of agents and brokers in the market for health insurance. Journal of Risk and Insurance, 85(1), 7-34. https://doi.org/10.1111/jori.12139

Hunter, Benjamin M. "Going for brokerage: strategies and strains in commercial healthcare facilitation." Globalization and Health 16 (2020): 1-13.

F. M. O’Neill et al., "The economic burden of chronic diseases: Insights from population health data," American Journal of Managed Care, vol. 25, no. 12, pp. 653-661, Dec. 2019.

T. A. Perry, “The Future of Claims Processing: Trends and Innovations,” Insurance Journal, vol. 57, no. 3, pp. 19–25, 2021.

Meyers DJ, Belanger E, Joyce N, McHugh J, Rahman M, Mor V. Analysis of drivers of disenrollment and plan switching among Medicare advantage beneficiaries. JAMA Intern Med. 2019;179(4):524–32.

Kuye IO, Frank RG, McWilliams JM. Cognition and take-up of subsidized drug benefits by Medicare beneficiaries. JAMA Intern Med. 2013;173(12):1100–7.

L. T Thomas and J. P. Rodriguez, "Dynamic Pricing Models for Personalized Health Insurance Plans," Journal of Risk and Insurance, vol. 88, no. 2, pp. 300-318, 2021.

Abaluck J, Gruber J. Choice inconsistencies among the elderly: evidence from plan choice in the Medicare part D program. Am Econ Rev. 2011;101(4):1180–210.

A. R. S. Alkarbi and K. M. Alshahrani, “Cost-Benefit Analysis of Automated Claims Processing Systems,” International Journal of Information Management, vol. 57, no. 5, pp.102–115, 2022.

Y. Yi and H. Lee, “Cost-Effectiveness of Predictive Analytics for Early Risk Identification in Health Insurance,” Value in Health, vol. 20, no. 5, pp. 663-669, 2017.

R. P. McLafferty, "Data privacy in population health analytics," Health Affairs, vol. 39, no. 4, pp. 678-683, Apr. 2020.

H. R. Abreu and S. W. Chan, "Interoperability and integration in population health management," Journal of Health Information Science, vol. 6, no. 1, pp. 1-10, 2020.

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