Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis
Keywords:
Medicare, broker commissions, enrollment trendsAbstract
Understanding Medicare enrollment, customer spending, and broker fees is vital in the changing U.S. healthcare system. This data-driven study investigates Medicare broker commission enrollment and cost. Complete Medicare Advantage and Medicare Part D enrollment statistics examine broker incentives, client behavior, and healthcare costs. The research links broker financial incentives to beneficiary enrollment using advanced statistical methodologies.
Regardless of value or appropriateness, high broker commissions effect Medicare plan enrollment and beneficiaries' alternatives. Broker incentives and customer welfare may raise out-of-pocket prices. Demographically, brokers affect low-income, ethnic, and chronically ill populations.
This article examines broker communication and marketing on customer choice subjectively and statistically. Insufficient enrollment transparency may prevent beneficiaries from making educated healthcare and financial decisions.
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.