Broker-Led Medicare Enrollments: Assessing the Long-Term Consumer Financial Impact of Commission-Driven Choices

Authors

  • Dr. Juan Carlos Pereira University of Barcelona, Biomedical Data Science and Health IT, Spain Author
  • Tobias Svensson Swedish Institute of Computer Science (SICS), AI for Sustainable Development Group, Sweden Author

Keywords:

Medicare, broker-led enrollment

Abstract

The landscape of Medicare enrollment has evolved significantly, particularly with the increasing role of insurance brokers in facilitating the selection of Medicare plans. This paper investigates the implications of broker-led Medicare enrollments on consumer financial outcomes, specifically emphasizing the influence of commission-driven choices on long-term financial sustainability. The utilization of brokers has become a prevalent trend in the Medicare enrollment process, wherein these intermediaries assist beneficiaries in navigating the complex array of plan options, which include Medicare Advantage and Medicare Part D plans. However, the potential for brokers to prioritize personal financial incentives over the best interests of consumers necessitates a thorough examination of the repercussions on the beneficiaries’ financial health.

This study employs a mixed-methods approach, incorporating quantitative analysis of enrollment data alongside qualitative interviews with Medicare beneficiaries who utilized brokers for enrollment. By analyzing enrollment trends, plan selection patterns, and subsequent healthcare utilization, this research aims to elucidate the relationship between broker involvement and the financial implications for consumers. Key variables examined include out-of-pocket costs, premium variations, and service access, all of which are influenced by the broker's recommendations.

Initial findings suggest that broker-led enrollments may result in suboptimal plan selections, potentially leading to increased financial burdens for beneficiaries. These outcomes may stem from a lack of transparency in broker compensation structures and insufficient consumer education regarding plan details. Furthermore, the study explores the potential ethical dilemmas faced by brokers in their advisory role, particularly in instances where financial incentives may conflict with consumer welfare.

The analysis also highlights the regulatory landscape governing broker activities and the implications of commission structures on the choices presented to beneficiaries. Existing policies designed to enhance transparency and consumer protection are evaluated, with recommendations proposed to ensure that broker-led enrollments align with the best interests of consumers.

The findings underscore the necessity for heightened scrutiny of the broker enrollment model within Medicare, advocating for reforms that prioritize consumer education and equitable access to unbiased information. The potential for increased consumer awareness regarding broker incentives and the implications of plan selections is a focal point of this research, with an emphasis on empowering beneficiaries to make informed decisions that align with their long-term healthcare needs and financial goals.

This paper contributes to the ongoing discourse regarding the intersection of Medicare enrollment processes and consumer financial outcomes. By illuminating the complexities of broker involvement and the potential long-term ramifications of commission-driven choices, the research provides critical insights that can inform policymakers, regulators, and stakeholders within the healthcare system. Ultimately, fostering a more transparent and equitable enrollment process will be imperative to safeguarding the financial well-being of Medicare beneficiaries in an increasingly complex healthcare environment.

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References

Praveen, S. Phani, et al. "Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management." Journal of Intelligent Systems & Internet of Things 13.1 (2024).

Jahangir, Zeib, et al. "From Data to Decisions: The AI Revolution in Diabetes Care." International Journal 10.5 (2023): 1162-1179.

Pushadapu, Navajeevan. "Real-Time Integration of Data Between Different Systems in Healthcare: Implementing Advanced Interoperability Solutions for Seamless Information Flow." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 37-91.

Rambabu, Venkatesha Prabhu, Munivel Devan, and Chandan Jnana Murthy. "Real-Time Data Integration in Retail: Improving Supply Chain and Customer Experience." Journal of Computational Intelligence and Robotics 3.1 (2023): 85-122.

Priya Ranjan Parida, Chandan Jnana Murthy, and Deepak Venkatachalam, “Predictive Maintenance in Automotive Telematics Using Machine Learning Algorithms for Enhanced Reliability and Cost Reduction”, J. Computational Intel. & Robotics, vol. 3, no. 2, pp. 44–82, Oct. 2023

Reddy Machireddy, Jeshwanth. “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, Feb. 2022, pp. 501-518

Kasaraneni, Ramana Kumar. "AI-Enhanced Virtual Screening for Drug Repurposing: Accelerating the Identification of New Uses for Existing Drugs." Hong Kong Journal of AI and Medicine 1.2 (2021): 129-161.

Pattyam, Sandeep Pushyamitra. "Data Engineering for Business Intelligence: Techniques for ETL, Data Integration, and Real-Time Reporting." Hong Kong Journal of AI and Medicine 1.2 (2021): 1-54.

Qureshi, Hamza Ahmed, et al. "Revolutionizing AI-driven Hypertension Care: A Review of Current Trends and Future Directions." Journal of Science & Technology 5.4 (2024): 99-132.

Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.

Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.

Sahu, Mohit Kumar. "AI-Based Supply Chain Optimization in Manufacturing: Enhancing Demand Forecasting and Inventory Management." Journal of Science & Technology 1.1 (2020): 424-464.

Pushadapu, Navajeevan. "Advanced Artificial Intelligence Techniques for Enhancing Healthcare Interoperability Using FHIR: Real-World Applications and Case Studies." Journal of Artificial Intelligence Research 1.1 (2021): 118-156.

Sreerama, Jeevan, Venkatesha Prabhu Rambabu, and Chandan Jnana Murthy. "Machine Learning-Driven Data Integration: Revolutionizing Customer Insights in Retail and Insurance." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 485-533.

Rambabu, Venkatesha Prabhu, Amsa Selvaraj, and Chandan Jnana Murthy. "Integrating IoT Data in Retail: Challenges and Opportunities for Enhancing Customer Engagement." Journal of Artificial Intelligence Research 3.2 (2023): 59-102.

Selvaraj, Amsa, Bhavani Krothapalli, and Venkatesha Prabhu Rambabu. "Data Governance in Retail and Insurance Integration Projects: Ensuring Quality and Compliance." Journal of Artificial Intelligence Research 3.1 (2023): 162-197.

Althati, Chandrashekar, Venkatesha Prabhu Rambabu, and Munivel Devan. "Big Data Integration in the Insurance Industry: Enhancing Underwriting and Fraud Detection." Journal of Computational Intelligence and Robotics 3.1 (2023): 123-162.

Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.

Kodete, Chandra Shikhi, et al. "Hormonal Influences on Skeletal Muscle Function in Women across Life Stages: A Systematic Review." Muscles 3.3 (2024): 271-286.

Ahmed Qureshi, Hamza, et al. “The Promising Role of Artificial Intelligence in Navigating Lung Cancer Prognosis.” International Journal for Multidisciplinary Research, vol. 6, no. 4, pp. 1–21.

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Published

20-02-2024

How to Cite

[1]
D. J. Carlos Pereira and T. Svensson, “Broker-Led Medicare Enrollments: Assessing the Long-Term Consumer Financial Impact of Commission-Driven Choices”, J. of Artificial Int. Research and App., vol. 4, no. 1, pp. 627–645, Feb. 2024, Accessed: Nov. 07, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/265

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