Data-Driven Quality Improvement: Improving Provider Performance in Medicare Advantage
DOI:
https://doi.org/10.47941/jts.2692Keywords:
Medicare Advantage, Data engineering, Quality improvement, Predictive analytics, Value-based careAbstract
In the evolving landscape of Medicare Advantage (MA), quality performance is not only a regulatory requirement but a strategic imperative. This paper explores how data engineering and advanced analytics are transforming provider performance through data-driven quality improvement initiatives. By integrating claims, EHR, patient-reported outcomes, and social determinants of health, organizations can generate a holistic view of care delivery. Predictive modeling, provider scorecards, and incentive-aligned financial models enable proactive interventions and measurable improvements in CMS Star Ratings and financial outcomes. The paper also addresses operational challenges such as data silos and provider buy-in, emphasizing the role of cloud platforms, collaborative ecosystems, and robust data governance. Looking forward, technologies like AI, NLP, and blockchain promise to elevate the impact of analytics in value-based care. This study provides a comprehensive, data engineer–oriented perspective on optimizing MA offerings through scalable, real-time, and compliant analytics strategies that support long-term sustainability and patient-centered outcomes
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On Medicare Advantage and Star Ratings: Centers for Medicare & Medicaid Services. (2023). Medicare Advantage Star Ratings. Retrieved from https://www.cms.gov/medicare/health-plans/medicare-health-plans-quality
On Predictive Analytics and Big Data in Healthcare: Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123–1131. https://doi.org/10.1377/hlthaff.2014.0041
On Data Governance and Silos in Healthcare: Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3. https://doi.org/10.1186/2047-2501-2-3
On Value-Based Care and Provider Incentives: Porter, M. E., & Lee, T. H. (2013). The strategy that will fix health care. Harvard Business Review, 91(10), 50–70. https://hbr.org/2013/10/the-strategy-that-will-fix-health-care
On AI and Machine Learning in Clinical Practice: Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
On Cloud Computing and Emerging Technologies in Health IT: Kuo, A. M.-H. (2011). Opportunities and challenges of cloud computing to improve health care services. Journal of Medical Internet Research, 13(3), e67. https://doi.org/10.2196/jmir.1867
On Provider Dashboards and Performance Analytics: McWilliams, J. M., Hatfield, L. A., Landon, B. E., & Chernew, M. E. (2018). Medicare spending after 3 years of the Medicare Shared Savings Program. New England Journal of Medicine, 379(12), 1139–1149. https://doi.org/10.1056/NEJMsa1803388
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Copyright (c) 2025 Sravanthi Kalapati
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