A Blueprint for Cost-Effective and Equitable Healthcare Delivery through Technology Automation
DOI:
https://doi.org/10.47941/jts.2547Keywords:
Medicaid Redetermination, Risk Sharing Models, Automation in Healthcare, Value-Based Care, Predictive Analytics, Healthcare Analytics, Healthcare EquityAbstract
Purpose: The primary purpose of this paper is to explore how automation in Medicaid redetermination and risk-sharing models can enhance operational efficiency, reduce manual errors, and align financial incentives with patient outcomes, thereby driving cost containment and improved provider accountability.
Methodology: The study adopts a comprehensive qualitative approach, leveraging a strategic analysis of advanced data systems, cloud-based platforms, and scalable integration frameworks. The paper synthesizes insights from existing literature, industry reports, and case studies to propose an integrated model for modernizing Medicaid management.
Findings: The integration of automation in Medicaid redetermination significantly improves operational efficiency, reducing processing times by as much as 30% and enhancing eligibility accuracy through real-time data integration and predictive analytics. Risk-sharing models, including shared savings contracts and performance-based incentives, align financial objectives with patient outcomes, reducing healthcare costs by up to 10% while improving provider accountability and patient satisfaction.
Unique Contribution to Theory, Practice, and Policy: This paper advances theoretical understanding by proposing a unified framework that integrates automation and value-based care within Medicaid, highlighting the transformative role of predictive analytics and cloud-based platforms. Practitioners are provided with a blueprint for implementing automated Medicaid redetermination and risk-sharing models, showcasing best practices for achieving operational efficiency, financial sustainability, and health equity. The paper outlines policy implications, emphasizing the need for regulatory frameworks that support data privacy, interoperability, and continuous innovation, paving the way for resilient public healthcare systems. By leveraging automation and value-based care frameworks, this blueprint offers a path to a more efficient, accountable, and patient-centered Medicaid program, paving the way for a resilient public healthcare system
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References
Centers for Medicare & Medicaid Services. (2022). Risk-Based Arrangements in Health Care - https://www.cms.gov/priorities/innovation/key-concepts/risk-based-arrangements-health-care
Centers for Medicare & Medicaid Services. (2023). System Automation Resource Guide - https://www.medicaid.gov/resources-for-states/downloads/system-automation-resource-guide.pdf
Guan, G., & Braverman, M. (2022). Empirical Characteristics of Affordable Care Act Risk Transfer Payments - https://arxiv.org/abs/2208.02372
HealthEdge. (n.d.). Why Automation Technology Will Disrupt Claims Processing - https://healthedge.com/resources/medicaid/why-automation-technology-will-disrupt-claims-processing
Innovista Health. (2021). Understanding Risk-Bearing Contracts in Value-Based Care - https://innovista-health.com/risk-bearing-contracts-value-based-care/
Medicaid.gov. (2018). Accelerating the Adoption of Value-Based Payment in Medicaid - https://www.medicaid.gov/medicaid/downloads/accel-adoption-vp-pay.pdf
mPulse Mobile. (2019). Conversational AI Activates Hard-to-Reach Medicaid Populations - https://go.mpulse.com/hubfs/2023%20-%20Case%20Studies/mPulseMobile-CaseStudy-Medicaid-Reach.pdf
TechTarget. (2022). Understanding the Value-Based Reimbursement Model Landscape - https://www.techtarget.com/revcyclemanagement/feature/Understanding-the-Value-Based-Reimbursement-Model-Landscape
Wikipedia. (2023). Accountable Care Organization - https://en.wikipedia.org/wiki/Accountable_care_organization
Wikipedia. (2023). Pay for Performance (Healthcare) - https://en.wikipedia.org/wiki/Pay_for_performance_%28healthcare%29
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Copyright (c) 2025 Sravanthi Kalapati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
