1998
1998
MedInsight History
Our journey of innovation and impact
For over 25 years, Milliman MedInsight’s participation in the world of healthcare analytics has centered on our cutting-edge tools and solutions that are revolutionizing the industry. Milliman MedInsight understands that success in healthcare requires collaboration across the entire ecosystem, from payers and providers to ACOs, purchasers, and consultants. With a strong foundation in actuarial science and extensive expertise in healthcare, Milliman MedInsight is dedicated to empowering payers and providers.
How Milliman MedInsight makes a difference in the market:
Milliman MedInsight Data Confidence Model
Ensure credible, high quality data, with speed to interpretation
Industry-leading Milliman MedInsight Benchmarks
Better evaluate what is driving cost, utilization, and trends within your population
Consultants
Milliman’s long history in actuarial science gives us a leg-up with unbiased research and expertise in risk assessment and healthcare management
Actionable savings
Identify, implement, and measure within a specific timeframe and immediate follow-up steps to act upon
Curated reporting
Tailored and specific reports to our clients needs across all healthcare markets
Data aggregation including EHR
Support better care coordination, health outcomes tracking, and decision-making with comprehensive integrated data
Relevant resources
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What is risk adjustment software? A guide for healthcare organizations
Risk adjustment depends on accurate data, complete documentation, and consistent workflows. For healthcare organizations managing risk-based contracts, those requirements can become difficult to coordinate across teams, systems, and populations. Claims data may live in one place, clinical data in another, documentation review in a separate workflow, and reporting in static dashboards or spreadsheets. Risk adjustment … What is risk adjustment software? A guide for healthcare organizations
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Whitepaper
The importance of data selection in AI model training, testing, and deployment
Artificial intelligence is only as good as the data behind it. In healthcare, even small differences in inputs can lead to vastly different outcomes, making data quality and provenance critical to evaluating AI models. This whitepaper introduces the Milliman MedInsight Data Confidence Model, a structured approach built on 25 years of experience matching the right … The importance of data selection in AI model training, testing, and deployment
Whitepaper
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Signs your organization needs better clinical and claims data integration
Almost no healthcare organization intentionally builds a fragmented data environment. Fragmentation develops through reasonable decisions: an acquired practice arrives with a second EHR instance, a risk-based contract introduces another payer file, a vendor relationship brings in laboratory or pharmacy feeds. Their combined effect is a set of systems describing the same patients and members differently. … Signs your organization needs better clinical and claims data integration
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How to choose the right risk adjustment vendor
Executive Summary Choosing a risk adjustment vendor should never be reduced to coding function alone. The right platform should improve documentation quality, integrate into clinical and operational workflows, provide transparent analytics, support prospective and retrospective programs, and maintain audit readiness. Organizations should prioritize configurable technology, explainable AI, strong governance, measurable outcomes, and an implementation approach … How to choose the right risk adjustment vendor
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Responsible AI in healthcare analytics: The MedInsight approach
Key Highlights Introduction› Analysis in the AI era› Principles of AI adoption at MedInsight› Conclusion› Download the white paper Introduction The recent explosion of artificial intelligence (AI) capability and deployment at scale has dramatically altered conversations and expectations for technology solutions. Healthcare and healthcare analytics are no exceptions to this. AI as a technological innovation … Responsible AI in healthcare analytics: The MedInsight approach
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Heart failure admissions: Cost implications of potentially avoidable inpatient care
Key Highlights Background› Methodology› Analysis› Conclusion› Download the article Heart failure (HF) represents one of the most pressing cardiovascular health challenges facing the United States (US). According to the CDC, heart failure occurs when the heart cannot pump enough blood and oxygen to meet the body’s needs. Although it is a serious condition, it does … Heart failure admissions: Cost implications of potentially avoidable inpatient care
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Examining trends and impacts of rising cesarean section delivery in nulliparous women
Key Highlights Introduction› Study population and delivery trends› Maternal age and payer mix› Costs and complications› Conclusion› Download the article Childbirth is the leading cause of hospital admissions in the United States, with cesarean deliveries being the most frequently performed surgical procedure.1 Cesarean deliveries are a crucial and life-saving intervention2 but they can also pose … Examining trends and impacts of rising cesarean section delivery in nulliparous women
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Why clinical data aggregation is essential for better healthcare analytics
Clinical data accumulates in many places at once, collecting in electronic health records, laboratory systems, pharmacy platforms, care management tools and claims files, each with its own format and update schedule. The consequences are familiar: two departments run comparable reports and arrive at different figures, and analysts spend their time reconciling records instead of interpreting … Why clinical data aggregation is essential for better healthcare analytics
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When screening becomes waste: Ovarian cancer testing in asymptomatic women
Key Highlights The “Silent Killer” and the Urge to Screen› The Current State of Ovarian Cancer Screening› Key Findings From the Analysis› Conclusion: Turning Insight Into Action› Key Takeaways for Healthcare Leaders› Download the article The “Silent Killer” and the Urge to Screen Ovarian cancer is the deadliest gynecologic cancer and the 10th leading cause … When screening becomes waste: Ovarian cancer testing in asymptomatic women
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Case Study
How Missouri Health Value Collaborative cut reporting time from two days to one hour and tripled its regional coverage.
Missouri Health Value Collaborative (MHVC) is a St. Louis-based nonprofit regional health improvement collaborative that brings together self-insured employers, providers, and payers to improve healthcare quality and affordability across Missouri. Its four-person analytics team produces the population health scorecards, utilization reports, and market share analyses that give self-insured employers a clearer picture of the populations … How Missouri Health Value Collaborative cut reporting time from two days to one hour and tripled its regional coverage.
Case Study
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Under CJR-X, the focus is on both quality and cost: Two former CMS officials on what the earlier bundles reveal
Key Highlights Introduction › Interview › What this means for your hospital › With the CJR-X final rule now released in July 2026, the first mandatory, nationwide episode-based payment model for lower extremity joint replacement is set to begin on January 1, 2028. For most acute care hospitals, participation is now settled and required, and … Under CJR-X, the focus is on both quality and cost: Two former CMS officials on what the earlier bundles reveal
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Mitigating the impact of high-cost claimants: A payer analytics approach to total cost of care
It has long been understood that a relatively small portion of any covered population accounts for a disproportionately large share of healthcare spending. A 2025 study by the Employee Benefit Research Institute (EBRI), which examined 13.1 million individuals with employer-sponsored health coverage in 2022, found that the highest-cost 20% of members were responsible for 84% … Mitigating the impact of high-cost claimants: A payer analytics approach to total cost of care
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