Learn more about MedInsight in our latest resources
Featured Resources
Modernizing healthcare analytics to deliver faster cost and utilization insights
Cambia Health Solutions (Cambia) serves more than 2 million members through its regional health plans and is guided by a mission to make healthcare easier and lives better. To support that mission, the organization set out to strengthen its analytics foundation with a more scalable, reliable approach to delivering timely, trusted insights for actuarial and … Modernizing healthcare analytics to deliver faster cost and utilization insights
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
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
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
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
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
Browse our full library of resources
All resources
Modernizing healthcare analytics to deliver faster cost and utilization insights
Cambia Health Solutions (Cambia) serves more than 2 million members through its regional health plans and is guided by a mission to make healthcare easier and lives better. To support that mission, the organization set out to strengthen its analytics foundation with a more scalable, reliable approach to delivering timely, trusted insights for actuarial and … Modernizing healthcare analytics to deliver faster cost and utilization insights
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
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
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
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
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
