Today, payers face mounting pressure to deliver better outcomes, control the cost of care, and manage financial risk. Meeting these goals requires more than access to raw data—it demands actionable insights that support strategic, cost-effective decision-making. This is where the integration of clinical and claims data becomes transformative.
By bridging the gap between clinical records and claims histories, payers gain a comprehensive view of each member’s health journey. This unified approach not only reveals opportunities for proactive care management and accurate risk adjustment but also helps identify gaps in care, optimize provider performance, and reduce unnecessary costs. Ultimately, clinical and claims data integration supports smarter resource allocation and strengthens value-based care initiatives.
In this blog, we’ll explore why integrating clinical and claims data is essential for payers aiming to lead in a data-driven, cost-effective healthcare environment.
Why multi-source clinical data matters
Multi-source clinical data is essential for payers because it provides a more complete, accurate, and timely view of a member’s health. Relying solely on claims data offers only a retrospective look at care that’s already been billed, and is often missing critical clinical context. Additionally, receiving fragmented clinical data results in a limited view of the member’s health journey and reduces opportunities to effectively coordinate care across the continuum. The union of both claims data and clinical data can provide a complete picture.
For example, organizations that rely solely on EHR data available at the point of care often operate with a narrow view of a member’s health history because their EHR data is limited only to those services delivered within their system. This limited visibility can result in suboptimally managed populations or loosely managed populations, as care teams may miss critical information that exists outside their immediate systems. This is because different data formats and systems can make integrating clinical and claims data time-consuming and complex. Without access to timely or comprehensive data, it becomes difficult to proactively identify gaps in care, manage chronic conditions effectively, or coordinate across providers.
Moderately managed populations often benefit from the integration of additional data sources, such as admissions, discharge, and transfer (ADT) notifications. These data points provide more timely insights into key care events, allowing for more responsive follow-up and better care transitions. However, the insights are still fragmented, often siloed across systems or lacking clinical depth, which can hinder true continuity of care.
In contrast, well-managed populations are supported by enterprise-level access to rich, longitudinal clinical data across the care continuum.i By incorporating clinical data from multiple sources such as electronic health records (EHRs), labs, vaccination registries, and pharmacy systems, payers can gain real-time insights into diagnoses, treatments, and outcomes. This more complete picture enables earlier identification of rising-risk members, supports more precise risk adjustment, and drives more effective care coordination. With this comprehensive view, care teams are better equipped to deliver personalized, proactive care that addresses both current conditions and future risks. Ultimately, multi-source clinical data helps payers partner more efficiently and impactfully with providers and other key stakeholders to improve population health management, reduce costs, and succeed in value-based care arrangements.
How payers can improve their capture rate for clinical data
Among the ways that payers and what are often referred to as payviders (aka providers taking financial risk) can improve their capture rate is to establish different strategies for different types of data. Examples include:
- Establishing direct connections for primary clinical data
- Leveraging Health Information Exchanges (HIEs) to expand reach
- Incorporating digital fax packages to cover smaller or rural providers
- Working with national aggregators, especially for transient populations like snowbirds and for settings like long-term care facilities
- Reinforcing the importance of accurate risk coding for reimbursement in discussions of how to appropriately improve clinical documentation
- Integrating smoothly into provider workflows to establish trust and minimize abrasion—that is, reducing any unnecessary administrative burden or disruptions that can negatively impact provider efficiency and satisfaction
Maximizing clinical data integration impact
Extracting the true value of your healthcare data requires more than capturing it. By integrating clinical data into cloud-based data stores and enterprise data warehouses, you enable a streamlined process that drives greater resource efficiency and accelerates the identification of priorities. This integration supports advanced analytics and powerful AI tools that empower your organization to build predictive models, identify system-wide efficiency opportunities and generate high-impact, tailored insights for informed decision-making. Different teams across your organization—whether focused on population health, risk management, medical economics, or provider engagement—require specialized analytics and reporting to meet their unique objectives. High-quality benchmarks are essential for tracking progress and making informed decisions about resource allocation.
Adopting a big-picture approach to population health, for example, requires looking past only the most accessible or cost-related data. When organizations exclude pharmacy claims from their analyses simply because their products don’t cover those expenses, they lose out on key insights into members’ overall health, medication adherence, and potential future risks. Additional solutions like the Milliman ACO Builder® suite further expand your analytic capabilities by incorporating valuable third-party content, including provider performance data. This allows you to benchmark your organization’s provider performance against peers, supporting more strategic and data-driven decision-making.
The critical importance of data confidence
Data confidence is critical to the success of your data analytics efforts. If you don’t build in the necessary processes to ensure that your data is thorough, accurate, standardized, and contextually complete, you cannot rely on the data for the insights you need to drive care improvement and efficiencies.
Healthcare data is complex and fragmented, and its collection is subject to gaps, disparities, documentation errors, and duplication. From diagnosis codes to the range of care settings, there are many factors that can introduce data anomalies. Even when the data has been well scrubbed, the sheer volume and complexity of clinical data demands sophisticated tools and processes for effective management and analysis.
The unique challenges of integrating third-party clinical data make it all the more important to partner with an expert who not only understands these complexities but also has a proven track record of navigating them with care. Choosing a partner with established expertise in clinical data integration ensures this critical information is managed securely, sensitively, and effectively, enhancing the overall value and impact of your data-driven initiatives.
How Milliman MedInsight can help
With decades of healthcare analytics experience and actuarial expertise, the Milliman MedInsight team is committed to empowering healthcare organizations to transform data into actionable insights and meaningful innovation for improving the quality, cost, and efficiency of the care they provide.
Our comprehensive suite of tools and solutions is designed to help your organization collect, integrate, and leverage your healthcare data. Backed by our industry leading Data Confidence Model, we can ensure you have the clarity and confidence to identify key opportunities for improvement. MedInsight can provide your organization with a comprehensive view that allows you to:
- Identify hidden risk: Members who appear stable based on diagnosis codes alone might be at higher risk due to patterns in prescription fills or medication non-adherence.
- Improve care management: By understanding the full scope of a member’s health journey, care teams can intervene more effectively and personalize outreach.
- Enhance predictive accuracy: Models that incorporate all relevant data provide more precise risk scores, helping organizations allocate resources where they’ll have the greatest impact.
- Drive better outcomes: Ultimately, seeing the bigger picture supports proactive, rather than reactive, care. It enables organizations to anticipate needs, close care gaps, and improve both clinical and financial results.
Learn how Milliman MedInsight can help you integrate clinical data
Schedule a call: Connect with one of our healthcare analytics experts to learn more about solutions for maximizing clinical data, identifying cost drivers and enhancing risk adjustment and risk modeling efforts.
Visit our website: Learn more about our clinical data integration capabilities.
Watch our webinar: Explore how a MedInsight customer leveraged clinical data integration to support value-based care.
References:
i. MedInsight provides several analytic products and methods to evaluate health plan efficiency. MedInsight Benchmarks leverage more than 70 years of Milliman research and embeds Milliman’s widely accepted Health Cost Guidelines (HCG) at its core. MedInsight Benchmarks are available for all of the industry standard medical cost and utilization measures, including, but not limited to, [Admits per 1,000], [Avg LOS] and [Allowed PMPM]. MedInsight Benchmarks offer the ability to generate custom tailored benchmarks with deep drill-downs of insight for evaluating health plan efficiency. And, unlike other benchmarking products, MedInsight Benchmarks provide a range of benchmarks related to how populations are managed. This range is called the Degree of Healthcare Management (DoHM). Loosely managed equates to a DoHM of 0% and represents relatively high total cost and utilization levels. Well managed equates to a DoHM of 100% and represents, in general, the lowest achievable total cost and utilization levels. The majority of plans are moderately managed, i.e., they have a DoHM of between 20% and 60%.
