Some of the nation’s largest insurers have begun integrating external specialty data with proprietary claims to optimize risk adjustment analytics, provider networks, and population health management — but their progress represents only the tip of the iceberg for what’s possible.
Maximizing the value of real-world evidence (RWE) may be the single greatest challenge facing stakeholders across the healthcare industry. Although patient-level data is now readily available from a multitude of sources, the significant hurdles to identifying and vetting those sources and then integrating datasets in a way that ensures data integrity, privacy, and compliance continue to be cited as significant barriers.
While risk-bearing entities (RBEs) are in the unique and enviable position of generating expansive proprietary data foundations, they too need to integrate external data sources to optimize performance. Market pressures demand accuracy that translates into fair premium pricing, high-quality care, provider networks that facilitate choice and continuity, and improved health outcomes. That’s why RBEs must pursue the precision that only integrated datasets can deliver.
Comprehensive + Contextualized Is the Key to Accuracy
When used alone, RBEs’ proprietary claims data fails to deliver the accuracy and precision needed:
-
- Actuarial modeling: With an estimated 15-20% of individuals changing health plans each year, there’s a continuous fluctuation in member populations. RBEs need access to new members’ comprehensive and complete (longitudinal) health histories for accurate risk modeling
- Underwriting: While claims data provides a superficial understanding of health history, it doesn’t capture the detail needed to accurately assess risk. For example, a CPT or HCPCS Level II code will reveal the lab test ordered, but not the results. RBEs need to integrate lab and EHR data to obtain context; e.g., A1C levels, body mass index, etc.
- Devising provider networks: Measuring physician-level performance requires a breadth and depth of data that exceeds that of any individual RBE — regardless of size. General estimates suggest that payers have visibility into 10-40% of a healthcare provider’s total patient volume and up to 50% of a provider’s Medicare population
- AI modeling: Predictive AI modeling requires a high quality and quantity of data that is longitudinal and representative in order to accurately learn patterns, make predictions, and support decision-making across a range of applications
- Member intervention programs: Program effectiveness studies/analytics are a tremendous asset for proving commercial value, but require both a longitudinal view and integrated specialty data such as lab and EHR in order for RBEs to analyze changes in utilization, quality, and total cost of care between a baseline and intervention cohort
- Care quality initiatives, network optimization, and risk-sharing arrangements: When RBEs’ view of the healthcare industry is restricted to proprietary claims data, they are limited in their ability to identify opportunities for improving care quality, reducing costs, benchmarking best practices, and ensuring accurate assessment of provider performance
A Glimpse at Return on Investment
RBEs that have successfully launched initiatives leveraging integrated datasets have shared the significant impact that comprehensive, complete RWE can make. Examples culled from across the industry include:
- Greater accuracy in actuarial modeling and underwriting risk scores with the integration of lab and EHR data
- Identification of members at high risk for chronic conditions and diseases 9-12 months earlier than when using claims data alone, enabling faster intervention
- Better adherence to medications and follow-up appointments when using SDoH data to develop “whole-person” risk scores and provide support services
- 30% reduction in unplanned hospitalizations among targeted members within 18 months
- Improved RAF scores, resulting in more accurate CMS payments for the Medicare Advantage population
- Greater provider satisfaction due to data-driven interventions and improved patient outcomes
Getting Started
Early adopters have proven that integrating external data with proprietary claims data can improve the performance of operations exponentially across the board. Choosing a single data partner that manages the identification, vetting, and compliant integration of specialty data sources can streamline an otherwise complex, time-consuming process — giving RBEs a jump-start in leveraging the power of comprehensive RWE.
To see more articles like this, follow Komodo Health on X, LinkedIn, or YouTube and visit our Resources Hub.
If you’d like to connect with one of our data experts supporting RBEs, just let us know. Talk to an Expert
1 Employer-Sponsored Health Insurance 101, Kaiser Family Foundation, May 2024.
2 Trends in Disenrollment and Reenrollment Within US Commercial Health Insurance Plans, 2006-2018, JAMA, February 2022.
