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Case Study 7.23.2019

Defining Population Health Analytics Strategies

Trexin helped a regional Healthcare payer prepare for a gradual data-driven transition to value-based care.

Population Health Analytics Strategies

Business Driver

Our Client, a mid-sized health insurer, was actively studying how a transition to more value-based reimbursements could be accomplished. Focusing on the plan’s population health management capabilities, the Chief Medical Officer partnered with the Chief Information Officer (CIO) to lead the effort, and the CIO asked Trexin to help develop a strategy and roadmap for improving data management, analytics, and data-driven business processes to support population health and enable the gradual transformation to value-based care.


Led by a small team of senior-level resources with cross-functional expertise in Healthcare, provider engagement, business strategy, data/analytics, and information technology, Trexin applied its Assessment, Strategy, and Roadmap (ASR) methodology, which is a strategy execution framework. Phase 1 of the 3-phase project included voice-of-the-customer interviews with providers to ensure an inclusive definition of the business goal, strategy, and tactics. The current-state of the data and analytics ecosystem was also inventoried. In Phase 2, Trexin worked closely with internal stakeholders and subject-matter experts to translate the tactics into future-state capabilities and expansively define a future-state vision that included a conceptual technical architecture and gap analysis. Then in Phase 3, Trexin formalized the business case and created a project roadmap with individual project charters that defined how to build the future-state capability enhancements over a 3-year period.


The future-state vision, which drove internal alignment because it forced clarification on topics that were often discussed abstractly, addressed numerous capability enhancements to support population health and value-based care, including:

  • Improved view of claims data with integration of external/clinical data.
  • Improved population health analytics and management capabilities.
  • Explicit technology support for pay-for-performance, shared risk, and other value-based contracts.
  • Provider profiling capabilities for new product, network, and benefit design.
  • Improved risk assessment and physician-led care management capabilities.
  • Improved provider engagement, network design, and network management capabilities to improve outcomes and medical expense trend.
  • Improved member experience and employer engagement capabilities.

The data and analytics strategy to manifest these capability enhancements included a business-ready data layer feeding operational, financial, and population health analytics combined with a data lake component for rapid exploration and discovery. Consolidated analytics tools and standardized analytics processes were also defined.

Tagged in: Analytics, Healthcare & Life Sciences, Optimized Operations