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The latest healthcare topics from a trusted, proven, and unbiased source.


Getting to Enterprise Analytics in the Government Healthcare Sector Begins With a Modern, Connected Data Warehouse


By Rick Williams/Monday, June 30, 2014
Rick Williams imageEnterprise analytics is a hot buzz phrase these days. What used to be an analyst-only topic has moved to the executive level. And it’s no secret that the idea of analyzing disparate data from across an organization is becoming increasingly important in all of healthcare today – perhaps even more so in the government sector.

Policymakers are talking about it, elected officials want it, and taxpayers expect that it’s already happening.

Meanwhile, state agencies, such as Medicaid and Departments of Health and Human Services (HHS), are facing an urgent need to curtail rising costs, boost efficiencies, report accurate information, and improve quality of care.

To achieve that, they need to see not only the big-picture of program data, but also to understand the intricacies of population health and even coordinate patient-level care across agencies. And thanks to Affordable Care Act-driven concepts, like ACOs and risk-based contracts, it’s all at a tipping point.

The key lies in an interoperable data hub – a modern, connected warehouse that  facilitates the flow of data and reporting, automates workflows, and helps staff be more efficient while providing the right decision-making knowledge to the right stakeholders. 

Of course, as this type of warehouse is developed, particular attention must be paid to data integrity – because without that, enterprise analytics are meaningless.

The development should be guided by an iron-clad master data management process, ensuring that all data values being collected and connected speak the same language. This results in a data warehouse that truly becomes a single source of truth across departments and agencies.

At Truven Health, we see the warehouse development process unfolding with these steps:
  • Identify stakeholders and “champions”
  • Assemble strong executive leadership
  • Create a shared vision of the modern data warehouse
  • Formalize the governance structure
  • Establish a clear decision-making process
  • Evaluate the governance system and adapt as necessary
  • Maintain transparent communications throughout development
  • Identify an enterprise reference model as part of the information architecture
After the enterprise warehouse is developed, we can then apply the all-important, advanced metrics and modeling. Just a few of the typical analytics and applications we recommend include:
  • Calculations for episode grouping
  • Hierarchical Condition Categories (HCC) score calculations
  • Risk stratifications
  • A measures engine
  • Practice-to-cohort comparisons
  • Disease registries
Ultimately, the end result will be an ultra-connected depth and breadth of useful data that can be streamlined and analyzed at all levels, from a policy analyst to a caseworker on the front lines.

Rick Williams
VP Data Warehouse

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