The Truven Health Blog

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


Enterprise-Wide View of Population Health Can Lead to More Value

By Truven Staff
The are focused on supporting their organization’s initiatives to drive analytics for population health management. Rightfully so, they are looking at the extreme demands on their IT departments, their lower margins and the magnitude of change occurring around them to make deliberate and specific steps toward managing their populations’ health. Many of these experiments are happening in very discrete and targeted ways, employing resources carefully, all of which makes a vast amount of sense in the current healthcare landscape. The danger of this approach, however, is a myopic view of population health by the stakeholders that fails to deliver true organizational impact.

Equally critical to husbanding resources appropriately with these new endeavors is the need to have an enterprise view of how these strategies will play out over the whole network over time. A synchronized organizational strategy that involves all key stakeholders will guide resource decisions that will be critical in preventing future costly rework. A common aggregation of population health needs across stakeholders will often uncover the requirement to have a longitudinal view of patient data. This ensures common patient counts in the most basic of analytics, as well as more complex questions of exclusions, duplicative information and which measures matter the most to ensure compliance with quality, utilization and outcomes goals for the whole enterprise. Furthermore, the level of investment required for an organization to fully liberate the vast sums of useful data currently locked in provider facilities and data silos is a strategic choice better suited to serving multiple purposes to ensure that the investment reaps its potential economic value and supports enterprise-wide decisions.

Working with limited data sets in the short-term to quickly get an experiment up and running or focusing on meeting one Centers for Medicare & Medicaid Services (CMS) requirement must be in line with the long-term goal of having financial and operational metrics that are in sync, both for the clinician leveraging that data for his or her own practice as well as the CFO looking at the outcomes across a program, as an example. Neglecting to take time to sort through these universal data needs in the planning of a program can lead to frustration and a potential lack of trust in the data which will in turn severely challenge key stakeholder engagement.

It’s a simple idea that is much easier said than done in the real world.

A fully vetted strategy that is supported by partners with real-world experience on the cost/benefit choices to be made during these kinds of population health analytics efforts, will go a long way to set an organization on a track, break population health into manageable pieces, and ensure that the whole organization ultimately moves forward together. Without this alignment and focus, limited resources can be lost to isolated pet projects that fail to impact overall patient outcomes or the organization’s bottom line.

Rebecca Molesworth
Manager, Solution Management

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

By Truven Staff
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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