Identifying Appropriate Metrics For Population Management

By Nancy Zoelzer

19 December 2013

For many reasons, including the advent of the Affordable Care Act, Population Health Management is gaining increased attention from healthcare organizations. A population health initiative has the promise to make care proactive, better coordinated and more customized for the population. A previous blog posting – Analytics for Population Health Management – outlined how populations are defined and presented a sampling of metrics for measuring performance. In this blog article we dive deeper into the process of identifying the appropriate metrics for population health management.

One size does not fit all for population-based analytic methodologies. One reason to look at populations from a variety of perspectives is to analyze trend. What is driving trend? Is it something clinical? Is it geographic disparity? Or network variations? Differing metrics are needed in order to effectively analyze a population. For example, looking at compliance with recommended HbA1c lab testing is appropriate when looking at a population of diabetics, but the same measure is far less meaningful when looking at a population of cancer patients.

In identifying the appropriate analytic metrics one should consider the population to be analyzed, as well as the focus of the population analysis initiative. There are several categories of analytic tools for population health management, including measures, service categorization, clinical categorization and risk adjustment.

Measures include:

  • Primary Prevention Measures – Disease avoidance, immunization rates, and wellness program participants.
  • Secondary Prevention Measures – Early detection and treatment of disease, colon cancer screening, and breast cancer screening.
  • Tertiary Prevention Measures – Reducing the impact of disease and aspirin use in CHF patients.
  • Quaternary Prevention Measures – Avoidance of unnecessary or unsafe interventions and avoidance of imaging for low back pain.

Service Categorization is a methodology used to analyze resource utilization and cost by service types. Categorization enables the creation of a cost model for population trending, benchmarking, and profiling. Milliman’s Health Cost GuidelinesTM is a widely-used methodology used to organize healthcare claims into service categories.

Clinical Categorization, such as Milliman’s Chronic Condition Hierarchical Groups, helps organize populations by disease cohorts.

Risk Adjustment is a methodology used to assess the overall health of a population or sub-population. Risk adjustment enables comparison across populations by adjusting the health risk of the population. Additionally, individuals within a population can be stratified by risk. There are many risk adjustment methodologies available in the industry, including the Milliman Advanced Risk Adjuster (MARA).

Other Analytic Tools include resource efficiency tools (e.g., Dartmouth Atlas, NYU Avoidable ED Visits, and Prometheus), care management tools, and Milliman Global RVUs (a method to measure utilization across all categories of care).

For a robust population analysis, Benchmarks are needed in addition to the analytic tools. Benchmarks enable the comparison of actual population cost and utilization with a benchmark.

For more detailed information click on the box below to receive Milliman’s white paper entitled Population Health Management Concepts. Watch for upcoming blog posts that dive into development of a population analysis for a specific population. Click here to review an earlier posting describing Pediatric population analysis – Pediatrics Population Health Management.

Contact us to learn more about healthcare data analytics