It's time to grow beyond yesterday's population health management, and health organizations must employ uncommon methods to truly discover new methods to do so, writes Jason Burke, a senior advisor for advanced analytics at the University of North Carolina School of Medicine in InformationWeek.
Burke argues that population health management offerings these days look "remarkably like yesterday's offerings"--focusing on individual chronic diseases, relying on claims data, lacking a larger inventory of factors with real predictive models and including little or no real-time intervention capabilities.
"Health organizations need a balanced portfolio of health analytics capabilities--some focused on supporting the status quo, and others helping the organization to pursue more strategic goals," Burke writes.
Some of Burke's strategies for getting an organization out of "box 1" include asking the following questions:
- How well does our data architecture provide reusable data assets that can be used for any clinical research or quality improvement projects? Every new project shouldn't require more data work, Burke writes.
- How easily can we explore relationships [among] different types of data (clinical, financial, administrative, patient-reported outcomes) as opposed to only within a single type of data? Burke writes than an organization should have access to more than one of these forces at all times.
- Are our methods tied to specific patient subpopulations, or are they extensible to any patient populations? This ties back again to flexibility, extensibility and reusability--methods should apply to more than just one patient population.
Earlier this year, National Coordinator for Health IT Karen DeSalvo declared population health management as one of ONC's primary focuses for 2014.
A report published last year by the Institute of Health Technology Transformation determined that analytics are the key to population health management and the success of ACOs. The paper concedes that there is "no single roadmap to achieving analytics excellence," but sets out an analytics framework starting with establishing a data governance committee, putting together an analytics team through setting benchmarks and ways to measure efforts.
To learn more:
- read Burke's post in InformationWeek