While the perspective on and effects of Population Health Management (PHM) differ according to the stakeholder or “player,” as I discussed in an earlier post, all the players agree they need cutting-edge analytics to make sense of their population.
To recap, in PHM the players include health systems, practitioners, insurance companies, employers and government agencies. Perhaps the simplest definition of PHM that seems to be accepted by all parties is this: Meeting the healthcare needs of a defined population of individuals, from the healthiest to the highest risk, with the right programs at the right time to ensure the best outcomes possible. Common stakeholder questions include:
- What does my population look like and what are its overall healthcare needs?
- How do I keep the healthy people healthy?
- How do I best manage those that are already sick?
- Whom do I need to target for care management/intervention?
- Who is at highest risk for hospitalization, disease progression, higher costs, or other negative outcomes, and how can I best mitigate that risk?
Given those common questions, it may seem as if it would be a simple task to identify the analytic methods required and start churning out new analytics as fast as possible. However, in the analytics world, nothing is as simple as it may first appear!
Developing an analytic to support these broad business needs requires answers to three key questions. First, who is the population that is to be managed? Depending on the perspective, this could be any of the following:
- Individuals assigned to a particular physician or to an entity (e.g., Accountable Care Organization (ACO) or a Patient Centered Medical Home (PCMH)) for management
- Individuals who have sought, or are likely to seek care from a particular health system
- Individuals within a specific geographic community
- Individuals enrolled in a particular health insurance plan
- Employees of a given organization
As you might imagine, different populations may require very different analytics. For example, a population of basically healthy fully employed young individuals may require analytics focused primarily on prevention and wellness, while a population of older, less healthy adults may require a more proactive disease management approach. True PHM requires analyzing different types of individuals in different ways. There is no “one size fits all” approach in analytics.
Tomorrow, I will discuss the second and third key questions.
Senior Director, Advanced Analytics
Added later – here are links to the other two blogs: