Demystifying Population Health
By Jeff Wu
Population health was once again a major topic of this year’s HIMSS conference. We saw even more vendors offering products, services, and solutions aimed at helping organizations deal with the challenges population health management presents.
Unfortunately, population health is such a broad domain that no singular solution really encompasses all of it. As a result, vendor offerings tend to only address a specific challenge. The wide and varying offerings across vendors adds confusion to the topic.
Population health shouldn’t be an industry buzzword that’s approached with trepidation. Instead, we need to understand the categories of challenges we are trying to address and the process for developing interventions to solve them. Let’s start by taking a look at the three categories that population health management interventions fall into.
- Government or mandated interventions. For many organizations, this is the primary (and perhaps only) component of their population health strategy. Some initiatives, like becoming an accountable care organization, encompass requirements that address items that will be discussed below. For many organizations, this may be enough.
- Enterprise population health interventions. These encompass interventions that are applied to the full population of an organization’s patients. Immunization and vaccination interventions or physical activity interventions are broadly applied to an organization’s full patient population. As organizations begin to try to standardize care, interventions aimed at variation reduction are also encompassed here.
- Cohort, group, or sub-population health interventions. This class of interventions is the most varied and covers any intervention that addresses a sub-population of patients. Some examples of interventions in this category include health maintenance for diabetes patients, preventative care efforts like breast cancer screening in women over 50, and depression/PTSD screening for military veterans.
Population health management evolves linearly in three stages that borrow some classical tools from epidemiological tracking.
- Passive surveillance. Passive surveillance involves the retrospective analysis of a specific issue. This is the evaluation of data that already exists. Passive surveillance addresses questions like, "How many of our diabetic patients got a glucose test in the last six months?" or, "How many of our patients got flu vaccines last month?" Most analysis starts from this level of surveillance. It’s important to note that the majority of organizations are just getting to this point in their analytical journey. Implementation of the EHR tools necessary to do this level of surveillance are finally settling and getting to a state that allows for this to happen. To date many ‘organized’ population health based initiatives focus only on this type of surveillance. CMS’s MSSP ACO initiative is a classic example of this, where an organization participating in the MSSP ACO need only report their measures for the first year to receive their financial incentive.
- Active surveillance. The next evolution is active surveillance. If passive surveillance identified how many patients got flu vaccines last month, active surveillance would try and answer the question how many of our patients got a flu vaccine last week or yesterday. If passive surveillance told us which of our diabetes patients got a glucose test in the last six months, active surveillance would try to address which ones are being well controlled. In the epidemiological world, passive surveillance relies on existing data, while active surveillance implies a program that generates more recent and/or new data. This could be as simple as querying the medical record or running a report more frequently for simple cases or designing a whole new workflow and data elements to monitor for more complex cases.
- Prescriptive intervention. Once a population or initiative is identified, prescriptive intervention is what an organization uses to address the problem. This is where the art of evidence-based medicine comes in. We now have a lot more data to develop more fine tuned and effective interventions. Things like smoking cessation no longer have to be just a pamphlet, a discussion with a provider, and then a check box in the medical record. Full care teams can be coordinated and then patients can be monitored to help them with compliance.
As the industry and technology continues to advance, so do the tools at our disposal. Sentinel surveillance and predictive analytics offer some exciting opportunities to do more earlier. Additionally, the increased volume of data allows us to start taking a more in-depth look at cost-effectiveness and variation reduction between treatments for diseases.
It’s imperative to remember that every organization’s population health strategy will necessarily be different. This is because each organization’s population of patients is different. The vendor perspective often approaches organizations with packaged solutions, when in reality, it’s almost impossible for these solutions to be “one size fits all.” Even a product geared to a specific population health goal will require nuanced configuration to be effective for an individual organization.
Here in Madison, Wisconsin, population health interventions for UW Health are drastically different than Dean St. Mary’s or Group Health Co-op. UW is an academic medical center that draws high-acuity patients from across Wisconsin, while Dean has the region’s only obstetrics practice and GHC handles only primary care needs. While these organizations may benefit from adopting collaborative population health initiatives like the MSSP ACO (which both Dean and UW are a part of), their intervention focuses differ significantly based on their unique patient populations. Seldom can a product or solution apply to both, and even more rarely will it work for both.
As the industry continues to shift care delivery to encompass a population-based perspective, we are constantly introducing changes to our workflows, our assumptions, and most importantly, our expectations. These changes introduce uncertainty and apprehension, but they are also our greatest opportunity. It’s important to realize that population health management isn’t actually anything new. We’ve been here before—we’re just upping the scale.
Jeff Wu is a population health researcher at the University of Wisconsin-Madison.