Population segmentation — so what?
Population segmentation is definitely the buzz word of the month. At OBH we’ve been working on segmentation for as long as we have been measuring outcomes. To us, they are two sides of the same coin. So what is population segmentation, and why is it important in healthcare?
Outcomes measurement only works if you are measuring outcomes for people with similar needs. Measuring the total number of emergency hospital admissions for the whole population may tell you that there is a problem, but it doesn’t give you any tools or insights to understand, and then solve the problem. The people who are being admitted to hospital most frequently will have a whole host of different health needs and problems, so a one size fits all solution will not work.
This is why groupings based on risk stratification are not the best foundation for outcomes measurement. Risk stratification identifies people at high-risk of a certain event or costly treatment. So it bases groupings on ‘volume of need’, rather than what that need actually is.
Conversely, segmentation is the grouping of people who share similar needs. Meaningful insights can be drawn by then measuring outcomes that match those needs – so measuring things like exacerbations in people with organ failure, or falls in people with frailty. Segmentation is the first step to making sure that the outcomes we measure are person-centred, and meaningful to the people we measure them for.
Following a review of segmentation and risk stratification models used in healthcare, we have developed our own dynamic data-driven application of the internationally-recognised Bridges to Health modeli. It works really well for whole population outcomes measurement, because it includes every single person in the population, and uses needs-based criteria for each segment.
Implementing the model locally means that we can start to answer some of the questions that really matter, to patients, but also to the health and care system. Without a data-driven approach, how would a health system know that new service redesigns, or integrated working, are having any meaningful impact on people’s live? With these insights, we can start to understand how people progress through the segments over their life course. We can start to test whether this progression can be slowed down or prevented entirely. To test whether we are deferring the onset of diabetes, or preventing someone with mild COPD from progressing to severe COPD. We can look at the interactions between different conditions and different segments, and the impact that these have on outcomes.
There is a constant feedback loop between segmentation and outcomes. Segmentation provides a tangible view of the population’s health as a whole. As outcomes are improved, the segmented population will gradually change. This is our goal. We will know we are making progress when we are keeping people in the ‘Healthy’ segment (ie. in good health) for longer.
This is where healthy lifespan (or healthspan) comes in – for the first time, we are measuring, in an objective way, the amount of time that people spend in good health (and the proportion that this represents of people’s total lifespan). OBH have developed this measure to move us on from the status quo, where the focus of measurement is on the period of our lives that we spend in deteriorating health. By re-shifting the lens to view the population’s health in terms of our number of healthy years, we are creating a tool that truly supports the case for prevention, and public health interventions. Using healthspan gives us insight into what a sustainable future could look like, and what we need to do to get there (read more about healthspan here: humanhealthspan.com)
But without a meticulously developed segmentation model, measurement of healthspan would be impossible. The extensive work we have done on segmentation at OBH has resulted in an approach that can provide fast, up-to-date insights into a locality’s population health. Any emerging trends, in healthspan, or related segmentation metrics, are identified and displayed rapidly, as they occur. A single, one-off, static view of the segmented population wouldn’t tell us enough about how people progress through the segments, or whether patterns of progression were changing over time. This is why our focus has been on developing a dynamic model, with an emphasis on measuring the impact of interventions to delay and prevent ill health on the segmented population. The OBH segmentation model brings to life each person-centred journey through the life course, and builds this up to provide meaningful, population-level insights, like healthspan.