When it comes to healthcare spending in the United States, there are some shocking facts:
- By 2020 annual healthcare spending is estimated to reach $4.64 trillion dollars. (That’s a big chunk of change.)
- In 2009, healthcare spending accounted for 17.6% of Gross Domestic Product (GDP). And by 2019, it’s projected to reach 19.3% of GDP.
- America’s healthcare spending is projected to grow at an annual average rate of 5.8% from 2010 to 2020. (That’s a growth rate 1.1% faster than the expected growth in GDP.)
But it doesn’t have to be that way. With the help of timely interventions, a significant portion of this burden could be avoided. By taking a holistic approach to health and well-being, monitoring the entire population (both diseased and non-diseased members) we can deliver impactful, personalized interventions to members in the greatest need. That’s the philosophy behind Total Population Health, but it hasn’t always worked out that way in practice.
Historically, organizations have relied on risk assessments, age/sex demographics, and pharmacy based models designed and used in disease management to identify members in the greatest need of these programs. But these models are flawed – they often rely on cost triggers to target individuals who exceed a certain cost threshold. But we know that low cost doesn’t necessarily mean low risk – they may be a “ticking time bomb” as a result of not maintaining their health.
There had to be a better way. Our Healthways Center for Health Research went to work developing a predictive model that would bridge the gap between the conception of the total population health approach and reality.
The result? The Avoidable Cost Model. Designed to identify the high-risk segments of the population who are likely to have near term, costly (but avoidable) in patient events, the avoidable cost model allows for proactive care management tailor fit to meet the needs of your population using member claim data.
And unlike standard, disease-specific models that are frequently used to predict future health risks, our model allows for early interventions for members with whom there is the greatest opportunity to have an impact and helps to optimize the use of intervention resources. It also produces greater healthcare savings; in fact, when compared with a model developed to predict high-cost members in a diseased population, the avoidable cost model captured an additional $15 million dollars in total savings.
To find out more about our research on the avoidable cost model and see how it compares to other models in the market, check out the paper below.
Predictive Modeling: The Application of a Consumer-Specific Avoidable Cost Model in a Commercial Population