Most employers routinely make mistakes when tracking population health analytics. Here is how you can avoid the most common pitfalls.
Without a full picture of your company’s population health, making decisions on programs that improve health and reduce rising benefit costs would be like throwing darts while blindfolded. Having current and correct analytics based on all your company’s health care data empowers you to make accurate decisions and implement programs and benefit design changes based on real information.
The good news is that a growing number of employers are relying on analytics to improve population health. However, there is also bad news: most employers I come across routinely make at least one if not more of the following four common mistakes when it comes to how they create and use their population health analytics. Here is how you can avoid some common pitfalls and get all the benefits that analytics can offer.
1. Don’t trade speed for accuracy.
Many of us as benefits professionals have been in the situation where our boss wants something and they “needed it yesterday.” Far too often I meet employers who have literally rushed to upload their population health data to begin a new analytics program, only to cringe when they see the results. When it comes to anything in data, there is an old saying that is true: “junk in, junk out.” To get reliable reports based on data, the initial data we put into the system needs to be fully accurate and “clean,” free of errors or confusion.
Even something as simple as matching people between different data sources can be a monumental task for some company’s data sets. We regularly see data sources that have incorrect employee names (first/last), dates of birth, and even social security numbers (sometimes even from their own HRIS systems). Imagine rushing to upload all this data into a new analytics system—you get all the data in but then the system can’t accurately tell you what percent of employees have diabetes or high BMI because literally, people are missing in the data set.
Taking the time to “clean” data before putting it in, and double checking everything for accuracy can be expensive and at times, painfully slow. However, it is a necessary step.
2. Don’t be left in the dark
Too often I come across employers who trust their analytics vendor is using the correct definitions and algorithms to produce reports. To these employers, I say “trust but verify.” Consider this simple example: your analytics vendor uploads data and delivers a report that tells you your employee population has a hypertension rate of 30 percent of the population. You check with your carrier who reports back their number is closer to 10 percent (which can tend to happen with carriers who often use a “cookie cutter” approach to reporting).
What happened? Your analytics vendor may have applied a definition of hypertension more in line with your company culture, than that “cookie cutter definition” used by your carrier. However, the result is a report that is not accurate or actionable. How can you make changes to your wellness strategy to reduce rates of hypertension when you aren’t sure how many employees are truly affected? Is hypertension a significant problem for your employee population or a minor one?
As much as it creates extra work and time, benefits professionals need to take the painstaking step of asking their vendor to fully explain all definitions—where do they come from and what do they really mean? Then, they need to take the additional step of asking the vendor to explain —in plain English—how they will be used when reports are run.
3. Don’t stop at the top level of analysis
I once worked with a company who couldn’t explain their incredibly high expenditures on late-stage breast cancer care even though a huge percentage of the insured population was getting regular mammograms. On paper, a population getting early detection should not have such high rates of cancer, discovered at a late stage.
We took a more in-depth look at their data. As it turns out, in the insured population, mostly the spouses of this predominately male workforce were getting their regular mammograms. However, only a tiny percent of female employees were getting regular mammograms. And among this population, several presented with Stage 4 Breast Cancer. We drilled down again. This handful of women employees were finding it difficult to find the time to get their regular screening. The solution was simple: female employees now get four hours of PTO to get mammograms every year.
Had the company just looked at the topline data, which showed they had high rates of cancer expenditures and high rates of mammography, they would have been confused and unable to take action. However, once they drilled down into the data, they could understand the why and steps they could take to improve care and reduce costs.
4. Don’t stop with just the analytics
Like with the example above, the best vendors won’t just hand you a report. They should also be able to advise you on strategies and program designs that need to occur based on that report. They should present solutions to your problems. Moreover, perhaps most importantly, they should also be able to tell you the cost of those solutions and whether your current programs and benefit designs are worth their price. Unfortunately, time and time again I meet benefits professionals who have stacks and stacks of analytics reports and no idea what to do with the information. That is not the value of analytics.
Analytics are powerful, and they are the future of population health. They can and should be able to tell us what health issues your employees face and also what to do about them to improve health and reduce costs. However, to get their full benefit, we cannot be satisfied with a rush job or a job half done. Benefits professionals need to understand good analytics takes time, expense, and elbow grease. And they need to be prepared to tell their HR leaders these hard facts.
Date: April 05, 2019