Bountiful Resources In Your Own Backyard: Thoughts on Smart Use of Data–Blog by Diane Stollenwerk, MPP
The Minnesota All Payer Claims Database has data from 1.1 billion claims for care provided to more than 4.3 million people in the state. That’s about 89% of insured people in Minnesota.
For what seems like the 98th day in a row, I picked a sizeable number of tomatoes from my backyard garden. They are red, sweet and abundant, and I wouldn’t dream of letting them go to waste, dying on the vine or sitting unused on my kitchen counter. It would be difficult to eat them all raw: We’d get sick of tomatoes and, while they are good for you, eating tomatoes alone has limited nutritional value. Instead, I’ve been adding them to other ingredients to create a variety of healthy dishes to eat in the days and months to come.
My tomato problem brings to mind two important considerations when using health care data to inform your purchasing decisions. For most of my career, I’ve worked with employers and others in regional and state coalitions committed to improving health care value by using data to assess quality, cost and outcomes. Some routinely measure quality or efficiency of health care services and treatments, the impact of health plan actions, utilization rates, prices or charges or cost of care by region, and levels of need in a specific community or across a broad population. To do this, some of us are lucky to live and work in place that have an abundance of data available to us through an All Payer Claims Database (APCD), or another resource with data aggregated from several sources.
An abundant source of excellent data
If you are in Minnesota and you don’t know about the Minnesota APCD, or if you know about it but you and your benefits consultants aren’t using it to augment their analyses and inform your health care decisions, you are letting an excellent data source die on the vine.
The largest set of health care data in the state is ripe and ready for your use. The Minnesota APCD has data from 1.1 billion claims for care provided to more than 4.3 million people in the state, or about 89% of insured people in Minnesota. This is not a proxy dataset that some big consulting firm created, then modified based on modeling to try to approximate what might be happening in this state. The robust data in the MN APCD is what actually happened in Minnesota. And there’s no cost to you or your consultants to use it. Analyses from the MN APCD are online and free.
If you are in Minnesota and aren’t using the Minnesota APCD to augment analyses and inform health decisions, you are letting an excellent data source die on the vine.
Statewide claims trends and insights
Anyone can access MN APCD extracts or public use files that offer the most comprehensive picture of trends, utilization, variation and other insights about health care in Minnesota. Admittedly, it won’t give you all you want because the reporting rules are currently the most restrictive in the country: Analyses from the MN APCD data are not allowed to actually name any hospital, medical group, or health plan. Even with that, however, the MN APCD is a solid place to start for statewide context and comparisons. Don’t let this valuable resource go to waste.
And just like a healthy recipe with lots of ingredients, tapping into different data sources can result in richer analyses and more informed decisions versus relying on smaller datasets. However, because details matter – like being able to see which hospital is the most expensive for C-sections or hip replacements, or which has the lowest infection rates – most employers and payers use smaller data sets, generating analyses from a health plan’s or self-insured employer’s in-house data. The problem is that such data reflects a subset of what’s going on. Without the ability to see context and comparisons, it’s impossible to accurately determine how much improvement might be possible, and identify top performers who deserve your business, or who can share their best practices to help motivate low-performers to improve.
For example, in a five-county region on the West Coast, we used data from several health plans to look at how often physicians got patients to fill prescriptions for generic drugs, rather than expensive brand-name drugs. We focused on four drug categories that have many generic options, because the potential cost savings was huge: Every 1% increase in the generic fill rate in any of the four categories would mean at least $2 million less would be spent by employers and individuals for their prescriptions. When comparing the results based on self-insured and commercial data versus Medicaid data, we discovered that generic fill rates were much better for people on Medicaid. Why? Two reasons: The benefit design had clear financial incentives for providers to talk with patients about generics, plus the Medicaid program worked hard to educate physicians and patients about the relative quality and value of generic drugs. Medicaid information provided a wake-up call for employers and commercial health plans, offering insight how to actually reduce spending. This analysis from community-wide data was actionable for medical groups, for example, where individual physicians in the same medical practice had wide variation in the generic fill rates, doctors could walk down the hall and talk with their own peers to learn how to improve.
Just like a healthy recipe with lots of ingredients, tapping into different data sources can result in richer analyses and more informed decisions versus relying on smaller datasets.
Using only in-house data, or data from a subset of the market, is limiting in what you and others can learn from and do with the analysis. Like trying to live on tomatoes (or any one ingredient) alone because it is growing in your backyard — it may be easier, but the health value is limited.
To put a finer point on it, many health plans analyze data from their own book of business (or a self-insured employers’ dataset) and share certain results with hospitals and other providers for accountability and to motivate improvement. Yet these separate, competing analyses can be limited in value and might actually be working against each other.
A few years ago, I led a focus group with physicians about quality reports they routinely received from various health plans. The doctors talked about the reports from different insurers, who were each addressing the same topics and supposedly measuring the same things. But because the underlying data from each health plan covered only a portion of the physician’s panel of patients, and the measurement results were not calculated in exactly the same way, the reports reached conflicting conclusions. One physician summed it up bluntly, “If I change to improve for one health plan, I’ll do worse on scores for another. So I take all of the reports, throw them in the trash, and continue practicing like I always do.” How much duplicative money is spent conducting analyses on smaller sets of data and producing quality or cost reports that ultimately have no impact?
Huge value in your own “backyard!”
Just as it’s wrong to leave ripe and tasty vegetables unharvested, it’s a shame to miss out on the value that you and your benefits team can get from tapping into the analyses and reports from MN APCD. It’s right in your own backyard! And you might even want to get involved in discussions about how to make this abundant data source even more useful in the future. Just don’t forget to combine it with data sources you already use to get the greatest possible value from comparisons with other payers, programs and regions, and identifying practical actions that you, your benefits team, providers and others can take to get higher quality, more affordable, and effective health care for your employees and their families.
Diane Stollenwerk is the founder and president of StollenWerks, Inc. She has served on the Maryland Health Care Commission and as vice president of Stakeholder Engagement at the National Quality Forum in Washington, D.C.
Her focus areas are multi-stakeholder engagement to develop workable solutions to address community needs, patient and consumer engagement, and using data for measuring, reporting and aligning incentives to improve health and health care. View her full bio here.