Avoiding the trap of “data perfectionism”

June 29, 2017
Steve Meurer Ph.D., MBA, MHS, Executive Principal, Data Science and Member Insights, Vizient

Meurer has extensive experience in health care administration, including serving as chief quality and information officer at the DeKalb Regional Healthcare System in Atlanta and as vice president of operations and performance improvement at St. Mary’s Medical Center in Langhorne, PA. He has spent time in increasing roles of responsibility at the Orlando Regional Healthcare System, University of Pittsburgh Healthcare System, and BJC HealthCare. He has taught quality to graduate students at Loyola University and Rush University in Chicago, Georgia Tech, Georgia State University, and Temple University.
Meurer holds a doctorate in health services research from St. Louis University. He also holds two master’s degrees from the University of Florida and a bachelor’s degree in psychology from the College of the Holy Cross.

As the head of data science and member insights at Vizient, I regularly consult with executive leaders at hospitals and health systems of all sizes, including some of the nation’s largest academic medical centers (AMCs). One of the most common questions I am asked about is the future of healthcare.

While I’m asked in a number of ways, the most frequent theme is this: “What can I do today to ensure financial viability for my organization in the communities we serve?”

My answer typically starts with a question of my own: “How are you using your data?”

Really, you have the data

Working with health organizations across a number of clinical analytics projects, I’ve observed a prevailing belief among hospitals that they lack the data needed to achieve transformative improvements in cost and quality. In most cases, the exact opposite is true.

Some of this could be perpetuated by industry conversations happening right now about analytics, including how interoperability (siloed data), a limited understanding of social determinants of health, and a myriad of other issues such as data latency limit the effectiveness of analytics.

There is certainly going to be a future in which all the aforementioned challenges are resolved (more or less) and a golden age of health data ensues. But hospital leaders shouldn’t wait around for it. Don’t let perfect be the enemy of good. In truth, most organizations have all the data they need to make big improvements today. And, there is no shortage of low-hanging fruit.

From our own work, for example, we have found that re-evaluating the utilization of lab and radiology tests, blood products, drugs, and ancillary services in order to reduce clinical variation is one of the largest untapped sources of expense reduction in the healthcare system. Using our clinical analytics tool, we recently looked at one 1,000-bed member hospital. Our analysis of the data shows that if the hospital were to reduce drug utilization to where the top 25% of AMCs are in just 10 clinical conditions, it could save more than $7M.

Another example comes to mind when I showed the leadership team at a member hospital that they were over-utilizing a high-cost ancillary item for spinal fusion patients as compared to three similar hospitals. The CEO told me that he was approached by the neurosurgeons a number of months ago saying the ancillary service was a best practice for spinal fusion patients and it reduced length of stay. The data told a different story.

We were able to show that the length of stay had not been reduced and it was actually higher than the other comparison hospitals. Whenever I present this kind of information to members, the number-one response I get is, “do we have access to this data?” When I tell them yes, they want to know why they haven’t acted upon it.

So how can organizations start using their data more effectively? A good starting point is to reframe the relationship your staff—from executive leadership to clinicians—have with the data that is currently available to set performance goals and measure progress.

Trusting in data (securing buy-in)

The biggest analytics challenge facing most organizations today is not related to technology access or obtaining data. The problem lies with promoting meaningful engagement around the information that is currently in-hand.

To this point, one of the most cited challenges we hear from members is securing physician buy-in on how data should be used to track performance. This challenge has been compared to moving across the stages of grief from denial to acceptance.

The truth is that securing physician trust in benchmarks that evaluate their performance requires a nuanced approach. There are often conflicting views about what measures truly gauge quality, and using comparisons against public benchmarks isn’t always welcomed.

Creating more structure around the areas of care delivery that an organization wants to evaluate can help. For one of our hospital members, the approach was to re-structure the entire organization into clearly defined service lines and attach an analytics and quality resource to each service line. Then a comparison feature—afforded by our clinical database of hospital members—was used to rank the performance of each of the service lines against top peers.

Mostly importantly, physicians were engaged in the selection of the peer comparison group, which proved to be an essential step in securing their buy-in. Taking this a step further, other members have introduced intra-service line performance comparisons, openly reporting on how individual physicians are contributing toward quality and cost goals, to promote transparency and stoke a competitive spirit around improving patient care outcomes.

This leads to a final point about fostering trust in data. It often requires a culture shift, across all levels of the organization, including leadership. It can take time for stakeholders to grow comfortable with having performance openly reported across the organization, especially when it relates to individual departments or personal performance. Leadership needs to be prepared to demonstrate that it is equally committed to quality by investing in solutions once issues are identified. This can, on occasion, mean adding more staff and resources to address complex issues such as hospital readmissions.

The bottom line is that hospitals largely have the data they need to achieve transformative change today. Yet, many fall into the trap of waiting for better data or for the perfect analytics tool before fully committing to quality improvement. In practice, however, the larger lift tends to be with catalyzing organizational change. In other words, engaging the people who will use these tools, who must wrestle with the data, and collaborate and build trust with one another in order to move forward with care improvement. The present is a perfect time for your organization to start this process.

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