There has been plenty of noisy disagreement about the state of health care in the past several years, but when you get beyond the controversies surrounding various programs and changes, a great deal of common ground exists.
Everyone agrees that there's a lot of waste and inefficiency in the way we've been doing things, and that health care should be delivered as efficiently and effectively as possible. But while a lot of successful models exist for using data to improve quality, the medical field has been slower than many other industries to adopt such data-driven quality improvement (QI) methods.
We have been talking to physicians, nurses, administrators, and other professionals at health care organizations in the United States and other countries to get more insight into the challenges of using data to improve health care processes, and to learn how Minitab might be able to help.
Operating with a Scalpel—and Statistics
We had a particularly enlightening conversation with Dr. David Kashmer, chief of surgery for Signature Healthcare in Brockton, Mass.
In addition to being a surgeon, Kashmer is a Lean Six Sigma Black Belt. In the 10 years since earning his belt, he's become passionate about using QI methods to improve trauma and acute care surgery. He also helps fellow practitioners do it, too, and talks about his experiences at the Surgical Business Model Innovation blog.
Kashmer told us about the resistance he encountered when he first began using statistical methods in his practice: “I kept hearing, ‘This guy is nuts...what’s he even talking about?’”
Nobody's saying that any more. Kashmer has shown that applying even basic statistical methods can yield big improvements in patient outcomes, and those once-skeptical colleagues are now on board. "When they saw the results from using statistical process control rather than typical improvement methods, they understood and began to appreciate their value," Kashmer said.
The Human Face of Health Care Quality
I've written previously about the language of statistics and how it can get in the way of our efforts to communicate what's really important about our analyses. Kashmer keyed in on similar themes when we asked him about the apparent reluctance among some in the medical profession to use data analysis for quality improvement.
"The language of the motivation for using statistics—to guard against type 1 and type 2 errors—is lost on us," he said. "We focus more on what we think will help an individual patient in a particular situation. But when we learn how statistics can help us to avoid making a change when nothing was wrong with the patient, or to avoid thinking there wasn’t a problem when there was one…well, that’s when these techniques become much more powerful and interesting."
For Kashmer, the most compelling way to show the value of data analysis is to draw a direct connection to the benefits patients experience from an improved process.
"Making decisions with data is challenging since it doesn't resonate with everyone," he told us. "Putting a human face on data and using it to tell a story that people can feel is key when talking about the true performance of our system."
Big Insights from a Little Data
Kashmer shared several stories with us about how using data-driven methods solved some tenacious problems. One thing that struck me was that even very straightforward analyses have had big impacts by helping teams see patterns and problems they otherwise would have missed.
In one case, an answer was found by simply graphing the data.
"We felt we had an issue with trauma patients in the emergency department, but the median time for a trauma patient looked great, so the group couldn’t figure out why we had an issue," Kashmer explained. "So we used Minitab to see the distribution, and it was a nonnormal distribution that was much different than just a bell curve."
Simply looking at the data graphically revealed why the team felt there was a problem despite the median.
"We saw that the median was actually a bit misleading—it didn’t tell the whole story, and that highlighted the problem nicely: the distribution revealed a tail of patients who were a lot worse when they stayed in the emergency department for over six hours, so we knew to focus on this long tail instead of on the median. Looking at the data this way let us see something we didn’t see before."
Read Our Full Interview
We'd like to thank Dr. Kashmer for talking with us, and for his efforts to help more healthcare organizations reap the benefits of data-driven quality improvement. He had much more to say than we can recap here, so if you're interested in using data to improve health care quality, I encourage you to read our full interview with Dr. Kashmer.