Featured Product
This Week in Quality Digest Live
Health Care Features
Claudine Mangen
If you have the energy to try and address organizational overwork, start small
Gregory Way
Drug designers are rethinking their response to medications that affect multiple targets
Adam Zewe
Research on machine-learning models that can help doctors consult patient records more easily
Karina Montoya
Analysis of social and economic impact bolsters the fight against hepatitis C
Tom Rish
Keep it organized and ready for inspection at any time

More Features

Health Care News
Google Docs collaboration, more efficient management of quality deviations
MIT course focuses on the impact of increased longevity on systems and markets
Delivers time, cost, and efficiency savings while streamlining compliance activity
First responders may benefit from NIST contest to reward high-quality incident command dashboards
Enhances clinical data management for medtech companies
Winter 2022 release of Reliance QMS focuses on usability, mobility, and actionable insights
The tabletop diagnostic yields results in an hour and can be programmed to detect variants of the SARS-CoV-2 virus
First Responder UAS Triple Challenge focuses on using optical sensors and data analysis to improve image detection and location
Free education source for global medical device community

More News

Eston Martz

Health Care

A Surgeon’s View of Data-Driven Quality Improvement

Seeing is believing, even with ‘little’ data

Published: Wednesday, March 23, 2016 - 17:41

There’s plenty of noisy disagreement about the state of healthcare, but when you look beyond the controversies, a great deal of common ground exists.

Many agree that the way we’ve been doing things is wasteful and inefficient, when healthcare should be delivered as efficiently and effectively as possible. Although successful models exist for using data to improve quality, medical practitioners have been slow to adopt these methods. At Minitab, we’ve been talking to physicians, nurses, administrators, and other healthcare professionals in several countries to gain insight into the challenges of using data to improve healthcare processes. We’re also looking to learn how Minitab might be able to help.

Operating with a scalpel—and statistics

We had a particularly enlightening conversation with David Kashmer, M.D., chief of surgery for Signature Healthcare in Brockton, Massachusetts. In addition to being a surgeon, Kashmer is a lean Six Sigma Black Belt. During the 10 years since earning his belt, he’s become passionate about using quality improvement methods to enhance trauma and acute care surgery. Kashmer is helping fellow practitioners to embrace improvement methods, and he writes about his experiences on the Surgical Business Model Innovation blog.

Kashmer spoke 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?’” recalls Kashmer.

Nobody’s saying that anymore. Kashmer has shown that applying even basic statistical methods can yield big improvements in patient outcomes. 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,” says Kashmer.

The human face of healthcare 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 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,” Kashmer says. “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,” Kashmer notes. “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,” explains Kashmer. “But the median time for a trauma patient looked great, so the group couldn’t figure out why we had an issue. So we used Minitab to see the distribution, and it was a non-normal 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,” Kashmer continues. “The distribution revealed a tail of patients who were a lot worse when they stayed in the emergency department for more than six hours. 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 the full interview

I’d like to thank 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 I can recap here, so if you’re interested in using data to improve healthcare quality, I encourage you to read the full interview with David Kashmer, M.D.

Discuss

About The Author

Eston Martz’s picture

Eston Martz

For Eston Martz, analyzing data is an extremely powerful tool that helps us understand the world—which is why statistics is central to quality improvement methods such as lean and Six Sigma. While working as a writer, Martz began to appreciate the beauty in a robust, thorough analysis and wanted to learn more. To the astonishment of his friends, he started a master’s degree in applied statistics. Since joining Minitab, Martz has learned that a lot of people feel the same way about statistics as he used to. That’s why he writes for Minitab’s blog: “I’ve overcome the fear of statistics and acquired a real passion for it,” says Martz. “And if I can learn to understand and apply statistics, so can you.”

Comments

Followup

Eston, what nice work with the write up. You've nicely captured some of the challenges we face in healthcare.

To our colleague above, well said on many points. The zero harm target makes for a great talking point that we all strive for. I would respectfully disagree with the ease you seem to feel the defect reduction associated with Six Sigma occurs. Charts of all sorts are easy to make and yet that is not the heart of the process.

Glad to hear about the challenge and setting this goal that is easy to endorse, would respectfully add that following our progress toward it, knowing whether we are sustaining our efforts, and knowing when (and whether) to intervene requires a rigorous understanding of how our process performs rather than just how we feel about it. I would add that a great system is built on the fire to achieve zero harm and the rigorous data collection and evaluation that lets us know when we've gotten there and / or that we need to do more.

I also agree: zero harm is the goal and would add we are making progress in healthcare in that we can see the goal. That's a start. The next layer, I think, is to continue to embrace data driven framing of our goal and this is not often done yet in our field to the level seen in other realms. Last, Lean and Six Sigma are, unfortunately, not easily deployed or executed well in healthcare that I've ever seen...but are very worthwhile when they are as they can unlock incredible improvements.

Curious: given your obvious expertise with Lean and Six Sigma tools, how do you feel about the rise of Big Data in Healthcare and where have you seen it used for quality? Do you see it as more valuable to discover counter intuitive solutions than our typical Six Sigma tools?

Pledge Allegiance to Science and Evidence

At the last Institute for Healthcare Improvement conference, Don Berwick asked attendees to recommit to measurement, statistics and evidence. But from looking at the hundreds of poster presentations, I can tell you that most teams are still using line, bar and pie charts with an occasional trendline to tell their improvement story.

As the author of Lean Six Sigma for Hospitals and the QI Macros, I can tell you it's not that hard to draw a control chart, histogram or Pareto chart and start to understand the clinical and operational processes in a hospital. And it doesn't take weeks or months to do the analysis; you can do it in an hour or two.

As we all know, Six Sigma is easy; people are hard, but ZERO HARM is the new target for healthcare. To get there will take a willingness to embrace the few key tools required to improve healthcare.