Quality Improvement Trends in Healthcare

What are the statistical ‘soft spots?’

Published: Wednesday, July 13, 2016 - 09:55

It’s been called a “demographic watershed.” During the next 15 years alone, the worldwide population of individuals aged 65 and older is projected to increase more than 60 percent, from 617 million to about 1 billion, according to a U.S. Census Bureau report.

Increasingly, countries are asking themselves: How can we ensure a high quality of care for our growing aging population while keeping our healthcare costs under control?

The answer? More efficiency. Less waste. Reduced error. Quicker turnaround. Improved patient outcomes. Which is, of course, where lean, Six Sigma, and quality improvement come in.

Another watershed: lean and Six Sigma in healthcare

Faced with the challenge of increased demand and rapidly rising cost, it’s no surprise that more lean and Six Sigma studies are being performed and published in the fields of medicine and healthcare. A search for “lean sigma” or “six sigma” in the U.S. National Library of Medicine database pulls up more than 470 published studies. The year-by-year search results are shown in the following trend analysis plot in Minitab.

Note that this trend forecast based on a linear model could be conservative. The database already shows 39 published studies during the first six months of 2016. In the coming years, these data may require a quadratic (curved) model!

A surprising breadth of applications

Quality improvement studies in healthcare run the gamut, e.g., reducing appointment no-shows, avoiding preventable hospital readmissions, cutting down on the amount of expired medical drugs and equipment that must be discarded, and reducing the number of falls in nursing care facilities. There’s even a study tracking and reducing the number of times the door is opened and closed during surgeries.

The one consensus in all these diverse studies? There’s no shortage of opportunity for trimming waste and for improving patient outcomes in the healthcare setting.

Improving on improvement: statistical ‘soft spots’

With the increased number of published studies on lean and Six Sigma in healthcare comes increased scrutiny. Review studies are beginning to look more closely at the methods used to monitor and measure healthcare quality outcomes. These reviews have identified statistical shortcomings in the published studies.

No testing for statistical significance: A study might report a reduction in, say, mean waiting times for surgery for patients after an improvement initiative. But no statistical analyses are performed to determine whether that reduction is statistically significant.

Lack of randomization: The samples examined in a study are not randomly selected, and may not be representative of the population. Therefore, the results are subject to selection bias.

Inadequate follow-up: A study might report a statistically significant change after a lean Six Sigma initiative, but there is lack of adequate follow-up. Therefore, it’s unclear whether the improvement “stuck,” or whether the initial change was simply a short-term Hawthorne effect.

Missing confidence intervals: Many studies neglect to report associated confidence intervals with their results. As a result, the level of precision is unclear, and the clinical significance of the results can’t be reliably interpreted.

No single study is a statistical slam-dunk

There’s no such thing as a perfect study. You do the best you can, with the resources you have and the constraints you face. Progress is made in increments. Conditions change. And results must be reverified and reproduced.

Still, it’s important to remember that continuous improvement can be applied not only to the process itself, but also to the methods we use to monitor and improve a process. To get the most out of quality improvement efforts in healthcare, it’s critical to be aware of the common statistical soft spots, and how to avoid them.

That way, when the wave breaks, we’ll be on sturdier ground.

About The Author

Patrick Runkel

Patrick Runkel is a statistical communication specialist at Minitab LLC. He’s spent most of his professional life teaching and writing about mathematics—from helping kids divide fractions in elementary school to helping Ph.D. researchers apply logistic regression analysis in their fields. In The Minitab Blog he likes to focus on mining and presenting statistical “gems” giving you quick practical insights about statistics.