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Health Care Quality Improvement

Blog posts and articles about using data analysis and statistics in quality improvement initiatives in the health care sector.

Process validation is vital to the success of companies that manufacture drugs and biological products for people and animals. According to the FDA guidelines published by the U.S. Department of Health and Human Services: “Process validation is defined as the collection and evaluation of data, from the process design state through commercial production, which establishes scientific evidence that a... Continue Reading
October 16–22 is National Healthcare Quality Week, started by the National Association for Healthcare Quality to increase awareness of healthcare quality programs and to highlight the work of healthcare quality professionals and their influence on improved patient care outcomes. This event deserves your attention because the quality of healthcare affects every one of us, and so does the cost of... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

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The ultimate goal of most quality improvement projects is clear: reducing the number of defects, improving a response, or making a change that benefits your customers. We often want to jump right in and start gathering and analyzing data so we can solve the problems. Checking your measurement systems first, with methods like attribute agreement analysis or Gage R&R, may seem like a needless waste... Continue Reading
There may be huge potential benefits waiting in the data in your servers. These data may be used for many different purposes. Better data allows better decisions, of course. Banks, insurance firms, and telecom companies already own a large amount of data about their customers. These resources are useful for building a more personal relationship with each customer. Some organizations already use... Continue Reading
The Centers for Medicare and Medicaid Services (CMS) updated their star ratings on July 27. Turns out, the list of hospitals provide a great way to look at how easy it is to get random samples from data within Minitab. Say for example, that you wanted to look at the association between the government’s new star ratings and the safety rating scores provided by hospitalsafetyscore.org. The CMS score... Continue Reading
It's been called a "demographic watershed".  In the next 15 years alone, the worldwide population of individuals aged 65 and older is projected to increase more than 60%, from 617 million to about 1 billion.1 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... Continue Reading
By looking at the data we have about 500 cardiac patients, we've learned that easy access to the hospital and good transportation are key factors influencing participation in a rehabilitation program. Past data shows that each month, about 15 of the patients discharged after cardiac surgery do not have a car. Providing transportation to the hospital might make these patients more likely to join... Continue Reading
In part 2 of this series, we used graphs and tables to see how individual factors affected rates of patient participation in a cardiac rehabilitation program. This initial look at the data indicated that ease of access to the hospital was a very important contributor to patient participation. Given this revelation, a bus or shuttle service for people who do not have cars might be a good way to... Continue Reading
What does the eyesight of a homeless person have in common with complications from dental anesthesia?  Or with reducing side-effects from cancer? Or monitoring artificial hip implants? These are all subjects of recently published studies that use statistical analyses in Minitab to improve healthcare outcomes. And they're a good reminder  that when we improve the quality of healthcare for others, we... Continue Reading
My previous post covered the initial phases of a project to attract and retain more patients in a cardiac rehabilitation program, as described in a 2011 Quality Engineering article. A Pareto chart of the reasons enrolled patients left the program indicated that the hospital could do little to encourage participants to attend a greater number of sessions, so the team focused on increasing initial... Continue Reading
Over the past year I've been able to work with and learn from practitioners and experts who are using data analysis and Six Sigma to improve the quality of healthcare, both in terms of operational efficiency and better patient outcomes. I've been struck by how frequently a very basic analysis can lead to remarkable improvements, but some insights cannot be attained without conducting more... Continue Reading
In the first part of this series, we looked at a case study where staff at a hospital used ATP swab tests to test 8 surfaces for bacteria in 10 different hospital rooms across 5 departments. ATP measurements below 400 units pass the swab test, while measurements greater than or equal to 400 units fail the swab test and require further investigation. I offered two tips on exploring and visualizing... Continue Reading
Working with healthcare-related data often feels different than working with manufacturing data. After all, the common thread among healthcare quality improvement professionals is the motivation to preserve and improve the lives of patients. Whether collecting data on the number of patient falls, patient length-of-stay, bed unavailability, wait times, hospital acquired-infections, or readmissions,... Continue Reading
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... Continue Reading
If you want to convince someone that at least a basic understanding of statistics is an essential life skill, bring up the case of Lucia de Berk. Hers is a story that's too awful to be true—except that it is completely true. A flawed analysis irrevocably altered de Berk's life and kept her behind bars for five years, and the fact that this analysis targeted and harmed just one person makes it more... Continue Reading
At the start of a new year, I like to look for data that’s labeled 2016. While it’s not necessarily new for 2016, one of the first data sets I found was healthcare.gov’s data about qualified health and stand-alone dental plans offered through their site. Now, there’s lots of fun stuff to poke around in a data set this size—there are over 90,000 records on more than 140 variables. But to start out I... Continue Reading
People who are ill frequently need medication. But if they miss a dose, or receive the wrong medication—or even get the wrong dose of the right medication—the results can be disastrous.  So medical professionals have a lot at stake in making sure patients get the right medicine, in the right amount, at the right time. But hospitals and other medical facilities are complex systems, and mistakes do... Continue Reading
Whatever industry you're in, you're going to need to buy supplies. If you're a printer, you'll need to purchase inks, various types of printing equipment, and paper. If you're in manufacturing, you'll need to obtain parts that you don't make yourself.  But how do you know you're making the right choice when you have multiple suppliers vying to fulfill your orders?  How can you be sure you're... Continue Reading
Rare events inherently occur in all kinds of processes. In hospitals, there are medication errors, infections, patient falls, ventilator-associated pneumonias, and other rare, adverse events that cause prolonged hospital stays and increase healthcare costs.  But rare events happen in many other contexts, too. Software developers may need to track errors in lines of programming code, or a quality... Continue Reading
All processes have some variation. Some variation is natural and nothing to be concerned about. But in other cases, there is unusual variation that may need attention.  By graphing process data against an upper and a lower control limit, control charts help us distinguish natural variation from special cause variation that we need to be concerned about. If a data point falls outside the limits on... Continue Reading