Before building a small shed about 15 years ago, I read with great enthusiasm copious articles about how to design and build it. But every time I had the urge to build, I would lie down, let that urge pass, and read some more. I read so much about how to build it that I educated myself into inactivity. There were so many design choices and construction techniques. I knew more than I needed, but I did nothing.
Eventually, I talked to a builder who told me there was no one right way to do it. Nearly all the designs I had were fine. He told me to choose something simple that I could accomplish. I took his sage advice and finally built my shed. It looks great and still stands. The injuries—the hammered thumb, the strained back—are long healed.
The same quest for knowledge leading to inertia holds true with quality improvement (QI). There are so many tactics, techniques, and things to know: books, articles, videos, and courses on meth-ods, including a library on Lean. QI learners also can explore myriad subtopics such as choosing metrics, mapping processes, and how to talk with staff. We also have access to courses, certificates, mentoring systems, travel opportunities, and a list of consultants as long as a roll of paper towels.
QI consultants include those who know and used to do, those who know and have never done, and those who know and do. Some have experience in the laboratory domain, or at least the healthcare domain, and some are trying to painfully crosswalk their experience in construction, software development, cars, furniture, or instrument manufacturing into the lab domain.
In the best case, laboratorians’ quest for QI knowledge leads to something valuable becoming known. In the worst case, they gather only a bucket of fading jargon and the scars from a cashectomy (a common operation in which an abundance of cash is removed from a budget).
Knowing is about reading and listening and being stimulated with new ideas and exercises and a lunch break and socializing. It is fun. Knowing is a slow jog on a flat track. Doing is about struggling and collaborating and failing and arm-twisting and trading and blaming and crying and feeling inadequate. Doing is pushing a boulder up a hill. It is exhausting. Knowing takes time, doing takes back-bone.
Yet knowing is a prerequisite to doing. To carry out a QI project is to know it could have been car-ried out better, faster, and cheaper, but that will have to wait until next time. Some examples of know-ing versus doing in life and the lab are listed in Table 1.
In a previous article (1), I wrote about how to choose the right project to gain an early victory in QI. This involves choosing a problem that is hated and solvable; choosing a measurement that de-scribes that problem and making that measurement visible; using a few simple tools to understand the problem deeply; intervening to mitigate the problem; sustaining the gain; and finally, celebrating.
If you have a QI experience you would like to share that would be of interest to readers, write to me at Michael.email@example.com about your experience of knowing versus doing, and we can make these collected reflections the subject of a future piece.
Knowing Versus Doing: A Story
A composite story from multiple colleagues about the difference between knowing and doing
A clinical laboratory wanted to install an automated immunoassay analyzer to reduce the many manual methods the staff used for some autoantibody and infectious diseases testing. The manual method involved frequent pipetting—an error-prone process that caused a variety of complaints about repetitive motion injuries.
The laboratory director and manager attended educational sessions at a conference, as well as a few webinars, to learn about trends in automation and automated immunoassays. All the education sessions were fun, and some even served food.
The lab staff had a fair amount of experience with automation and putting chemistry and hematol-ogy assays on line, but had less experience with automating the kinds of assays they were moving onto this piece of equipment. As it turned out, some of these assays proved to be tricky, with a fair amount of interferences and indeterminate results. The technologists in the lab had warned about this.
The lab went ahead and purchased an instrument and migrated the assays over. Some of the techs were angry about this decision since they were experts in the manual methods, some of which required considerable manual dexterity and experience. The instrument repeatedly broke down in the first few months. Part of the problem was operator error, but some rested with the instrument. In their troubleshooting, the staff found it hard at times to separate one from the other.
At one point the lab had to issue more than 10 corrected reports in 1 week, some of which involved moving values into or out of the reference range. The lab director had to apologize to a patient who was very upset about receiving a false diagnosis.
The lab director thought about quitting, but couldn’t afford it. She also didn’t want to be defeated by a robot. Over time, the lab gradually learned the instrumentation’s quirks and worked with clinicians to better understand the nuances of how they used the results clinically. It took about a year, but the instrument became a stable workhorse.
The lab’s leadership learned: It can be hard to automate a tricky assay; it is important to listen to the voice of the customer, including bench technologists; trust the manufacturer, but verify what the company says with unbiased, real-world visits or consultations with colleagues who have similar labs; leadership is all about change and the pain of change is always underestimated.
Michael Astion, MD, PhD, editor-in-chief, CLN Patient Safety Focus, is clinical professor of laborato-ry medicine at the University of Washington department of laboratory medicine, and medical director of the department of laboratories at Seattle Children’s Hospital.+Email: michael.astion[at]seattlechildrens.org
1. Astion M. How to choose a quality improvement project. Clinical Laboratory News 2017;43:10.
2. Astion M, Hernandez J. Should I stick with this turnaround—or quit? Clinical Laboratory News 2017;43:7.