The other day, I had the honour of being a panelist on a PhD thesis defence panel. The candidate’s background was in implementation science. I must confess that I had only a vague understanding of what implementation science was, and I had no idea how it related to quality improvement (my discipline) or knowledge translation, which is another discipline that frequently gets mentioned along with QI.
This being my very first panel, I wanted to make sure I was well-prepared, so I made sure to read around everything I wasn’t familiar with (or as much of it as I could possibly fit into the time I had), and there was a ton. I learned about different implementation models, methods, and the research that supported them.
After this crash course, I think I finally figured out the subtle differences that distinguish knowledge translation, implementation science, and quality improvement. Knowledge translation deals with both the dissemination and implementation of new medical/scientific data into bedside patient care. On the dissemination side, researchers use conventional methods to determine which interventions are most effective in spreading information far, wide, and deep. On the implementation side, researchers use conventional methods to determine which interventions and methods are most effective at getting best practice to take hold.
The crux, however, is that both of these disciplines are conventional research. Philosophically, these fields aim to discover generalizable principles and create new scientific data. Practically, these fields use statistical methods that are very familiar to conventional researchers (T-tests, ANOVA etc.). KT and IS are in fact the conventional science that informs the palette of potential tools used in quality improvement projects (is audit and feedback, or intense education more effective at changing a behaviour of interest?).
Quality improvement, on the other hand, is distinct in both of these key areas. Philosophically, the aim is to achieve local proficiency and apply already existing data; general principles are secondary. Practically the QI field uses tools that conventional disciplines do not: rapid-cycle tests of change (“PDSA” cycles), and statistical process control charts. After reflecting on the subtle differences among the three disciplines, I propose that the following are what is truly unique about quality improvement:
1. Extensive local diagnostic phase: being interested primarily in achieving a local level of performance, QI work emphasizes a thorough understanding of the local problem through the use of a suite of tools including cause and effect diagrams, Pareto charts, and process mapping. While KT & IS do perform a small diagnostic phase, there isn’t the same emphasis on drilling down & systematically addressing individual root causes (for example, drilling down into the data so deeply that you can isolate a particular unit or particular practitioner that has exceptionally good or exceptionally poor performance & learning from that individual)
2. Rapid cycle tests of change: also known as Plan-Do-Study-Act (PDSA) cycles, these form the backbone of most QI projects, but are not typically present in KT/IS. While there is some iteration used in KT/IS, the timescale is much longer – weeks/months in contrast with PDSA cycles in QI, which may only be days or sometimes even hours!
3. Statistical process control: rapid cycle tests of change make large data collections impractical. As a result, conventional statistical methods are difficult to use. Statistical process control mitigates this difficulty by understanding natural variation and analyzing small sample sizes over time to achieve statistical rigor.
QI, KT, and IS are three disciplines with much overlap. They are complementary fields, each of which bring their own philosophies and methods to the united goal of improving the delivery of patient care. Knowing the strengths and the limitations of each can help practitioners design their projects with much more efficiency.