I created a course on Human Factors and Patient Safety, I called PSHF 101. This course was based on my work on the Human Factors Checklist Series as part of the NICQ project. As the Sunnybrook course was delivered we developed local faculty and they have written up this development and the course's application in a new book. See Using Human Factors Engineering to Improve Patient Safety.

Several years ago I was in a workshop facilitated by Paul Plsek. Paul has a great way of cutting to the chase. In the introduction to flow diagrams he asked the question: "What percentage of your work day is spent doing real work?" He defined real work as all the things that add value to patient and families' care; when everything goes perfectly, the first time. Then what's non-real work? All the things you do that do not add value, the problems you deal with, wasted time, errors, mistakes, time you spend searching for things, bureaucratic processes... etc. In other words: waste. I was amazed at how low the percentage estimates were for real work - no higher than about 80% - many much lower. That stuck with me and I've repeated that pop-quiz many times, with similar results.

 

Of course, this is not formal rigorous study, but I believe people are never too far off with their intuition on things like this. The UK's NHS now has a program called Releasing Time to Care which "focuses on improving ward processes and environments to help nurses and therapists spend more time on patient care thereby improving safety and efficiency." Apparently this follows a period of intensive development and testing. Other examples that reinforce the need to reduce waste can be found in the emerging stories about the application of Lean to healthcare (such as ThedaCare, the Pittsburgh Way and Virginia Mason, who've turned their pioneering work into an institute).

The diagram shown here is a gross simplification, but provides the basis for our improvement opportunity: how to eliminate B, move the time saved to C or D and design and execute systems that reliably optimize time spent on A.

 

On a recent trip to Europe I was struck by how different some things are and wondered how they may actually be better... and why we seem stuck in our North American ways. A great example of this is the traffic roundabout.

When you first drive up to a roundabout you think: this is crazy, how can this be a better idea than stopping? is this really a safe idea?

I've had lots of experience with the intersection roundabout (traffic circle) from trips elsewhere and was reminded of how they really are superior to the stoplight based on experience AND key performance indicators. So why are there so many in the UK and Europe and so few in North America? I suspect a primary reason is history, not logic or evidence. Research has shown them to be safer, more energy efficient and  save travel time. (Two scholarly reports here and here and a very enlightening and entertaining presentation at TED 4:20 min video)

 

My "ah ha" experience when driving in Portugal may be similar to insights one could gain by examining a different NICU or a different clinical area. Such exposure can make you wonder why you do what you do, the way you do it. So, if one was to go on a field trip to a different land to get ideas about how to improve - where would you go? When I read this report, I thought: probably nowhere in North America. The US and Canada are 7 and 6 respectively out of seven (yes, last place) on measures of quality, safety and cost! 

 

Here's an idea. I periodically read an ad farm tour to a far-off land advertised in my local  farming journal. I put this down to some fancy kind of boondoggle - but maybe farmers really gain new knowledge by seeing the other side of the world. Another trend: eco-tourism - in which the tourist visits with keen attention to ecology, local economy and carbon footprint. So, along these lines I propose a new form of tourism: healthcare QI tours in which the tourists visit far-off lands with open eyes and ears to do some serious benchmarking for improvement. 

In this magazine article Jonah Lehrer cites the work of Kevin Dunbar and others who study how scientists study things - how they fail and succeed. As we work to raise safety and quality improvement work to scientific standards, we might do well to understand what he has learned and how it could be applied to sharpen our improvement acumen. This article is worth reading - I think you will easily see some possible pitfalls of improvement as with science. From the pitfalls come some simple messages about learning from failed improvement, modified from Lehrer's summary:

1. Check your assumptions. Does the result you observe contradict your theory? How might you change your hypothesis?
2. Seek out the ignorant. Ask someone who is unfamiliar with your improvement what think - explaining it to them will help you see it in a different light.
3. Encourage diversity. Form a diverse team around quality improvement work and value all input. Include family and non-clinical team members.
4. Beware of failure-blindness. Be careful that you do not filter the result you see with bias and preconception.

Here is a link to an eBook I just edited. I also wrote the chapter: System Safety in the NICU. This book captures the work of the Vermont Oxford Network's NICQ project.

 

Just published in Clinics in Perinatology

Abstract:

Human factors analysis (HFE) presents a formidable contribution to quality improvement (QI) in the neonatal intensive care unit (NICU). The science behind the fundamental principles concerning the design of work systems that match the needs of the people who work in them is sound and is applied widely in other safety critical situations. Early application of HFE in NICUs has shown the usefulness of these methods for frontline teams working to improve quality, reliability, and safety. The inclusion of human factors considerations in the design of structure and process has the potential to improve outcomes for patients and families and to improve the comfort and usability of work systems for providers who work in them. New technologies and continual change must be informed and designed through the application of HFE methods and principles to realize the full potential of QI.

In Switch, the authors present examples of successful big changes - like improving child nutrition in impoverished Vietnam or reducing nurse turnover but they also implore the reader to apply the same principles to their own personal, n-of-1 improvement. Improvement is a such a common human aspiration it's fitting that fundamental principles have universal application from a personal to a global scale.

In a recent article about safety and quality improvement education in medical school the authors point to a successful strategy they've used for several years. Students are given an assignment to carry out a personal improvement project that includes areas such as exercise, diet, or study habits. "Students use process diagrams, fishbone diagrams, and run and control charts; carry out PDSA cycles; and make daily measurements (miles run, colas drunk, or minutes of study) so they can link their process to outcomes." They even have a personal improvement workbook freely available. (download here)

Those of us keen on sharpening our improvement habits can do so by turning the improvement process in on ourselves. If you can internalize sound improvement method in your own changes it may bring added resolve and discipline to your NICU team's quality improvement work.

A different twist to the words of Mahatma Gandhi... "Be the change you want to see in the world."

Several years ago I attended a lecture by Kurt Vonnegut.. During the talk he used a chalkboard to graphically explain using a story arc why people have such a need for drama in their lives. Recently I stumbled upon a blog post that describes this Vonnegut lecture nicely and it occurred to me that the use of a story arc could be a valuable way to reflect on and improve improvement.

Below is an story arc based on an imaginary improvement - it's not a real graph, but rather a conceptual one on which we can consider some typical quality improvement stories. It might be possible to use this as a language to aid collaborative learning and to discover ways to improve your improvement practice.

 

First, the axes. The x-axis is elapsed time - this could be days, weeks, months or years, but typically would be in the order of months for most improvement reported in NICQ. When evaluating your improvement performance you might consider time to achieve aim (T1). What are you doing during this stage of improvement? How long does it take to get to the first real test of change? What strategies do you use to determine what to change? Or, your might ask: during T2 what happens to create A, B or C? what can we do to be sure that C does not happen?

 

The y-axis is normalized to correlate with your key outcome measure, it's not the actual measure. I've set the limits as poor and excellent, which are relative and qualitative. The important points on this axis are what I've called aim and acceptable. The aim is a milestone level of accomplishment that's the target for an improvement initiative. It's different than acceptable in that, for instance, you may agree that any infection is unacceptable and the only real goal must be zero, but for practical or motivational purposes you are willing to set an aim below this point. In other cases aim and acceptable may be the same. Acceptable may also prompt "acceptable to whom?" Good point, a parent may have a different answer than staff. This axis is also germane to how much of a stretch goal the aim provides or, once an aim has been achieved do you 'raise the bar' to set a further improvement aim.

 

Over time an improvement moves from the setting of aim through testing and making change (T1) to reach a point at which the aim is achieved. T2 is a period of stability necessary to conclude you've made improvement (special cause). Condition B represents a stable level of performance at or above the aim. Condition A is what one might call continuous quality improvement. Condition C is a regression or slip to below the aim and back to the pre-improvement level (see discussion in this article). Is condition C a function of how the improvement is made (e.g. the change relied exclusively on education) or some fundamental flaw in culture (e.g. lack of practice discipline)?

All improvement stories have an arc for which these dimensions are important and every team has a range of experience to learn from. Take the time to reflect on your stories in these terms - learning from experience to become better improvers. What makes one improvement an A and another a C? Is a B a C waiting to happen? Should all improvement be A? How can you manage the improvement you're working on now so it's more likely to be B or A? What does a current story of yours look like right now? Where are you on an improvement story arc, what shape is it?

Do you need more improvement? Does it seem like you and your team have hit the wall? Consider thinking of improvement as growth. Growth has limiting factors and sometimes more of the same will not create growth, or improvement.

A long time ago in undergrad nutrition the lab assignment was to conduct an experiment about growth in chicks. The experiment involved feeding groups of chicks rations of varying composition. One element of growth involves protein synthesis, which as you know requires amino acids. The test diet on our trial was deficient in one of the amino acids. The experiment showed that despite free access to unlimited feed, the growth rate of the test group was stunted.

The professor used an analogy known as Liebig’s law. While originally conceived to explain soil nutrient composition and the growth of plants, some such analogies are universally helpful. In the chick growth study, the limiting amino acid was the shortest barrel stave; the one that limited the rate of growth. Adding more of the other amino acids or other dietary requirements will not improve growth. You cannot fill the barrel past the limiting stave.

Complex systems exhibit similar dynamic characteristics. The discipline of systems dynamics uses archetypes or generic structures to help explain difficult challenges when concerned with system performance. Liebig’s Law is similar to the limits to success archetype. One key take away lesson from this archetype is that more of the same things you've been doing does not necessarily give you more of what you want.

 

What might be some limits to improvement? Number of people improving quality? Time? Improvement knowledge and skills? Cultural characteristics, such as an aversion to following standards?

You might apply this analogy to a specific improvement or your unit’s improvement work in general. What is your limited amino acid for improvement as growth… and what could you do about it?

 

Liebig's barrel image is in the wikipedia commons here.

While in London on vacation I made a point of seeking out all things Nightingale. Unfortunately, the museum was closed for renovations. There were two striking memorials I did visit: one at St Paul's Cathedral and the other at Waterloo Place, pictured here.

Florence Nightingale's contribution to quality improvement was nothing short of breakthrough. Two facts I learned while in London suggest a persistent Nightingale Challenge to guide improvement.

First was her work improve hygiene practices in military hospitals resulting in a dramatic reduction in the infection-related mortality rate among wounded soldiers - from 40% to 2%!

Second was her interest in making statistics visible and understandable. Apparently in order to advocate for improvement in military procedures she needed to persuade Queen Victoria and felt that a report composed of text and tables of numbers would not be convincing. The coxcomb graph (animated on this page) was an attempt to turn data into a picture that could be easily understood. This graph has not stood the test of time, and a simpler presentation of the same data is probably appropriate. The point is she recognized the need to make data understandable and convincing.

So, the Nightingale challenge is twofold: set out to accomplish dramatic improvement by setting stretch goals, think 40 to 2. And, use meaningful data that answers important questions displayed in a simple manner such as run charts annotated with change; tell a story that anyone can follow.