How One Team Used Data (Not Intuition) To Improve ED Throughput

By:  David Kashmer, MD MBA (@DavidKashmer)



Most hospitals want to improve throughput…


Have you ever worked at a hospital that wanted to improve its ED throughput?  I bet you have, because almost all do!  Here’s a story of how advanced quality tools lead a team to find at least one element that added 20 minutes to almost every ED stay…


Once upon a time…


At one hospital where I worked, a problem with admission delays in the emergency department led us far astray when we tried to solve it intuitively. In fact, we made the situation worse. Patients were spending too much time in the emergency room after the decision to admit them was made. There was a lot of consternation about why it took so long and why we were routinely running over the hospital guidelines for admission. We had a lot of case-by-case discussion, trying to pinpoint where the bottleneck was. Finally, we decided to stop discussing and start gathering data.


Follow a patient through the value stream…


We did a prospective study and had one of the residents walk through the system. The observer watched each step in the system after the team mapped out exactly what the system was.  What we discovered was that a twenty-minute computer delay was built into the process for almost every patient that came through the ED.

The doctor would get into the computer system and admit the patient, but the software took twenty minutes to tell the patient-transport staff that it was time to wheel the patient upstairs. That was a completely unexpected answer. We had been sitting around in meetings trying to figure out why the admission process took too long. We were saying things like, “This particular doctor didn’t make a decision in a timely fashion.” Sometimes that was actually true, but not always. It took using statistical tools and a walk through the process to understand at least one hidden fact that cost almost every patient 20 minutes of waiting time.  It’s amazing how much improvement you can see when you let the data (not just your gut) guide process improvement.


The issue is not personal


We went to the information-technology (IT) people and showed them the data. We asked what we could do to help them fix the problem. By taking this approach, instead of blaming them for creating the problem, we turned them into stakeholders. They were able to fix the software issue, and we were able to shave twenty minutes off most patients’ times in the ER. Looking back, we should probably have involved the IT department from the start!


Significant decrease in median wait time and variance of wait times


Fascinatingly, not only did the median time until admission decrease, but the variation in times decreased too.  (We made several changes to the system, all based on the stakeholders’ suggestions.) In the end, we had a much higher quality system on our hands…all thanks to DMAIC and the data…


Excerpt originally published as part of Volume to Value:  Proven Methods for Achieving High Quality in Healthcare


Here’s How Bad Data Affects Your Bottom Line



By:  David Kashmer, MD MBA MBB (@DavidKashmer)

LinkedIn Profile here.


Hello, and welcome to the Healthcare Quality Podcast.  My name is David Kashmer and my background is as a Lean Six Sigma Master Black Belt.  I am also a surgeon and an MBA, and my passion is data; data collection and using data to improve quality in healthcare.  Today I wanted to talk with you about data fidelity and certain thoughts on data quality.


40% Of Your Company’s Data Is Probably Inacurate


It turns out when we look at data by the numbers, approximately 40% of all company data is found to be inaccurate.  This is by halo business intelligence as seen on Data Science Central.  About 92% of businesses admit that their contact data is not accurate and about 66% of organizations believe they are negatively affected by inaccurate data.


Seems Like A Bigger Problem In Healthcare


In healthcare, experientially, our numbers are much higher.  Routinely when we go to pull charts and data, well, data fidelity and validation is a big problem.  One of the questions you may ask yourself is, now that we’re looking at just how good our data are, does this impact our businesses bottom line?  How does this impact our business, whether we’re a hospital or some other aspect of industry?  Well, it turns out there is evidence about what happens with a data fidelity or data quality initiative per  There’s a link to this article at our blog, and that’s  If you go there, you’ll find more information on data quality under an entry called 17,000 men are pregnant.  We’ll get to more on that in just a moment.


Again, it turns out that data quality initiatives show large changes in business end points, including 10-20% reduction in corporate budget, about a 40-50% reduction in IT budget, and 40% reduction in operating costs.  Increases are typically seen according to Data Science Central in both revenue and sales for industries where those end points are more applicable.


Dirty data can be damaging.  For example, the title for that blog, 17,000 men are pregnant, comes to us from the fact that due to incorrectly entered medical codes in certain British hospitals, thousands of men appear pregnant and seem to require obstetric and prenatal exams.  Those errors caused disastrous results in billing claims and compliance per the century at  So, there are a lot of factors that go into quality data, and having quality data, recording it and making it accessible and useful.  Again, experientially, this is something we teach and talk about when we talk about the use of statistical process control in our hospitals.


How Do We Fix This Problem?


Let me tell you how we do that.  One of the main focuses when we teach and work in quality in hospitals is to get data directly from the process.  When I say directly from the process, I don’t mean get data from a data warehouse the next day or query or registry for data.  Those things are all typically what we do.  We have registries and it’s great to have them, but it turns out it’s a lot more valuable to go to where the process is occurring and collect continuous or discrete data, depending on how you’ve set up your particular data collection plan, but to go right to the process and to collect those end points.  For more information about continuous versus discrete data, you can visit us on the blog,, and also where we talk about discreet versus continuous data.  The point here is, whichever data end point you use, focus on getting the data directly from the process at the point the process is occurring and doing so in a prospective way is key.  This is because we see that by the time data leaves the process, gets into the registry that you’re using, a lot of different things happen.


First, the operational definition for the end point you want to look at, the one that has meaning for your quality improvement project, doesn’t always line up with what the registry asks or wants.  So, because we know so well that often definitions, often fields that need to be entered, often those things don’t line up.  We focus on going right to the process and collecting data.  Now, there are a lot of challenges in that and one of them is resources.  Typically, what we hear in hospitals I’ve helped out with, hospitals where I’ve worked in the past, one of the things is, “boy, staffing to collect data is very challenging”.  It’s really just not valued.  Hospital staffing, a great amount of the costs to run a hospital comes from labour.


If you agree that approximately 60% of hospital costs are labour costs, and that’s broadly speaking what it is across organizations, it’s very challenging to make the argument for why you should have an FTE (full time employee) or a part time employee go to the place where the process is occurring and collect data.  It’s hard to make that argument, but I think you’ll understand based on the numbers we just shared about how data fidelity and poor data impacts our business end points.  I think you can agree now that it’s very worthwhile to have the best data you can.  If your decisions are based on data and you run a very data driven shop, you can probably intuit that its key that the data we use are accurate.  So, if you think that it’s too expensive to collect good data, well you’re likely incurring the costs and expense of not having good data and that tends to be much more significant than you anticipate.


Again, as noticed that when data quality projects are done, projects that focus on the quality of what we put into our registries or what comes to us in a timely fashion to make decisions, well in those projects we see again reductions in corporate budget of 10-20%, IT budget reductions, operating cost reductions, and we see increases in revenue.


So, for today’s entry, I wanted us to talk just a little bit about data fidelity and how it impacts our bottom line.  Again, a lot of what we talked about can be found on and there is a link to this article.  You can find this article with the link at, and we have a little gloss on it and then a link to the article.


Again, if you think it’s too expensive to collect good data, well you should try not collecting good data because that’s a lot more expensive.  So, again, in summary, we just wanted to highlight for you all today some of the really dramatic costs associated with bad data.  Again, our advice and my advice from having done many quality improvement projects over the years, and a typical teaching in healthcare quality improvement projects is go right to the place of the process you’ve teased out.  Go right to it, take the stopwatch, clipboard or what have you, and take a look at it.  Take a look and collect your data right from the process.  It won’t be as cleaned as the registry may make it, it won’t be fraught with the challenges of taking the operational definition that you want to look it and somehow shoehorning that into what the registry wants.


So, good luck with your data collection and your quality improvement projects, and if you have any questions or stories about the use of data in your healthcare system, whether it be a success story, a question about how to have a success story, or a warning for other data users out there, feel free to visit us at and share your experiences.  We are always happy to hear.  Have a great day!




The Use Of Social Media In Surgery–Podcast

By:  David M. Kashmer (@DavidKashmer) & Vivienne Neale (@SupposeIAm)


Click here to listen to the podcast.


DDD, bringing you the metrics behind the data. Here is your host, Vivienne Neale.

Hi, and welcome to DDD, which is Data Driven Decision Radio, episode five. My name is Vivienne Neale and I am delighted to be back with you. For those who have asked, my background is in education, training, broadcasting and social media. Of course, I am also a sometime patient curious to know what decisions are being taken in my name that might just affect me and thousands of people just like me. So, once again I am joined by David Kashmer, Chair of Surgery at Signature Healthcare. David is an expert in statistical process control, including Lean and Six Sigma. He has a specialist interest in new tools to improve healthcare, like gamification. David also edits and writes for a blog called So, hi David and welcome back.


Vivienne it’s great to be with you again today and I hope the weather in England is just as nice as it is here on this side of the pond.


Well, I have to say, yes it is. It’s a beautiful sunny afternoon, so we are very grateful for that, but the evenings are now getting just a wee bit cool and certainly we are in autumn, or should I say fall?


Well, it’s great to be back and what do you have to share with us from the news today?


DDD News…

Well, actually just a quick thing. Some of you might be aware of our export, which is Jamie Oliver who is a chef, who a few years ago, probably four or five now, was campaigning for better school meals and he did that in the UK and also in the US. He has just made another documentary where he has been showing the impact of the amount of sugar we are all eating in the UK. I assume that probably in the US its worse than where we are here, but he did a fairly shock jock type of documentary where he said although we are not recommended to have more than six or seven teaspoons full of sugar a day, most of us are eating between 30-50 with all the unknown sugars, or hidden sugars, that we have in various processed foods. So, he came up with a rather startling statistic that around 130 limbs a week are amputated in surgery by people with type 2 diabetes, which was a really, really shocking thing, and he explored what that means for our national health service. So, that’s what we’ve been looking at here. What about you?


Well, Vivienne, having been through the United States school system I can easily support improved meals and better food.




I also have to agree that it is not surprising that we would consume so much sugar beyond what’s recommended in various ways that we can’t even appreciate. So, I have an easy time believing that that’s our current rate of sugar consumption and it’s easy to get behind the idea that nutrition is a key thing and likely needs improvement in our diet as well. So, I appreciate the news, and I have a piece of news from the last week.




This is an update from last week’s show, and a bit of a correction. We were discussing new prosthetic limbs with 3D printing and you brought up an interesting question about a product called Ninja Flex. After reading the article briefly from it looked as if Ninja Flex was the neoprene sleeve used to fit a prosthetic or some variation of a similar sleeve. Well, it turns out that with further research Ninja Flex is one of the new 3D printing filaments on the market. The filaments are the different type of substrate that come to us on a spool that our printers use to build whatever it is we are looking to make. So, to the creators of Ninja Flex and to clarify for our listeners, Ninja Flex is an innovative product that allows for a flexible construction. So, whatever you build has some flex and give to it, unlike many of the other 3D printable filaments, like ABS and PLA that are very firm and rigid by comparison. So, to the creators and users of Ninja Flex, I am glad we have some clarity now and it seems like a great opportunity for prosthetics in the future.


Right. Let’s hope we don’t need too many. Of course, if anyone is interested in Jamie’s new ideas, he’s got a hashtag which is #JamiesSugarRush, and if you check on Twitter you’ll be able to see how much conversation is being generated by his particular documentary. He finally made the decision that like Mexico, who have put a tax on fizzy drinks, he is also going to impose a tax within all of his restaurant empire to start funding research and so on and so forth.


How interesting. Very interesting.




We are DDD. Data Driven Radio.


So, anyway, that was one aspect of the news. I’ve also been thinking about the number of data points being generated every day and I know that you are very much at the forefront of this research. So, I’m just thinking, well if you’ve got billions of data points being generated, surely some healthcare providers may well actually feel overwhelmed by all that’s being generated. I guess you could end up wondering just what to do with big data, and I think that’s quite understandable, don’t you?


Oh, very much so, Vivienne. With the incredible amount of data points that are out there, it is easy to contract what we often term ‘analysis paralysis’. Whether we have the inability to draw meaningful conclusions from the data or really just freeze in the face of it all. That’s a well-known phenomenon, as you described.


Yeah. So, I suppose the whole idea that data can be difficult to manage, but of course we’ve got that dilemma that is absolutely impossible to ignore, not that we should of course. What happens if you do feel your department’s information isn’t being represented or you are wondering where to go next? Do you have any experience of situations like this?


Vivienne, in line with what we spoke about briefly last week, I do. The challenges are many, just as you say, owing to the amount of data that’s out there. What I’ve found useful, along with many others in my field, is to make sure you have a clear vision for strategy first. Where you want things to go, the type of care you want to deliver and where your focus is. Having that clarity allows you to know what type of data you are looking for and the questions that you can pose in the face of these huge data sets. So, I think with more data than ever I have seen data represented poorly, I have seen data that asks questions that really don’t have a lot of meaning, like the classic how many angels can dance on the heads of a pin? Interesting question, but probably not very applicable to what we do every day. So, it really refocuses us on strategy. When we see data that asks questions that aren’t necessary of gives answers that are either Archaean or really just not useful in any way. So, yes, I have seen it, I have lived it and avoiding it starts with having a strategy for your department and team.


I would say that it’s not even a strategy, but not wanting to split hairs here, but like a philosophy. I think that the whole department has to believe in what they are trying to do and that data isn’t literally, and this is not a joke, an add-on, but is actually part and parcel of everything that’s being done and the conversations and being made and the changes that you want to see are being driven by this.


Well, you’ve said it… I think you stated it much more succinctly than I did. You’re right. It’s how to make data an everyday part of improvement for your system and how to have a strategy to deal with it and the questions it can ask. I think you’ve stated it really nicely. The team has to understand that effective use of data is part of what we do on a daily basis. So, really well said.


Well, thank you, but I’m going to throw a curve ball at you now. As I am not a mathematician and I’m not a data analyst or data scientist, I actually find that my background in literature studies has put me in a really good place to start asking the stories beneath the stories generated by data. I find that I am creating a narrative which is really quite different from people who are used to analysing data in that conventional, almost mathematical way. So, I think that actually the quality of the insights that are being delivered, you have to really think about that, and what sort of insights have been delivered by your department when using big data, and do you ever bring in anybody that’s not medical to help you out? Just a question.


We do, especially in those sections that use data according to a standard required by whatever governing body. What I mean by that is, in the United States the American College of Surgeons plays a strong role in trauma care and it mandates that a certain registry be kept with certain types of data. It is pretty rare in healthcare Vivienne that we have people who know how to build models out of data and ask questions in certain ways, and we’ve talked about that in other episodes. The challenges of extracting a meaning from your data. So, yes, I have used other Six Sigma black belts for projects and other people who have deep knowledge of what questions you can ask data and its limitations. I really like what you said about using your literature background to extract meaning from data, it’s a well-known technique to use a story to highlight data to make it more human. So, for example, if we have data that says we are not being as responsive as we are to the emergency department, because we tracked all our response times and we saw the distribution of them, and there was a really large variation and that creates defects where we don’t show up in a timely fashion for our patients, however we may define timely. Then, we tell a story. We say, “Well, this patient coming into the system, Miss Smith, is likely to experience this based on our data”, where if we have a Miss Smith who did experience a certain finding, we use that to highlight our data. So, I really like your take on humanising the data by telling stories that line up with it. I’ll share with you that doing the reverse, using a story and saying, “Well, this is how it goes”, and driving change with one story is actually not always as effective. The reason why is there’s such a distribution of times, if we don’t know that distribution, Vivienne, we don’t know our performance rigorously. We may pick a story where we did unusually well and we may extrapolate from that that, boy, we do a great job, but in fact that one person, that story that we picked does not indicate our true performance. I’ve seen that very frequently with process improvement. So, I really like your idea of looking at the data and humanising it with a story rather than going right for the story not having a sense of the systems performance. That hasn’t been as effective in my experience.


In fact, there are different types of data, aren’t there? Like structured, unstructured, semi-structured, and internal and external data. I quite like… I don’t know how much you make use of, and maybe you don’t, of external data, which is that which is created or generated outside of your department that you don’t own or currently have access to. Some external data is free to access and some is not. Do you ever factor that into any of your analysis or not?


We do. We use multiple data sources that are external to our department, including what is called NSQIP, which is another body of data from the American College of Surgeons, it is pay to play and it benchmarks you versus other centres in some anonymous fashion. The centres tend to be anonymised that you look against, but Vivienne, I will share with you that there are challenges to benchmarking. Meaning the definition of what may be called a wound infection or how other centres come by the ability to tag something as a wound infection. It may be different than what you call a wound infection. I use would infections, but in fact every end point that you’re looking at may have a slightly different operational definition or the data may be cleaned better or worse by one centre than it is at another. So, I’ll share with you that the process improvement take on it, at least the way I was educated is to rigorously improve your system with data and before you benchmark, make sure you have an operational definition of what you’re looking at, which matches up with eventually how you are going to benchmark. The bottom line is, don’t benchmark it first. You probably have a long way to go, and once you’ve gone down that path to improvement, then look to those external benchmarks and external sources of data. It’s a different take on it than some others, but it’s been very valuable.


If you are saying that actually some people are not naming things correctly, should there be… or consistently rather than correctly, do you think there should be some state or national take on this that everybody does things in a similar way, or is that inappropriate?


Well, there often is a national or a CDC guideline or a definition mandated by the College of Surgeons. Let me explain a little further. For example, pretend I work at a centre and this is not the centre I currently work at. I want to be clear that this is a fairy tale, but pretend I work at a centre that seems to have a very increased risk of wound infections among wounds that are clean. It has… boy, it just seems like too many wound infections in clean wounds. Well, believe it or not, in the United States, one of the most common reasons or difficulties we have with labelling things as wound infections in clean wounds is that different raters, different people do not get the same classification of wound when they look at it. In other words, when they use the system of wound classes 1, 2, 3 and 4, the typical way wounds are classified to extrapolate risk of wound infection, they don’t call the same things class 1. One place may call the wound a class 1 and one may say it’s a class 2. That completely changes the expected outcome or the probability of a wound infection. In other words, Vivienne, sometimes certain measurement systems are limited. The wound classification system, 1, 2, 3 and 4, has an interrater reliability which is not perfect, meaning it’s not always reliable that two people using the same tool come to the same conclusion. Now, why is that? it’s a little beyond our scope, but I’ll share that it can be because if you have a surgeon reopen a wound, some centres would say, “Well, that wound is now a class X”, and another centre may say, “Well, that’s a class Y”, and it’s because different descriptions of the wound classification system don’t make it really clear what that would be. Now, my bottom line here Vivienne is, it may not be that another centre is counting things wrong or poorly. It’s not always that clear. It just can be that your centre counts things differently or somehow improperly. That’s why, again, I stress you have to make sure your centre is as improved as it can get by looking internally. Fix what you can fix and then go outside and benchmark against other centres. I’ve found when you do that it really gets you a lot further. You’ve built the infrastructure for how to improve, you have the data collection on what it means and you have a rigorous way to do better and achieve the lowest defect rate you can, and then you benchmark. Usually, when you wind up doing that, you find out you’re doing pretty well.


So, that moves me onto the notion of the social media use in surgery. So, if you were actually tweeting what you were doing and maybe it was something to do with wound infection, for example, does that mean that people will end up starting to ask questions about the nomenclature, to use that word, or the naming of particular… whatever you were talking about, about the type of wound, classification X or what have you. So, for example, if you were tweeting then perhaps that would actually open up a dialogue.


Fascinating question, and I am all for us, as patients, knowing what we’re getting into as much as we can. It’s a tough balance though between friction, blocking physicians from doing what they need to do, and respecting the years of education they have in this. Yet balancing that with the fact that we have a right to know about our bodies and what’s happening to us. So, it’s interesting. When I look at this like other professions, it’s rare that, for example, I would see a lawyer tweet about how they’re looking at ipsa res loquitur or a legal doctrine as they’re considering my case. There is a boundary between where it becomes obstructive versus useful. Now, they have to get done what they get done as lawyers, similarly physicians and surgeons have to get done what they have to get done. Yet I think in very select circumstances tweeting and the use of social media can be very valuable. So, I don’t have an easy answer on that one, but I think in general, conversation about what’s happening to our bodies as patients is a good thing.  One option is to automate tweets ahead of time so that they are put out at regular intervals.


It would be interesting to see what other people would feel about that. I mean, there would be possibly issues of privacy as well, wouldn’t there?


Absolutely. When you do tweet something, like live tweeting from a surgical procedure or something similar, you have to, in the United States, respect the HIPAA laws, or health information portability and accountability act. You need to be very careful with how you do it regarding protected health information.


Yeah, but it’s interesting because I know sometime, about three years ago, you had written about the fact that one in four physicians was using social media daily, and I assume that has gone up considerably since that time. So, these and other data indicate that twitter is one of the least used platforms by physicians, and at that time, three years ago, it was 7% of total use. Do you think that’s gone up much since then?


Experientially it seems to have. I’ve seen more physicians use twitter on a daily basis and even health systems allow tweeting from the OR in select circumstances. So, I do think that it’s becoming more useful and although I don’t have the hard data, I would estimate that that 7% has increased. There is even data regarding now the factors that influence the adoption and meaningful use of social media by physicians. So, people really have looked to tease out what is making us use it more or less and using it with meaning. There are these external factors such as how useful we think it is and how easy it is to use and then other drivers for our behaviour. In fact, there is an entire journal now dedicated to looking at things like this. So, really fascinating.


More specific data on social media use in Surgery here.


Yes. So, thinking about it, given the prevalence of social media usage, where do surgeons fit into the fold, do they?


They do. Despite that 7% usage that we talked about, tweeting from the OR happens every day and during cases, both laparoscopic and open procedures, surgeons do use twitter to highlight, educate and advertise for their programmes and how well they’re doing. Rex Healthcare, for example, in Raleigh, North Carolina, did join the growing roster of hospitals that have experimented with live tweeting. This is also being done in academic medical centres and beyond.


So, what do you personally see as the major benefits of this development?


Well, currently surgeons blog and tweet routinely, as we said, and they do that about the challenges in modern day surgery. They also share useful professional facts and opinions. Some of the business influencers in our field can add a lot to our practice and our approach. They can tweet out different things coming with the International Classification of Diseases version 10, ICD10, and other similar things. So, it’s this constant stream of ideas and an exchange of ideas across the county. So, in any event, tweeting, blogging and use of social media is now common, which represents a huge shift in my understanding of use of social media. Very different than when I trained, at which time we avoided things like that at all cost.


Yeah. I think in many ways it’s quite interesting, even in teaching. Teachers were encouraged not to be part of social media at all, but in fact without being part of this new phenomenon you can’t possibly understand it. I think it’s essential that we do make use of it, but each profession appropriates social media to fit their particular needs.


Well said. As you’ve mentioned before when we’ve talked offline, social media content is not all upside. Things that take just a moment to tweet can last a lifetime and have serious implications for professionals. Also, professional advice given out online can take on a life of its own. So, there are several strategies to mitigate this, including attempting to make it more than one click for professionals who want to ‘tweet angry’ or ‘blog angry’ or say something otherwise inflammatory. There is a real focus on putting useful content out there that staff in similar circumstances around the country can focus on. So, as you say, social media is not all upside, it can be very useful, but if used improperly, highly inflammatory and almost like an online permanent record of what we’ve done and it’s something we really need to be careful with.


So, you are saying that there are still many potholes you need to negotiate.


Well said.


Well, that’s that. We’ve sorted it. It would be really interesting to talk to physicians and surgeons that make innovative use of social media at times of surgery or pre or post-surgical experiences. It would be lovely to add yet another voice to this DDD podcast.


Well, Vivienne, I think in the next coming weeks we’ll have some colleagues on who have tweeted from the OR or have seen social media used to great effect from the hospital or operative side. So, watch for that coming in the future. I think we’ll have several of our colleagues on here soon. I would also add that one of the useful things to avoid those common mistakes with social media are guidelines written both here, by the American Medical Association, and abroad, about responsible use of social media. So, as I think it would be said on your side of the world, do have a look in on those useful guidelines for physicians and other professionals as our listeners begin to use social media more and more.


Well, thank you very much, David. Certainly I think we’ll have a lot to consider over the next seven days.


I think so.


Okay. Thank you. I hope you’ve enjoyed today’s episode and if you want to keep up to date with David Kashmer’s approach to quality and statistical process control, business model innovation and critical practice, do join us for the next programme. In fact, we are very interested to hear what innovative practices are being undertaken in your health provision. If you’d like to appear on the show, contact us through our website. We are looking forward to hearing from you. Meanwhile, if you’ve liked the show, do leave us a rating on ITunes. It’s one way we can ensure the word is spread. We look forward to being with you next time. Bye for now.


We are DDD, Data Driven Radio. Catch us on Soundcloud and iTunes.

What You Should Know About Gamification In Healthcare

By:  David Kashmer, MD MBA FACS (@DavidKashmer)


Why bother with gamification?


Did you know there is an engagement crisis on in America? It turns out that more than $500,000,000 in revenue are lost every year to the fact that employees are simply not engaged with their jobs (1). In fact, Gallup reports that 70% of American workers are either disconnected (emotionally) from their work or are actively seeking to hurt their company (1).


This crisis extends across America, and the worst part is it is insidious. The fact that employees often aren’t engaged leads to all sorts of missed opportunities, exaggerated costs, and unrealized revenues…and it does it so slowly. It happens in ways that are difficult to perceive. Gamification is one potential solution to the employee engagement crisis and it’s one that’s worked very well for me.  Let me share some stories and thoughts about gamification as I’ve applied it in healthcare.


A story


Once upon a time a section of Surgery was attempting to engage residents in a dramatic culture change. There were certain critically ill patients and administrative issues that were unrealized, and didn’t translate easily to the residents or daily work at the front lines of medicine.


There were different philosophies of care circulating in the department, and it was challenging to get through to a culture that had been in place for some time. The solution the team used? Gamification.


Deploying a comprehensive gamified system facilitated culture change and increased the rate of improvement for the section substantially. The resident (and attending) staff involved experienced increased job satisfaction as measured by a standardized tool.  Look here for more information.


Here is a little bit about gamification to let you know what the team did and how it succeeded.


Gamification is more than just points, badges and leader boards


You may already know that gamification is the use of game dynamics, techniques, and themes to improve staff engagement. One of the most commonly used techniques includes points, badges, and leader boards.


Points are awarded to participants for certain actions according to what the designers feel is important. Similarly, badges highlight special achievements and levels reached by the participants. These external signs of “leveling up” assist in providing social proof for culture change and reinforce aspects of compliance. A leader board uses peer benchmarking and peer motivation to help participants understand where they are relative to others in their group. Gamification, however, is much more than just these points, badges and leader boards (so called “PBLs”).


Although PBLs may be some of the external signs of gamification, there are other important techniques that can be utilized. One, for example, is often called the appointment dynamic. The appointment dynamic is the idea of giving positive reinforcement to the participant for returning to the same spot, location, or scheduled event at a certain time. (Works well to reinforce morning sign-out!) Rewarding this behavior with points can be an important dynamic to help culture change and improve such as improving the function of a healthcare department.


Another technique for gamification is unlocking new skills. When the participants “level up” this can be interpreted as achieving competency to a certain level within the system. This leveling up and unlocking new techniques can allow participants to unlock new abilities. It is a much more intriguing way to achieve competency-based training.


For example, in the story of the surgery department above, participants gained a new skill when they achieved certain point levels. For example, surgical residents gained the ability to examine and clear the cervical spine for injury in trauma patients. They achieved a certain level of points, took a brief test and interview, and were validated to clear cervical spines which is a very important skill in trauma and emergency surgery


Gamification uses powerful themes and motivators to engage


Did you know that the millennial generation is a larger bulge in the population plot than the baby boomers?  There are many more Millennials around than boomers, and it’s a fact that there are many more Millennials currently than other segments in the United States. These Millennials gravitate towards clear social interactions that can evolve from techniques like using game dynamics. As we mentioned above, gamification uses peer benchmarking, and indirectly positive peer pressure, to achieve excellent results.  It seems to resonate especially with generations other than the ones who are typically in admin positions nowadays.  In other words, the technique is for them not for us.


That can make it tough to understand for administrators, but no less effective for the staff who execute the organizational goals at the front lines.


Can be inexpensive to deploy


A gamification system does not need to cost tens of thousands of dollars to deploy at your hospital or business. Techniques like the gamification model canvas allow you to design a comprehensive game that can work well for your system. Look here.


Questions about gamification or how to deploy it at your center?  Send me an email at because I’m always happy to help.


Questions or comments about gamification particularly that applies to health care? You may wonder how staff react to the term gamification. You may wonder how we use leader boards and specifically how everyone reacts to having their name on a visible point tally. I’m happy to share these and other specifics for how we’ve successfully employed gamification in healthcare settings. Particularly, it’s good to share how gamification has improved job satisfaction among participants in statistically significant ways.



(1) Ed O’Boyle and Jim Harter, State of the American Workplace (Gallup, 2013),


4 Types of Bad Metrics Seen In Healthcare


By:  DM Kashmer MD MBA MBB FACS (@DavidKashmer)


Sometimes, you can see the train coming but can’t get out of the way fast enough.  Whack!  The train gets you despite your best efforts.  Wouldn’t have been great to start to get out of the way earlier?  In this entry, let’s focus on how to identify, as early as possible, four types of bad metrics in healthcare so that we can run away from that particular train as early as possible.  After all, the sooner we flee from these bad actors the more likely we are to avoid being run over by them.


Truth is, you’ve probably seen the train of bad metrics before.  After all, you know that all sorts of things are getting measured in our field nowadays and, sometimes, certain endpoints don’t feel particularly helpful and (in fact) seem to make things a lot worse.


First, a disclaimer:  this entry does not argue with metrics that the government mandates. There are some things that we measure because we have to for reimbursement or other reasons. However, if you believe (like me and other quality professionals) that a focus on reducing defects eventually impacts all sorts of quality measures (even mandated ones), then this is the entry for you!  This work does not focus on arguing or pushing back against those things that we must measure owing to regulation.  Now, on with the show…


Let’s explore four broad categories of bad metrics and how to avoid them.


#1 Metrics for which you cannot collect accurate or complete data.


It can be very challenging, in hospitals, to collect data. Often, data collection is frowned upon, or is even thought of as an afterthought or imposition.  So, as we launch in here, remember:  saying that you can’t collect complete or accurate data is not the same as actually being unable to.


Colleagues, listen:  if you think you can’t afford the time to collect good data, let me tell you that you can’t afford not to collect and use data.


When I’m working with a team that’s new to Lean or Six Sigma and we discuss data collection, the team often balks and focuses on the fact that no one is available to measure data, that we don’t have data collection resources or that, even if we had resources, we can’t get data.


I usually start with a quote:  “If you think it’s tough to get data, remember how tough it is to not get data.” (Split infinitive included for drama’s sake.)


Then we go on to explore together how there are several techniques we can use to make gathering data much easier so that we can avoid the “easy out” of “we can’t collect data about this and so it’s not a useful metric”.  In fact, most projects we do require data collection for 1-2 seconds per patient at most.  And that’s for prospective data collection.  (Want more info about how to make data collection easy, email me at and I’ll pass it along.)


However, in healthcare, we have all seen projects where data collection is arduous and so we react against data collection when we hear about it.


Sometimes, teams focus on using retrospective data. Of course, using retrospective data is much better than using no data. However, retrospective data has often been cleaned via editing or in some other way that makes it less valuable. Raw data that focuses on the specific operational definition of what you’re looking at tends to have the most value.


Sometimes, you have no way to measure a certain metric or concept and yet the team believes that concept to be very valuable. Take, for instance, a team that focused on scheduling patients for the operating room. The team felt that many patients were not prepared adequately before coming to the holding room. This included all sorts of ideas such as not having consent on the chart or some other issue. The team decided to measure this prospectively and found that only about one third of patients were completely prepared by the time they came to the pre-operative holding area. This was measured prospectively with a discrete data check sheet.


Let me explain that, sometimes, the fact that something hasn’t been measured previously means that the organization has not had that concept on its radar previously. This goes back to the old statement that if it is measured it will be managed and its corollary that if an endpoint is not measured, it is very hard to manage that endpoint.


To wrap this one up:  it is important to mention that one category of bad data or a bad metric is a metric that you cannot measure. However, it is important to realise that just because you haven’t measured it before doesn’t mean that you absolutely cannot measure it. Sometimes, if the idea or concept is important enough, you should develop a measure for it. We discuss how to develop a new end point in the entry here. That said, if it is absolutely impossible or arduous to collect accurate or complete data, the metric is much less likely to have value…but don’t just let yourself off the hook!  If you think something is important to measure, learn that there are ways to collect data that require only four or five seconds per patient!


#2 Metrics that are complex and difficult to explain to others.


If a metric gives a result that people can’t feel or conceptualize it’s just plain less valuable. Take, for example, a metric for OR readiness. In the month of April the operating room scored a very clear score on this metric. That score was “pumpkin”.


“Pumpkin?!”…Well, pumpkin doesn’t mean much to us in terms of operating room readiness. For that reason, you may want to measure your OR preparedness with a different metric than the pumpkin. Complex and difficult metrics that lack tangible meaning should be avoided.  Chose something that tells a story or evokes an emotion.  One upon a time, a center created (and validated) a “Hair On Fire Index” to indicate the level of emergent problems and crazy situations the operating room staff encountered in a day to indicate how stressed the OR staff was that day.  Wonder how they did it?  Look here.


#3 Metrics that complicate operations and create excessive overhead.


This type of metric is especially problematic. If a metric is difficult to measure and requires an incredible level of structure / workload to create it, it may not be useful.


Imagine, for example, a metric to predict sepsis that requires a twelve part scoring system, multiple regression, and the computing power of IBM’s Watson. This may not be a useful day to day metric for quality or outcome. Metrics that complicate operations and create excessive difficulty should be avoided.  When you see that type of metric coming, jump out of the way of the train.


#4 Metrics that cause employees to ‘make their numbers’.


This is similar to problem metric number two. When staff can’t feel the metrics that we describe, or see how they affect patient care, it can be very hard to mentally link what we do every day to our quality levels. That can lead to situations where employees are acting just to ‘make their numbers’. That type of focus is difficult and makes metrics less useful.


It’s important to have metrics that we perceive as having a tangible relationship to patients and their outcomes. We are so busy in healthcare that often if staff can fudge a metric, complete a form just to say it’s done, or in some other way ‘make numbers’, well, we often see that’s what happens. (That effect may not just be confined to healthcare of course!) It can be very challenging to create a metric that very clearly indicates what we have to do (and should be doing) rather than one that is sort of an abstract number we ‘have to hit’.


Take Aways, Or How To Avoid Being Hit By The Train Of Bad Metrics

In conclusion, there are at least four types of bad metrics and very clear ways to avoid them. Take a moment to try to see these trains coming from as far away in the distance as possible so that you can quickly get off the tracks unscathed.


We need metrics that we can feel and that tell a story of our patient care. We need ones that, whether government mandated or not, seem to relate to what we do everyday. We need ones that are easily gathered and tell the story of our performance clearly to both us as practitioners and staff who review us. Sometimes, we are mandated to collect certain end points yet, over time, I have come to find that when we do a good job with metrics that have meaning, we often have less defects and see better outcomes in all the metrics…whether we are mandated to collect a particular metric or not.


As part of your next quality project and how you participate in the healthcare system, take a minute to focus on whether the metrics you’re using are useful and, if not, how you can make them better.  Be the first to sound the alarm if you see the train of bad metrics on the track to derail meaningful improvement for our patients.

Have You Ever Used Stealth Sigma?

By:  DM Kashmer MD MBA MBB FACS (@DavidKashmer)


You took a job at a place where they don’t use Six Sigma…now what?


Ut-oh…you’ve entered an organization and it’s one that doesn’t use Six Sigma…but that’s one of your favorite toolsets!  Maybe you use Lean techniques and Six Sigma ones, routinely, together.  After all, you know very well that the Six Sigma process is really just a collection of effective tools put together in the best manner to achieve great outcomes. You know that it’s not so much that Six Sigma is the only way to get things done; however, you know it’s a highly effective process and that it complements Lean so well. Again, the problem is, your organization doesn’t use Six Sigma and maybe even says “We don’t do that here”.  It could be that the new place uses Lean by itself or some other process, which, of course, is infinitely better than having no process at all!

Have you ever been in that situation or heard about it? If you have, then read on for some advice about how to operate with the tools of Six Sigma effectively in an organization that “doesn’t use Six Sigma”.

BIG DISCLAIMER:  By the way…Lean, TQM, and other toolsets are, in fact, great!  Each has an important role in reducing waste, improving quality, and focusing on patient safety.  The question here is:  how do you use the Six Sigma toolset that compliments Lean and other tools so very well in an organization that hasn’t seen those Six Sigma tools before?  After all, Lean and Six Sigma are like peanut butter and jelly…


Don’t call what you’re doing Six Sigma.


I have learned this from entering several organizations that outright say “we don’t use the Six Sigma process”. Like I said above, practitioners who utilize the Six Sigma tools know that they are merely a set of statistical tools strung together in perhaps the best possible manner. More important even than the math behind Six Sigma is its ability to influence culture and produce positive change. Whichever way you look at it, I have found, over time, that entering an organization that practices something like Lean (to the exclusion of all else) often means that I can’t call what I’m using “Six Sigma”.  Sometimes this technique of “doing Six Sigma” without advertising it in any way is called “Stealth Sigma”.


Why bother with Stealth Sigma? People seem to react to the term “Six Sigma”. They have preconceived notions about it. Often, it’s not a body of knowledge they have attained and it’s sort of math intensive. They are interested in the sometimes more soft vocabulary of Lean. The Six Sigma body of knowledge, is, again, often fairly math heavy. Talking about things like data distributions doesn’t go down well in the organization that is focused on less quantitative tools.


Instead, don’t ever package or use a term for exactly which tools you’re doing. Use terms like “quality improvement project” and other terms like “statistical process control”. Again, let me recommend, whatever you do, call the process you are using something other than “Six Sigma”.


Enlist others.


Although you may be using the statistical tools and knowledge behind them, try to focus on bringing people together over important issues. Give them the background on devices you are using like project charters etc. There is no need to ever give them the overview of the fact that you are using the DMAIC process or other tools. Just walk them through it and show them the data. Often, they won’t ever realize that you are helping them perform their first ever Six Sigma project.


Celebrate successes.


When there is a success in a project, highlight it greatly. Again, I recommend doing this to make staff feel good about the quality project they’ve just done. Once they are on the other side of it they will start to feel that Six Sigma (or whatever you’ve called it) isn’t so bad.


Highlight tampering versus under controlling.


One of the powerful elements of Six Sigma is its ability to generate statistically useful conclusions. You can guard against tampering (and under-controlling) with hospital systems. Other quality systems don’t do that so well.  I recommend highlighting the risk of type 1 and type 2 error.  Highlighting decision-related issues like that helps differentiate the tools you know and use from others that the organization is currently using.  It shows what the tools can do for you and the the organization you’ve joined.


One thing I have seen in hospital settings is that we, as staff, are sometimes so eager to do better for the patients that we tamper with systems that are already effective or ones which we haven’t adequately characterized.  (More on type 1 and type 2 errors here.)


At the very least, the samples on which we base our decisions can be very poor. This leads to organizations that lurch from one problem to the next rather than truly repairing problems. It’s a problem, but by no means the worst one you can have.  I add this commentary because, in the set of problems your organization can have, I think this is a fairly good one.


It shows that people are trying, they are interested in making improvements, and they just need some guidance to understand when to intervene and when not to. So, when you use Six Sigma in an organization that does not have, believe in, or utilize the Six Sigma body of knowledge, highlight the benefits of using the statistical tools themselves rather than attributing them to the Six Sigma body of knowledge.  The tools can help protect you from tampering with systems and also under-controlling for problems.


In conclusion, let me share with you that I’ve been in organizations that use only the Lean toolset or only a similar quality improvement toolset. Those organizations can achieve remarkable outcomes and do very well. However, it’s worth noting that when utilizing some of the quantitative Six Sigma tools in organizations like that, it is important to perform what is often called “Stealth Sigma”. This means it’s important to call what we are doing statistical process control or use some other term. Remember to highlight early successes and try to get the team rallied around a certain important fact only to quickly celebrate their achievement when they get to the other side of their first Six Sigma project…whether they know they’ve done a Six Sigma project or not!

Do You Blame The Victim Of Your Lousy System?


By:  DMKashmer MD MBA MBB FACS (@DavidKashmer)


Don’t Just Attribute Issues To One Cause


One of the things we do every day is attribute issues to their cause. We are, perhaps, programmed to notice items that seem to cause other events. Some evolutionary biologists believe that our notion of causality has helped us modify the world around us and evolve as no species has before. Yet another interesting take from evolutionary biologists is the idea that we are programmed coincidence machines. In our imagination, when lightning strikes a log, we imagine fire. Soon after the rain when the sun is out, we expect to see a rainbow.


In fact, some believe that we over-notice coincidence. After all, both the log on fire and rainbow immediately post-rain are special, unusual cases–yet these are top-of-mind when we consider the scenario.  Reality is, often, much more mundane than what we imagine and more common than we are prone to notice: when a log is struck by lightning we generally don’t see fire unless the conditions are just so. This entry describes how our notions of causality are sometimes at odds with the process of statistical process control.  It is this evolved, intuitive notion of causality that sometimes gets in the way of quality improvement.  In the end, our over-simplified thoughts on causality may end up making us blame the victims of the lousy system that we own.


Consider, for example, the typical process improvement pathway at your hospital. Often, one service line blames another for trouble. I typically see this in trauma where trauma surgeons blame the emergency department for issues regarding notification of trauma activations etc. It’s a very standard story that one service blames another or one provider blames another for being wrong or somehow just not skilled enough. In fact, what’s really going on is not that simple.


Angry At One Another?  Probably A Bad System Putting You At Odds


More often, the situation and events conspire to make a system that puts people’s interests at odds.  People get mad and blame each other because the system is dysfunctional–and there’s more.


An expanded notion of causality lets us see the deeper truth regarding how people are setup to succeed or fail. For example, consider a trauma center that has issues with triaging its patients. It is over-simplified, often, to say that the emergency physicians do not “call traumas”. In fact, the truth is usually much more complex and requires a deeper dive with an take on causality which is very different than what we’re typically programmed to do.  (Techniques like the “five why’s” may be used to get at these deeper root causes.)


It’s NOT Usually Just A People Problem


Deeper causes lay behind superficial takes on different issues.  It’s rarely just “the ED docs are not calling the traumas”. Thought, and additional techniques, usually reveals that the reasons traumas are not called are multifactorial. The trouble is, perhaps, that we aren’t programmed to look beyond who acts upon us.  We attribute their actions to “how they are”.  (Maybe it’s related to the fundamental attribution error.)


In reality, perhaps the ED is slowed down by traumas and is measured on how long it takes to admit patients to the hospital. Perhaps trauma surgeons do not respond to trauma calls in a meaningful way and so the ED doctors do not activate the system. Perhaps the ED doctors feel like nothing different goes on for the patient when a trauma is activated. These are just a few of the common reasons why “traumas are not called” and notice that few of these are the result of ED provider judgement.  Expanded takes on causality even include things like how the weather impacts what we see from a process.  Further expansion allows us to see which causes are controllable (and how controllable) versus those that we are not able to influence.


Do You Blame The Person Who Is Drowning?


The notions of statistical causality, association, and our everyday notion of causality are clearly very different from each other. Consider, for example, a drowning man. Imagine this man in the middle of the Atlantic Ocean and he is alone.  Eventually, and sadly, you watch him drown. An over-simplified, typical version of causality for the man’s drowning (like one we’d use in the hospital) is that, well, he wasn’t a strong enough swimmer. But does that really make sense?  Although that may be true, it’s incredibly superficial and lacks recognition of the deeper reasons why this poor soul didn’t make it.


When healthcare providers aren’t performing (by the way, weren’t these some of the strongest, most apt, caring individuals coming out of college and high school–what happened?!), do we sometimes blame them as not being strong enough swimmers when the reality is much more complex?  Perhaps the issue is not that these people somehow changed; some of it may be how they look through the lens of our current system.


Now, expand the situation.  Consider that the man was in the middle of the Atlantic Ocean. Consider that he was alone. Consider that he had no safety equipment to indicate his location. Consider the reasons why his boat sank. Consider whether mother nature brewed up an unexpected storm. This expanded notion of causality, which focuses on the six M’s of statistical quality control and special cause variation, gives us a much more robust view of exactly why the man drowned. Bad hospital systems cause drowning people.


In truth, physicians and care givers who work in bad systems are also like drowning people. You can only swim against the wave for so long. And if the system conspires to have you drown rather than set you up for success, we see a recurrent group of problems.  And, just as people beset by the ocean don’t want to disappear quietly, so too do these providers get angry.


In Healthcare, We Tend To Blame Ourselves

In fact, in healthcare, we typically look to ourselves first as the reason something went wrong. We abide by the illusion that, typically, if only we were stronger we could have swam to shore. This is sometimes true, and in many ways (especially for trainees) it helps us focus on learning to be as skilled as possible. However, there are many other causes listed in the six M’s for which things go wrong.


When we really want to rebuild a system to make things go right, we should look to this expanded notion of causality. We should attempt to set things up so that it’s easier to be successful (poka-yoke) than it is to have difficulty. In fact, building a system to make it easier to do the right thing usually leads to success.  This starts with looking at causality that moves well beyond the person standing in front of us.


This can be very tricky, of course, as there are all sorts of reasons why dysfunctional hospital systems conspire to make people drown in work. Tampering with these systems often uncovers old resentments and deep history for the organisation. A careful balance is necessary when we go to adjust systems that are not functional.


…luckily for us there are pre-packaged toolboxes to help us do this. These advanced tools allow us to get beyond the frame of “whose fault it is” and look at that more robust view of causality. Next time you feel like you’re drowning at work, it’s worthwhile to ask yourself whether your swimming skills need improving or whether you can influence the water created by the system.  Remember, when you see someone drowning, it’s worth spending time to look to the system rather than blaming the victim.

Do You Use This Antidote To Painful System Issues?

By:  DM Kashmer MD MBA MBB FACS (@DavidKashmer)


Healthcare colleagues:  have you ever felt like you’re running in mud? We have a term for when multiple small elements create resistance to inhibit you from getting done the work that needs to be done. That term is “friction”.  Friction is the accumulation of all those little things that add up to slow you down (or stop) you from imposing your will upon the disease process.  Well, colleagues, here is the antidote:  poka-yoke.


What Does Poka-Yoke Mean?


Poka-yoke is a design philosophy that, simply put, means “make it easier to do the right thing”. Want the physician to get charts done on time?  Make the computer work.  Make it accessible from anywhere.  Make the charts not come to his/her inbox in the eleventh hour with only one minute remaining before they expire such that they violate hospital staff bylaws (!)


Charts aside, want doctors to be on time for trauma?  Make a call room (and work hours) so attractive that they’ll want to stay there.  Make it physically harder to make a mistake.


A related description of poka-yoke is “poka-yoke as error-proofing”.  At its heart, the concept is the same:  make it more difficult to make a mistake.  This means either setup the system for success (as above) or create a better way to detect defects before the next step in a process.  Add inspection to a step before moving onto the next step.  Create a device that beeps if the water level in the tank I’m filling gets too high, etc. etc.


Error detection and inspection seem, to our team, to be somewhat more challenging to implement in healthcare than the “make it easier” approach described above.  We joked around at a process re-design meeting recently:  hire someone to stand outside the call room and knock or yell “beep” if we don’t wake up to the pager.  Possible?  Yes.  As practical as other choices?  Not really.


Each of the cases above may remind you of challenges with your own system, and (by no means) is the list above representative of what will work for you or all your unique factors involved.  Your data will guide you to your issues (if you let it).  However, the point here is that a workable solution is often not:  “doc work harder and just get it done”, penalties for not working hard enough, or chastising a colleague or entire service line at a meeting.  In fact, a healthy, workable solution may involve some poka-yoke type thinking that is very different than those other listed (more pathologic) interventions.


It boils down to this:  when I’m on the administrator side of the table, and when I need a system to function, I try to make sure it is easier for the person at the tip of the spear (the person awake at midnight, etc.) to obtain the desired outcome.  It needs to be as easy as we can make it given our available resources and what is within the realm of possibility for our system.  In other words, if there is a certain outcome that we want to obtain in quality control, we must make it easier to do the right thing.  That’s the poka-yoke design philosophy that accompanies Six Sigma and, often, Lean.  When was the last time you saw that used in healthcare?


Friction Is So Common & Overwhelming That We’re Trained To Accept it…Yet We Shouldn’t


For physicians, and particular trauma surgeons, we have all experienced that running-in-mud feeling of daily friction. When issues come up that are minor, additive, and problematic, I often jokingly ask “is this just the routine level of friction?” meaning is this just the routine level of friction we see every day or somehow even more than the norm. The facts about friction are so common and known that, well, friction has become a joke…but it shouldn’t be one.


People who speak up are often worried they’ll be labelled as “complainers”.  The truth is (as we all know in healthcare) if we started complaining we may never stop, so it’s easy to try and avoid falling into that bottomless pit.  Training seems to teach some that there’s no upside to complaining.  However, sometimes (just sometimes) the person who has the (often minority) viewpoint of the complainer may be a sign that something is amiss.  Collecting some data about the system can show whether that person’s view is trying to help signal you that there’s an issue or whether the system feels bad to them yet works just fine.


Although we may joke about it, the routine level of friction is often completely unacceptable and mis-aligned with the outcomes we want. Again, in quality control, if we want a certain outcome we need to make the design of the system line up with that desired outcome. That means it greatly helps the person performing the action when it’s easier to do the right thing.  That’s where poka-yoke is so valuable.  Magically, when the system makes it easier to get a desired outcome that’s often what you get.


The poka-yoke design philosophy helps grease the wheels, or gets the wave moving in a way so as to make it easier to surf to shore.  Imagine an environment that makes it easier for us to surf along and achieve a great outcome.  Think of that environment that actually supports our ability to be effective.  There aren’t many that I’ve seen in healthcare, yet the ones that do function that way are truly amazing in terms of quality and provider satisfaction.


Caution:  clearly we can’t spend millions of dollars on each project.  Resources are limited and constrain available poka-yoke solutions.  However, often, the costs associated with poor quality (the COPQs) are MUCH higher than we realize, so some reasonable expenditures on the preventative measures seen with poka-yoke may often work better for our system.  By the way, prevention (as you recall) is the only type of expenditure on quality that has a positive return on investment.  More here.


Have You Ever Seen A Healthcare Quality Project That Decreases Paperwork?


Consider more about how poka-yoke finds its application in healthcare. Have you ever been part of a quality control initiative or similar healthcare project where your paperwork burden is decreased? Probably not, because it just doesn’t seem to happen.  (It can, my colleagues, be done!) Maybe, after reading this entry, you’ll start to look for ways to reduce forms at your next quality improvement meeting.


Next time you are in a quality improvement meeting, remember to look at what the improvement would look like (and how it would feel) to the person on the front line. (Maybe even get the end user’s input in designing the solution!  Dare we ask the residents how to design the specifics of the solution we choose?) Consider how the job of the people on the front line can be made easier and more aligned with the outcome you want. Involve them in the decision making.


Creating alignment may involve removing obvious obstacles, improving resources available, or implementing a solution that just works better for everyone. The bottom line, in any event, is that you should remember “poka-yoke” to make it easier to get the outcome you want. Remember, in the next quality improvement meeting, the idea of friction and its antidote:  poka-yoke.

How To Take Charge Of Your Transformed Data

By:  David M. Kashmer MD MBA MBB (@DavidKashmer)


No Need To Tell You About The Importance Of Data Again…

You will see many posts on here that describe why it’s important to manage ourselves and our systems with data. You have likely also read about other techniques utilized to make improvement including Kotter’s eight steps to culture change, tools like SIPOC diagrams, and even project charters. If you’ve opened an entry like this, odds are there is no need to tell you of how much more useful it is to utilize good data than more typical way we do process improvement in healthcare such as only reviewing individual cases.  You likely already understand all the reasons why what we do with data has many advantages over those alternatives.  No need to beat that horse again!


Now take a moment to focus on a finer, and more interesting point about managing systems with data. Specifically, this entry touches on points regarding non-normal data and the meaning behind transformed data.  Here, let’s wrestle with some of the interesting things that happen when we start to examine healthcare systems with data and wind up with seemingly odd things to manage.  For example, what happens if we transform data and end up having to manage something counterintuitive as a result?


What does it mean to the team, exactly, when you wind up having to manage some variable like time squared?


Ut-oh, I Used Data & Found A Non-normal Distribution

Have you ever measured a system only to find out that the data are not normally distributed? In fact, we have talked about this issue earlier here. As you may remember, we have several options for treating non-normal data. One is distribution fitting. We can try to find what distribution, if any, the data follow. Many tools for this exist including Minitab and Sigma XL. Nowadays, good quality improvement often requires software and knowledge of how to use it. For more, look here.


What about situations where the data don’t fit some non-normal distribution? In those cases, you may opt to transform the data. More on data transforms here. One of the available data transforms most commonly used is the Box-Cox transformation.



Box Cox Can Create Some Things To Measure That Feel Strange


This transformation tries to find a function that changes the data set into a normally distributed data set by raising the each data point to some power. Let me say that again, the data are changed to fit the normal distribution according to some power function that the system discovers. Usually, a program will then test the new data set (with the Anderson Darling test) to make sure these data now do follow the normal distribution.  (More here.)


So, for example, imagine you are measuring time in a certain scenario such as response of trauma providers to the trauma bay. Let’s say the data are not normally distributed and do not fit any of the typical distributions. What now? Let’s say you’ve opted for data transformation. The Box-Cox transformation tells you that although the data regarding time are not normally distributed the square of the time variable is normally distributed.


Okay, now what? What does it mean to manage time squared? After all, it’s hard to feel time squared and so time squared may not have a great deal of meaning intuitively.  All those instances where a provider responds to the trauma bay are measured in minutes and seconds. So, what exactly does it mean to measure time squared and how does the team go about paying attention to it?  This is an interesting philosophic question with which I have struggled in project teams. Let me share how we usually resolve this…


“Transform” Is A Bad Word…It Can Make It Sound Like Cheating


One step involves the utilization of the word “transform”. It’s important for the team to know that the data aren’t somehow subverted or cheated when they become transformed. The use of the word “transform” just means that the tool determines what function makes the variable under study (such as time) into a normal distribution with that aforementioned power function.


In fact, it seems that the word “transform” itself seems to make people feel, at times, that the data are no longer what they were. However, I try to impress upon the team (when we utilize data transforms) that transforming the data simply allows us to make the data into a form which allows use of routine statistical tests. That’s the whole point of the transform:  we get to use tests that are comfortable and straightforward.  If you remember, from earlier entries here, that many of the typical statistical tests we use assume the data are normally distributed. Therefore, when we have data that are not normally distributed, we cannot use typical tests. For more on tests which do and don’t require normal data look here.


Therefore, when we are in a situation where the data are non-normal yet don’t follow any typical distribution, one of the only options left to make the data set workable is a power transform such as the Box-Cox transformation. We try to focus users on the fact that we’ll be comparing variables such as time squared to the same apples (time squared again) that we collect post changes in the system. We will be comparing time squared to time squared.  Apples to apples.


Obviously, the longer it takes to get to the trauma bay, the longer the time is and thus the longer the time squared value will be. Importantly, we try to impress on users this more intuitive way of thinking about variables like time. Does it really matter if we are talking about time or time squared if when one is longer so is the other?  We see that same logic with many transformed process variables.


Keeping The Team Aligned With Reassurance Is The Key


In the end, we find that the typical issues with managing system variables like time squared after power transformation evaporate when we focus the group and educate them regarding what these variables indicate in the system. It’s an interesting philosophic question, but is one that rarely changes what we do or outcomes of quality improvement projects. As mentioned, however, it is key to know whether data are normally distributed or not and how to utilize power transforms effectively for working with non-normal data. Keeping the team aligned is important when we run up against challenging concepts like data-transforms that create unusual variables to manage such as time squared.

How To Decide Which Quality Project To Do First

By:  David M. Kashmer, MD MBA (@DavidKashmer.  Linked profile here.)


Once you start down the pathway of quality improvement, you may start to see potential projects everywhere.  Problems with prolonged patient times in the Emergency Department?  Maybe you’ll start with that project first.  Trouble with supplying or re-setting the trauma bay?  There’s another potential project.  And what about operating room turnover time?  There’s another.  Hmmm…each of these seems very different and very important.  Which one do we address first?


Read on, because this blog entry is about a useful tool to help prioritize each potential issue in order to decide where to turn first.


It’s Not Just About Which Feels Worse


Sometimes, there may be that one project that we just really want to do.  It could be because we are mostly in the operating room, and an issue with turnover time affects us each day.  Or, perhaps, it’s because we are in the Emergency Department a great deal and that project with patient time in the department is so obvious to us that it just has to be done right this minute.


Well, this is a situation where spending some time thinking about which project to do first may help you a lot later on.  Let’s look in on a useful tool to help decide where to go first.


Look At The FMEA


The FMEA, or Failure Mode Effects Analysis, is a tool designed to prioritize potential projects–and it does so based on some interesting criteria.  For example, the FMEA ranks potential failure modes of a system according to severity.


That criterion is clear enough:  the worse the outcome could be, the higher the severity score from 1-10.  That criterion seems fairly obvious.  If a situation could give a worse outcome, that situation (or the project to repair it) receives a higher severity score.


The FMEA also ranks failure modes using their probability of occurrence.  More common occurrences receive a higher score on the 1-10 scale.  We tend to think of the typical failure rate in service industries, with 1 defect in 1000 opportunities (1 sigma), as approximately a 5 on the 10 point scale.  That said, it is the next criterion that the FMEA uses which interests me most.


If A Defect Happens In The Forest, And There’s No One There…


What if you had a system that made a defect that it was impossible to find before it made it to the patient?  Think about it for a moment.  You may have met someone at the quality meeting who says “Well it’s just very difficult to detect this particular issue before it gets to the patient.  And even if it does they do ok.  I see no bad outcomes.  In other words, there’s no way to know.” The implication may be that the defect just doesn’t matter.


Well, in terms of ability to detect a defect, if it’s difficult to detect the defect then the defect matters even more.  Said differently, if there’s no known way to figure out that there’s a defect before it gets to the patient it is more important to prioritize that failure mode on the FMEA.  Impossible to find the defect before it gets to the patient?  That, my friend, is a 10 on the FMEA’s detection scale parameter.


Here’s The Most Useful Part


The next useful step is to multiply the severity (S) index by the probability of occurrence (O) and the probability of detection (D).  This S x O x D gives the Risk Priority Number, or RPN.  Find the failure mode with the highest RPN, and the project associated with that failure mode is often the one to address first.


In the end, the FMEA allows us to rank each potential failure (usually named as the project that would repair it) on the FMEA grid.  Just as importantly, it allows us to bring the team onto the same page about which issue to address first.  It even highlights how events that are more difficult to detect may be the more important ones to address earlier.


Hope you find this quality tool as useful as I do!  For more information on the FMEA process, click here.


And here is an Excel workbook, tabs at the bottom for each step, for your very own next FMEA:




Questions or comments?  Let me know.