Do Not Underestimate The Power Of…The Control Phase

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

 

 

Keep The Gains You Made!

 

Have you made any gains with your quality improvement project?  If you made meaningful improvements, it is every bit as important to make sure those improvements are sustained. This is the last step in our quality improvement project: the Control Phase.

 

 

Do Not Underestimate The Power Of…The Control Phase

 

The importance of the control phase is difficult to overstate. It is one thing to work through all the steps of the process and to make meaningful, measurable improvements. It is entirely another thing to be able to sustain those improvements as you continue to move ahead. Make sure that routine maintenance is performed so that you can sustain improvements and have something on which you can build the rest of your program or other processes.

 

The control phase has several important toll gates as pictured here. One of the important steps of the control phase is to transition the project to the project owners. That means that after extended quality team has gathered around the table and measured data from the trenches, it is important to make it very clear in the control phase regarding who the process owners are. The process owners will be responsible on an ongoing basis for maintenance and reporting if there becomes an issue with the end points of the project. If, on repeated measurement at different intervals, there is a new issue with the process the process owners send out a signal that there is a problem and the end points need to be revisited.

 

 

What Tool Should I Use For My Data?

 

From Villanova University's Lean Six Sigma Black Belt Course, 2010
Figure 1:  How To Choose A Control Chart Type.  (From Villanova University’s Lean Six Sigma Black Belt Course, 2010)

 

 

Just as important is the question of what tool to use for the control phase. There are multiple tools available for the control phase and here we will focus on one important tool that be easily utilized in healthcare. That is the ImR chart, where ImR means “individuals moving range” chart. The ImR chart is one of the multiple types of control charts that can be performed with your data. Please review Figure 1 regarding how to choose a control chart type for your data. In healthcare processes where individuals come through the system one at a time, the ImR chart works best. This chart demonstrates where individuals fall along a continuum over time. We will discuss some important highlights of the ImR chart now.

 

CAUTION:  There are some things the diagram above does NOT tell you.  For example, did you know that control charts generally require normal data? In other words, if your data for a certain endpoint are non-normal, you cannot apply the straightforward, continuous data driven control charts that the diagram lists. If your continuous data are non-normal, that clean Figure 1 just doesn’t apply.  For more information regarding how to determine whether your data are normally distributed look here.

 

Ok, now you have established whether your data are normally distributed.   If they are non-normal you will need to create a control chart based on non-normal data. We will delve into how to do this in another blog entry. For now, let’s just highlight how ImR and other control chart types are based on normal data.

 

 

Why, Here’s An ImR Chart Now!

Beneath please see a typical ImR chart (Figure 2). Notice that if you compressed all the data points to one end or the other of the control chart and remove the factor of time you would create a normal data plot. So, now to some definitions to let you know more about ImR charts and how to know whether your process is still in control or has gone out of control.

 

ImRExamplejpg
Figure 2:  An ImR Chart Example.  Time To OR (in hours)

 

 

Before we move on, look at the top plot in Figure 2.  The green bar represents the mean/median/mode of these data.  The red bars represent the 3 standard deviation mark beyond the mean.  Any data beyond three standard deviations is considered to be MORE than the routine amount of variation expected in this system…and that makes sense.  After all, the probability of a data point being more than 3 standard deviations beyond or beneath the mean in a normal data set is less than about 1%.  So, data points outside the red bars make us ask:  “What happened?” Setting the 3 standard deviation mark as what we use to identify outliers balances the risk of type 1 vs. type 2 error here.  Note that identifying cases to review this way doesn’t mean anything was done wrong in those cases by any particular doctor or healthcare provider.  We just have to ask how did the system produce such an outlier?  For more info on type 1 vs. type 2 error, look here.

 

Now pretend that there was some requirement (perhaps made by your state) that trauma patients needed to be brought to the OR within 2 hours of arrival for severe abdominal injuries (if they need to go at all).  What would you think about that top date in Figure 2?  Maybe draw another line at 2 hours to represent that barrier.  What do you notice?  Well, PLENTY of points on the graph show patients as being brought to the OR outside of the 2 hour mark.  Interesting!  Those points at 2.5 hours, 2.7 hours, are completely “in control” from what we told you earlier YET THEY ARE COMPLETELY UNACCEPTABLE.  Interesting, right?  This tells you that your system is performing at its routine levels, and that routine level of performance is NO GOOD.

 

This is why we should NOT apply control charts until the end of a quality project:  the control chart can tell us when the system is performing routinely yet lull us to sleep.  It can tell us the system is performing routinely…yet that routine may be NO GOOD!

 

More On How To Use The Tool

 

Control charts can be further evaluated with what are called the Westinghouse rules. These have been modified over time but are still a good basic starting point. Here those Westinghouse control chart rules:

 

1. The most recent point plots outside one of the 3-sigma control limits.

2. Two of the three most recent points plot outside and on the same side as one of the 2-sigma control limits. 

3. Four of the five most recent points plot outside and on the same side as one of the 1-sigma control limits.

4. Eight out of the last eight points plot on the same side of the center line, or target value.

5. Six points in a row increasing or decreasing.

6. Fifteen points in a row within one sigma.

7. Fourteen points in a row alternating direction.

8. Eight points in a row outside one sigma.

 

However, let me tell you that not everyone exactly agrees with the Westinghouse Rules, and there are different rule sets out there.  That one is pretty standard, however.  One thing we all agree on:  a point outside of the 3 sigma (3 standard deviation red bars) control limits is just not good.  Look at those cases.

 

Now let’s discuss the other part of the ImR chart, the range chart. The range is a measure of variance between data points. In other words this shows us how wide the swings are in our data. It is possible to have a range which is unacceptably high and which demonstrates that there is a significant amount of variance in the system that was unexpected. This can happen even when the data point itself is in control. If we see an unusual amount of variance between data points the question becomes “Why such a wide swing and why such an unusually wide swing for our data set at our institution?”

 

 

Conclusion

 

At the end of the day the ImR chart is a very useful tool in healthcare for the control phase of the project. Remember, just as important as the particular tool is the fact that we engage in a control phase. The control phase helps us receive feedback from the system when something has gone wrong, something needs maintenance, and the “weeds need trimming”.

 

There are many options for the particular tools you can use during the control phase for your project. Some are listed above. As usual the question of whether your data are normally distributed is central to being able to apply a control chart in a straightforward manner. Not only are there many types of control charts, but there are also some that do not require this focus on normal data and we will discuss these at another time. Best of luck with your quality improvement project.  Please leave any thoughts or comments beneath!

 

Without Data, You Just Have An Opinion

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

 

Do you agree with the thought that Six Sigma is 80% people and 20% math?  Whether or not you do, it’s important to realize that the 20% of the process which is math is VERY important.  As we discussed in other posts, the virtues of basing decisions on good data rather than your gut, social pressure, or other whims can’t be overstated.  As usual, we’re not saying that “feelings” and soft skills are unimportant; in fact, they’re very important.  Just as data alone isn’t enough (but is a key ingredient in consistent improvement) so too are feelings/intuition not enough when applied on their own.  Here, let’s explore an example of what good data analysis looks like–after all, without the engine of good data analysis, the quality improvement machine can’t run.

 

Starts With Good Data Collection

If the situation of your quality improvement project is not set up properly–well, let’s just say it’s unlikely to succeed.  We’ve discussed, here, the importance of selecting what data you will collect.  We’ve referenced how to setup a data collection plan (once over lightly) including sample size and types of endpoints.

 

It’s possible that the importance of setting things up properly can be overstated–but I think it’s very unlikely.  The key to the rest of the analysis we will discuss is that we have a good sample of appropriate size that collects data on the entire process we need to represent.  Yes, colleagues, that means data from the times it’s tougher to collect as well such as nights and weekends.

 

Requires A Clear Understanding Of What The Data Can (and Can’t) Say

The ball gets dropped, on this point, a lot.  In an earlier entry, we’ve described the importance of knowing whether, for example, your continuous data are normally distributed.  Does it make a difference?  No, it makes perhaps the difference when you go to apply a tool or hypothesis test to your data.  Look here.

 

Other important considerations come from knowing the limits of your data.  Were the samples representative of the system at which you’re looking?  Is the sample size adequate to detect the size of the change for which you’re looking?

 

You need to know what voices the data have and which they lack.

 

Nowadays, Often Requires Some Software

I’m sure there’s some value to learning how to perform many of the classic statistical tests by hand…but performing a multiple regression by hand?  Probably not a great use of time.  In the modern day, excellent software packages exist that can assist you in performing the tool application.

 

WARNING:  remember the phrase garbage in, garbage out.  (GIGO as it is termed.) These software packages are in no way a substitute for training and understanding of the tools being used.  Some attempt to guide you through the quality process; however, I haven’t seen one yet that protects you completely from poor analysis.  Also, remember, once the tool you are using spits out a nice table, test statistic, or whatever it may show:  you need to be able to review it and make sure it’s accurate and meaningful.  Easily said and not always easily done.

 

Two of the common, useful packages I’ve seen are SigmaXL and Minitab (with its quality suite).  SigmaXL is an Excel plug-in that makes data analysis directly from your Excel very straightforward.

 

Means You Need To Select The Correct Tool

We explored, here, the different tools and how they apply to your data.  (There’s a very handy reference sheet at the bottom of that entry.) If you’ve done the rest of the setup appropriately, you can select a tool to investigate the item on which you want to drill down.  Selecting the correct tool is very straightforward if the data setup and collection are done properly, because it’s almost as if you’ve reverse engineered the data collection from what it will take to satisfy modern statistical tools.  You’ve made the question and data collection which started all of this into a form that has meaning and can be answered in a rigorous fashion by common tools.

 

Allows A Common Understanding Of Your Situation Beyond What You “Feel”

This is my favorite part about data analysis:  sometimes it really yields magic.  For example, consider a trauma program where everything feels fine.  It’s pretty routine, in fact, that staff feel like the quality outcomes are pretty good.  (I’ve been in that position myself.) Why do we see this so commonly?  In part, it’s because services typically perform at a level of quality that yields one defect per every thousand opportunities.  Feels pretty good, right?  I mean, that’s a whole lot of doing things right before we encounter something that didn’t go as planned.

 

The trouble with this lull-to-sleep level of defects is that it is totally unacceptable where people’s lives are at stake.  Consider, for example, that if we accepted the 1 defect / 1000 opportunities model (1 sigma level of performance) that we would have one plane crash each day at O’Hare airport.  Probably not ok.

 

Another common situation seen in trauma programs concerns timing.  For instance, whatever processes are in place may work really well from 8AM until 5PM when the hospital swells with subspecialists and other staff–but what about at night?  What about on weekends?  (In fact, trauma is sometimes called a disease of nights and weekends.) Any data taken from the process in order to demonstrate performance MUST include data from those key times.  Truly most quality improvement projects in Trauma and Acute Care Surgery must focus on both nights and weekends.

 

So here again we have the tension between how we feel about a process and what our data demonstrate.  The utility of the data?  It gives us a joint, non-pejorative view on our performance and spurns us toward improvement.  It makes us look ourselves squarely in the eye, as a team, and decide what we want to do to improve or it tells us we’re doing just fine.  It puts a fine point on things.

 

Last, good data has the power to change our minds.  Consider a program that has always felt things are “pretty good” but has data that say otherwise.  The fact that data exist gives the possibility that the program may seek to improve, and may recover from its PGS (Pretty Good Syndrome).  In other words, part of the magic of data is that it has the power, where appropriate, to change our minds about our performance.  Maybe it shows us how we perform at night–maybe it shows us something different than we thought.  It may even tell us we’re doing a good job.

 

At The End Of The Day, Your Gut Is Not Enough

Issues with using your “gut” or feelings alone to make decisions include such classic problems as the fundamental attribution error, post-facto bias, and plain old mis-attribution.  It was DaVinci, if I recall, who said that “The greatest deception men suffer is from their own opinions.” We have tools, now, to disabuse ourselves of opinion based on our experience only–let’s use them and show we’ve advanced beyond the Renaissance.  So now we come to one of the “battle cries” of Six Sigma:  without data, you just have an opinion.  Opinions are easy and everyone has one–now, in high stakes situations, let’s show some effort and work to make actual improvement.

 

Let’s Analyze Your Data

 

By:  DMKashmer, MD MBA MBB FACS (@DavidKashmer)

 

Do you remember Mark Twain’s three categories of falsehood?  Mr. Twain described these as “Lies, damn lies, and statistics.” (I’ve also seen the quote attributed to Benjamin Disraeli.) Well, no matter who said it, the bottom line is clear:  we need to be very careful with statistics.  So, if you’re performing a quality improvement project for your system, what are the pitfalls of data analysis.

 

Just Having Data Is A Good Start, But Isn’t Enough

 

Up front, let me take a moment to compliment you, again, on even getting data for your quality project.  Deciding to make decisions based on your team’s data rather than your gut or your own feelings will get you a lot farther down the path to success.  Yes, your colleagues may be worried, initially, until you show them that the data in your project are not assignable to any one person.  (It’s team and system performance–not individual based.) However, let me share with you that I’ve been in organizations which try to use their gut or feelings or some other whimsy to make decisions.  Over time, you’ll come out way ahead with data…you’ll make constant improvement and you’ll be able to show those improvements over time.  You’ll know if you’re doing better or worse.  Not so with organizations that practice by whim or feelings.  (Feelings have a real value, don’t get me wrong, but feelings without data are like lost children.)

So, congrats on even having data.  But, my colleague, you need to go further to have a successful, high quality program:  you need to analyze those data effectively (and correctly) to avoid basing your decisions on damn lies (!) So this brings us to the next step of a sound quality improvement project:  analysis.

 

Pitfalls of Opening Pandora’s Box

 

You see, one of the pitfalls of making data-driven decisions is that you need to be able to correctly analyze the data…and that’s no easy task.  Six Sigma practitioners are trained to use standard statistical tools to demonstrate the valid, meaningful conclusions you can make based on your data–and let me share with you that, prior to my training, I had no idea of what needed to be done to understand and demonstrate meaning from data.  To my medical colleagues:  yes, we take biostatistics classes and these make us conversant in techniques; however, going from sample design to data collection to meaningful conclusions is NOT what I’d seen in medical school or elsewhere.

For example, take this example of the perils of using data distributions:  click here.  Or check out some other pitfalls here.

 

A Few Tricks of The Trade

 

In reality, there are more than a few tricks to the trade.  You’ve seen, in the links above, how important it is to decide whether you data are normally distributed (and what to do if they’re not).  You’ve seen, again above, some of the relevant ideas about how to collect data (and what type of data) to make later analysis much more straightforward.

We’ve discussed, in earlier entries, the idea of what to do when data aren’t normally distributed.  Take a look here.

 

Get Professional Help

 

With all that in mind, it’s no wonder people seek professional help!  Allow me to recommend, here, that you either develop the in-house expertise necessary to analyze and obtain data effectively or you find someone who can.  (Just email the team at info@thesurgicallab.com for our recommendations and ideas about where to go for more info.)

 

Some Parting Thoughts

 

If you’ve made it through the Define & Measure phase of your quality project, and you have data you’re looking to analyze, allow me to compliment you again.  You’re miles ahead of what I’ve seen in some organizations, and are on your way to looking at yourself squarely and both characterizing your system’s current performance as well as improving it over time.  Nice work–you’re miles ahead of others and miles further on the journey to excellent performance.

Now it’s time to focus on specifics of data analysis and some examples of how these tools come into play.  Stay tuned for the next entry on data analysis with examples from projects gone by.

Questions, comments, thoughts?  Let me know beneath.

Let’s Talk About Data

 

By:  DMKashmer, MD MBA MBB (@DavidKashmer)

 

 

A Car Isn’t Just About The Wheels…But It Needs The Wheels To Go.

 

We have spent a great deal of time running through the background of a typical Six Sigma project. This is because I have heard colleagues estimate that 80% of the Six Sigma process is about the people and the teamwork, and that seems about right to me.  Without a supportive team and a receptive administration, the Six Sigma pathway is slow to succeed if it succeeds at all. However, despite the fact that much of Six Sigma is not about the mathematics and statistics, the mathematics and statistics are a huge difference maker. Alone they are not sufficient for project success; however, they are a necessary ingredient. After all, a lot of the car is not about the wheels–yet you absolutely need the wheels. Now let’s take the time to discuss Six Sigma data collection plans and other important considerations so that we can get those wheels turning.

 

West Meets East

 

There are some important differences between classic Western management styles and Eastern management styles, and these play into what we do with data. First, as we have mentioned, the rebuilding of Japan post World War 2 allowed Western modern management thinkers to implement advanced techniques in a receptive culture. (West went East, and it flourished.)

 

This deployment resulted in an interesting blend of statistical process control and Eastern philosophy. Western classic management, by contrast, is very person and individual focused. It has often been described as being less data driven. Of course, counter examples spring to mind immediately. Instance, time-motion studies performed in early America, Henry Ford’s production line, and multiple counter examples clearly exist. We are speaking in generalities here not hard and fast rules.

 

That said, modern Western management styles are incorporating data driven decision making more and more. Not only is “big data” a catch phrase, but Six Sigma and Lean deployments have really changed the face of what we describe as Western management philosophy. Here, let’s describe some of how adding a data layer to your project really gets it moving in the right direction.  Let’s go to the nuts and bolts.

 

The Data Collection Plan Is Based On The SIPOC Diagram

 

We have previously discussed the SIPOC diagram as a high level process map (and beyond) for which ever process you are trying to improve. We mentioned the SIPOC diagram as one of the Six Sigma tollgates in the define phase. Now let’s move on to how this SIPOC diagram is woven into creating a data collection plan.

 

No Matter Which Data Points You Choose To Collect, Get The Data Right From The Process

 

First, some broad philosophic points. When we collect data for Six Sigma projects, we recommend avoiding the use of data from data warehouses or trauma registries whenever possible. Why? This is because data in warehouses and similar registries has often been cleaned, edited, or otherwise filtered. Whenever possible, we recommend getting data directly from the process. Spending time ‘on the factory floor’ is useful in that it leverages the Hawthorne effect and gives the team (as well as managers) a real feel for how the process works. We recommend going to the gemba whenever possible.

 

Let’s take a moment to continue the philosophy of why we collect data that way we do. First, the question has often come up of whether leveraging the Hawthorne effect is appropriate for these projects.

 

The answer:  we will take quality improvement anyway we can get it….as long as it is sustainable.

 

Hawthorne Effect?  We’ll Take It!

 

Let me explain what I mean. We don’t mind if the Hawthorne effect is a driver for quality improvement, as long as this quality improvement is sustainable. That’s why, whether the Hawthorne effect is at play or not, we look for improvement that is quantifiable, reproducible, and persists in the control phase (a later Six Sigma project phase) and beyond. We don’t care if the Hawthorne is one of the players that improves things; however, we want to make sure this is sustainable. So, the first two philosophic points are: 1. Take data directly from the process in real time if possible and 2. Hawthorne effect? So what…if it’s sustainable!

 

Specific Nuts And Bolts

 

Next let’s move into the nuts and bolts of how to turn your SIPOC diagram into your data collection scheme. Remember, the SIPOC diagram highlights Suppliers, Inputs, Process, Output, and Customer. In general, to fully characterize your system you will require six or seven endpoints. (Yup.  That’s it.  NOT 20 data points.  NOT 400–just six or seven for quality improvement projects.  We’re not talking about getting novel insights from data a la “big data” here.)

 

This is includes one or two input endpoints, 1 process endpoint, and two or three output endpoints. These endpoints are key to adequately describe your system.  Where do you get them?  You look at the SIPOC diagram to see each element of the process, and you choose endpoints that the group agrees have meaning for that element.  In healthcare, we suffer from an issue here:  we often can’t believe that (with all the data we see and track) that we need so few endpoints to make dramatic improvement.  Guess what healthcare colleagues:  we don’t need to be data rich and information poor for our quality improvement projects…even though we may be used to it in the rest of our work.  It’s always a challenge to focus healthcare teams on narrowing our view to the six or seven required endpoints.  It’s challenging…but rewarding when the project succeeds.

 

Another important note:  sometimes, we are able to ‘double dip’.  “Double dipping”, here, is a term used when one endpoint can actually represent two elements of your system. Yes, sometimes we do need to use trauma registry data or other data that has already been collected (because it’s very handy, easy to get, and does what we need.) Sometimes an element from the registry can serve as an input measure and a process measure.  Again, we try to avoid registry data whenever possible…yet, sometimes, not only do we use it but we allow it help us double dip.

 

How Much Data Do We Need?

 

The next question is how much data do you need? We have included sample size equations for discrete and continuous data here. These equations can help you determine how much data you will need to characterize your system in order to detect a certain size change. It is useful at this point to calculate what sample size you will need and to determine how small of a change you would like to be able to find. Figure those things out before you collect data.  Of course, positioning a sample to detect a smaller change means you need many more data points. Read more about it here.

 

The next important philosophic take home message is we try to use continuous data whenever possible. Why do we use continuous data? Read more about it here. As mentioned in that post, we can do a lot more with a lot less continuous data as opposed to discrete data. We encourage the use of continuous data whenever possible. And, if need be, it makes sense to turn your discrete data endpoints into a more continuous type of data with something like a Likert scale.

 

That’s The Setup, & More To Come

 

So, now you have the setup. You need to take your SIPOC diagram and establish which endpoints have meaning for you. It is a great idea to take endpoints that are easy to collect right from your process. This often makes data collection obtainable. All of that said, there are some other challenges to data collection.  More on that in the next post!

 

See you soon.  Questions?  Comments?  Let me know beneath.

 

 

 

Use A SIPOC Diagram For Your Next Quality Project

 

sipocinspirejpg

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

By:  DMKashmer (@DavidKashmer)

 

Next Tollgates In DMAIC’s Define Phase

Previously, we have discussed the importance of the project charter along with a focus on the cost of poor quality. (All of that is available to you here.) Now, let’s turn our attention toward two other important components of DMAIC’s Define phase which include determining the nature of customer needs and making a process map.

 

The Voice Of The Customer (VOC)

Customer needs and requirements are key in establishing the endpoints for the rest of the project. In particular, one important question is ‘Who is the customer?’ Different projects have different customers, and sometimes who exactly the customers are is not intuitive. You may be performing a project to satisfy a state or federal regulatory bodies endpoints, internal customers such as people in your organization who receive output from a process, or even both. The importance of focusing on who exactly the customers are for your project cannot be overstated.

 

Once you have determined exactly who it is you are trying to satisfy, you can begin to see the Voice Of the Customer, or VOC, in your processes. The VOC is determined from customer driven endpoints. Sometimes, these are very obvious in that the state or accrediting body tells you what has to be done. They may say, for example, that 80% of the time a trauma surgeon has to arrive to the trauma bay within 15 minutes of patient arrival. This is a very straightforward voice of the customer for your project. Other times, and in less regulated fields, things may not be as clear. You may need to have small focus groups or otherwise spend time with customers with surveys or other tool in order to determine exactly what quality looks like to a customer and what elements they are focused on. However, realize that we must determine what quality looks like to a customer in order to progress in the quality improvement project.

 

From Villanova University Lean Six Sigma Course 2009
Figure 1:  House of Quality (Quality Function Deployment or QFD) From Villanova University Lean Six Sigma Course 2009

 

 

 

Some interesting tools have evolved, including the House of Quality pictured above (as Figure 1), to turn customer needs into defined endpoints. (We will discuss the House of Quality in an upcoming entry.)

 

Building Out A SIPOC Diagram

After you have determined exactly what quality looks like to your customers, and who your customers are, it’s time to focus on making a process map. A process map is often called a SIPOC diagram. SIPOC stands for Suppliers, Inputs, Processes, Outputs, and Customers. So with a SIPOC diagram, the focus is usually first placed on the mapping the process involved.

 

Figure 2:  Sample SIPOC diagram from a previous project.
Figure 2: Sample SIPOC diagram from a previous project.

 

 

If you have previously determined the scope of your project, as we advised you in an earlier entry here, you clearly know the bounds for the process. For example, you usually have determined a start and end time. These allow us to focus first on the P in the SIPOC diagram.

 

Take a moment to review the sample SIPOC diagram listed above as Figure 2. Once you have determined the bounds of the process, describe the process in five to six high levels steps. In healthcare, we typically try to be overly detailed with respect to this process map. As you perform more projects, you’ll realize we only require five to six high level steps for the process at this point. Usually this easily suffices for later work with the project. If you need to get more specific later on, there are many tools for that available.

 

Once you describe the process at a high level, it is time to focus on your suppliers and inputs. Often, for healthcare projects, we describe the input as a ‘packaged patient’.  A packaged patient maybe a patient who is completed several key steps and is now ready for the next system–the process on which you are working for improvement. ‘Packaged’, to us, means a patient who has had multiple things already occur such as having a history with physical exam, cervical spine collar applied, imaging studies, etc., etc. Depending on the process you are looking to improve, a packaged patient may mean something different.

 

We’ve constructed the ‘packaged patient’ over years of Lean and Six Sigma work because it is an easy concept for people in healthcare to understand. Further, it is often easy to measure with a Likert scale or similar construct. While at least one input to different processes is a packaged patient, there maybe other inputs depending on the process you are trying to reform. Again, the particular inputs are completely focused and contingent on what your process is. That’s why, again, we recommend working on the P in SIPOC first.

 

Who Are The Suppliers For Your Inputs?

Once you have the process and inputs laid out it is important to determine who the suppliers are for your inputs. From where does the packaged patient come? From where does the back brace come? Suppliers may include the brace company, the consultant who determines that a brace is necessary, and the emergency department or floor nurse who supplies the patient. Often, for trauma-type SIPOC diagrams, EMS is a key supplier.  Note, interestingly, that the concept of a packaged patient can be leveraged as an easily measurable input or output from the process.

 

Now that you have determined your suppliers, input, and process, it is time to determine the output. The output is often the packaged patient with some additional feature or value added. This maybe a patent with a brace to follow our example, or something similar.   The customers, however, can be more challenging. As we have described here, it is often very challenging to determine who the customer is in healthcare. The customer maybe a third party payer, the patient, other physicians, or EMS. Here the customer may be the trauma service. It may be that patient and also may be the social worker / discharge planning who receives the fact that the patient now has a brace and is ready to progress in their care. Physical Therapist and Occupational Therapy team may also be customers. Of course, Physical Therapy and Occupational Therapy may also be suppliers, in that they give an input to the system which is their evaluation and recommendation for bracing or further physical therapy etc.

 

Completion of A SIPOC Is Essential

Clearly, completion of a SIPOC diagram is essential for the Six Sigma DMAIC process.  Failure to complete a SIPOC diagram makes it much more challenging for the team when it decides what to measure and what must be measured in order to focus the project and achieve success.

 

So, the next step for the Six Sigma project is to utilize this SIPOC diagram in the next phase of your project:  the measure phase. We take the defined process map and use that to generate endpoints that the team agrees on as having meaning in your particular organization. This is part of the power of Six Sigma and Lean in your organization.

 

Six Sigma Vs. Just Trying To Do “What The Literature Says”

Let’s take a moment to talk about the philosophy behind what makes these projects more effective than simply trying to implement what the literature says. After years of performing these quality improvement projects one thing becomes very clear:  the literature may talk about best ways or best practices for different endpoints of patient care. I cannot impress upon you enough the fact that the literature often indicates a great result achievable at some center owing to its unique processes, people, and other strengths.  Can you do it at your shop?  Maybe…but odds are it will look different and be achieved in a very different way. The challenge lies in applying that literature to achieve excellent end points in your system.

 

This is what makes Lean and Six Sigma so valuable. They are processes by which we can improve our endpoints in our systems at our hospitals. Sometimes there are barriers to the process that are cultural in nature. However, if we implement the Lean and Six Sigma process we see improvement in our endpoints that have meaning. We see recouped cost of poor quality at our institution. See the difference? The difference is the tension between the findings related in the literature (contingent on all the vagaries at the centre at which it was generated) versus a culture change with rigorous statistics and decision making that is completely focused on our institution and doing better with our systems. Sometimes the Six Sigma process encourages us to completely change or revamp a system. Whether we revamp a system completely or tweak the one we have, Lean and Six Sigma are very different than simply trying to apply another center’s literature to our center.

 

In short, what works at one center is unlikely to work at yours owing to how vastly different one place is from another. However, what Lean and Six Sigma generate will clearly demonstrate improvement or no improvement (and a continued need for improvement). These are very valuable and very different than trying to roll out whatever the literature tells us to do.  DMAIC, with its define step and team-based rollout, allows us to generate solutions and implement them as a team…even while we capture data to tell us whether we really are doing any better.

 

In conclusion, the define step is the first step forward to improving a system in your hospital or healthcare system. It requires team building, a project charter, and a clear path to be established including the Voice Of the Customer and a SIPOC diagram to map out the process for later improvement. All of this is done in the team context to focus on meaningful improvement that works at your center, rather than an often-doomed attempt to transport something the literature tells you is great directly to your center en masse.  Hope you find the SIPOC diagram and VOC elements useful as you work to improve the systems in which you practice.  Best of luck in your continued quality improvement journey and I look forward to hearing your thoughts in the comment field beneath.

TRIZ Helps Your Next Quality Improvement Project

 

By:  DMKashmer, MD MBA MBB

 

TRIZ Helps Generate Creative Ideas In A Focused Manner

 

An important factor in quality improvement projects, surprisingly, is creativity.  How do we generate and select interventions for a system?  How do we create entire new systems that have high levels of quality designed in?  The TRIZ tool (pronounced as “trees”) helps us design creative interventions in a focused, effective manner.

 

A Criticism Of Six Sigma & Lean Is That They Don’t Allow For Creativity

 

It’s a typical criticism:  Lean and the Six Sigma DMAIC pathway do not allow for creativity.  Clearly, to anyone who has participated in a DMAIC project, there is quite a bit of room for creativity.  Specifically, Six Sigma does not prescribe the specific interventions to make a system better.  It does, however, give certain philosophies like poka-yoke.

 

Poka-yoke Directs Us To Look For Creative Interventions

 

As we begin to explore TRIZ methodology, let’s take a moment to review one of the design philosophies routinely used by Six Sigma:  poka-yoke (pronounced “poke ah yoke”).  After all, the philosophic underpinnings of Six Sigma are what lead us to use TRIZ methodology in the hunt for creative, effective interventions.  Poka-yoke is an idea that guides us to make it easier to do the right thing.  That is, if we want physicians to record some piece of data on patients, we should make it very easy to input that data.  If we want someone to be somewhere on time, we should build a system that makes it as easy as possible to get to that place on time.  Poka-yoke says we should make it as easy as possible for a system / person to achieve the outcome we want.

 

This is challenging, often, for us in healthcare; we typically don’t see systems designed to make it easier to obtain a certain outcome.  We do get plenty of feedback telling us how important something is to do or how we MUST do something.  Yet we often have systems that conspire to make it difficult to achieve whichever item is being pushed.  However, let me share that processes which make it easier for us in healthcare do exist, and when we help create them it makes for a much more high-performing system.

 

In fact, poka-yoke design philosophy extends to many interventions.  For example, one trauma program with which I have participated needed to make sure trauma surgeons arrived to the trauma bay within 15 minutes of patient arrival to the trauma bay.  The team needed to make sure this happened more frequently than was typically occurring.  The poka-yoke design philosophy allowed the team to focus on specific interventions that made a higher probability the surgeon would be there on time.  This included NOT simply focusing on telling the surgeon ‘You need to do a better job’. Interventions included focusing on early identification and triage of trauma patients and positioning the call room physically closer to the trauma bay.  This type of poka-yoke design philosophy and associated interventions made it easier to do the right thing and achieve a timely arrival.

 

Now that we see how poka-yoke directs us to look for solutions to make it easier to do the right thing, where do we go to create them?  Typical tools that teams use to generate solutions include brainstorming, mind-mapping, and many other standard, creative tools.  Here, let’s add another tool to your toolbox that allows brainstorming along certain high-yield directions.  TRIZ methodology takes brainstorming sessions and focuses them in directions that are apt to be high-yield.  Here’s how.

 

TRIZ Tool Explained And Link To Where To Find It

 

The acronym TRIZ comes from the Russian wording equivalent of ‘Theory of inventive problem solving.‘   GS Altshuller and colleagues, between 1946 and 1985, reviewed world wide patent applications so as to determine themes and manners in which certain problems were solved.

 

Over time, the team identified fundamental conflicts that were at the heart of the many issues which the patents / designs attempted to resolve.  For example, some patents embodied a design used to make something stronger yet lighter.  These conflicts (and their solutions) were then codified into a TRIZ matrix.  TRIZ gives direction to resolve these conflicts by looking to how they have been resolved previously.  In other words, TRIZ is the process of codified creativity.  An oxymoron?  Maybe–yet TRIZ methodology has proven highly effective to accelerate our creative processes in the past.  You can use the TRIZ matrix here.

 

Consider this example of TRIZ applied to Six Sigma:  one of the challenges in designing a new system involved the trade off between strength of a product and weight of a product.  The TRIZ gave us focused ideas on how this problem has been solved across many, many, patents and designs throughout the world.  TRIZ focused meetings and sessions allowed us to be creative along certain highly productive lines. In fact, TRIZ methodology assisted us in many of our design projects and even in our DMAIC projects where brainstorming for intervention was more challenging.  TRIZ allows us to resolve the fundamental contradictions inherent in a problem in a codified, effective way.

 

In conclusion, TRIZ methodology gives us a focused tool that is often superior to the perhaps more routine brainstorming.  Once data have been reviewed and it is time to design an intervention, TRIZ methodology has been very handy for us.  There are multiple, online TRIZ resources including here, here and here. Remember, for your next quality improvement project, if you want to solve an issue with the forest in a creative fashion, look towards the TRIZ.

 

Questions, comments, or thoughts on TRIZ methodology in your quality improvement project?  Have you previously seen TRIZ methodology be successful for your project?  Let us know beneath.

Use The Project Charter For Your Quality Project

 

By:  DMKashmer, MD MBA MBB

 

 

The Project Charter Is The Most Frequently Used Tool

In earlier entries we talked about the DMAIC pathway and some of the different tollgates that make up DMAIC.  In any quality improvement project that you perform for your health care system, startup, or established business, one of the most useful tools that the Lean Six Sigma Black Belts use is the project charter.  There are many tools from which the black belt can select when they run a project.  One of the tools most frequently employed by black belts for each project is the project charter.  Whether you are a black belt or trying to run an ad hoc quality improvement project for your healthcare system, or working in a formal Six Sigma deployment, the project charter is a very useful tool.  Let’s take a moment and explore the project charter.

 

The Three Most Important Parts of The Project Charter

First, the project charter lays out the nature of your project.  The title clearly establishes what you are working on.  However, to my mind, the three most important elements of the project charter are 1: the stakeholders, 2: the project scope, and 3: the cost of poor quality.  We have previously discussed some of these elements.  For more information on the cost of poor quality you can look here.  The COPQ gives a bottom line expected return on the quality improvement project.  This is something that management and other administrators can rally around to get a sense of the impact your project will have on the bottom line.  Again, in healthcare, the cost of poor quality can be challenging to establish and is composed of the four buckets described here.

 

Scope Creep Is A Common Reason Projects Fail

Next, consider the stakeholders.  Although Lean and Six Sigma do have a substantial focus on math and statistics, it is important to realize that lean and six sigma are team sports.  Making it very clear up front who the involved parties are is key to overall improvement.  In the absence of a strong team, no improvement can be made.  One of the elements of the project charter that we have not described previously is the scope.  The scope is the defined interval to which the project applies.  By this I mean that we need to be very clear about the start and stop for a project.  If the project concerns admitting patients to the hospital we need to be clear that we intend to focus the project on the interval between when the patient arrives at the emergency department until the time at which the patient physically leaves the emergency department.  This is how we clearly scope the project.  The scope is very key to a projects success because one of the most common issues associated with project failure is called ‘scope creep’.  ‘Scope creep’ occurs when the project becomes too large with too many elements.  Therefore, defining the scope clearly and adequately at this point in the project is important.  There will be later opportunities to clarify this scope during the formation of a data collection plan.

 

Project Charter Is One Of the Most Important Elements In A Successful Project

In the end, the project charter is one of the most important elements of a successful quality improvement project.  It focuses us on the team members involve, the scope of the project, and the projects expected return.  For a sample project charter please click here:  charter for blog.  Questions, comments, or thoughts on the project charter?  Please let us know beneath.

Have You Heard Of DMAIC? (Blogging A Book Part 2)

 

By:  DMKashmer, MD MBA MBB

 

Origin Of The Name

 

Now let’s talk about the pathways for Six Sigma.  First, let me share with you the origin of the name “Six Sigma”.  The idea behind the name of Six Sigma is that we want to be able to fit six standard deviations of data between the lower limit we would accept for the data and the upper limit we would accept for the data.  In other words, we take a process that usually functions with 0 or 1 standard deviations between the lowest and highest value (most systems) and attempt to make the process much more likely to deliver a good result by creating a situation where six standard deviations of data fit between the upper and lower specification limit. This will become much more clear as we go on.  For now just know that the term Six Sigma refers to the level of quality we are trying to achieve.

 

Six Sigma Uses Statistical Tools You May Already Know Arranged In A Certain Way

 

Next, take a minute and realize that Six Sigma takes existing statistical tools and puts these together in a meaningful way to achieve this outcome.  Six Sigma is nothing magical or revolutionary in terms of methodology.  It simply takes known tools that we could use by themselves and puts these together in a much more meaningful way.  So, to my healthcare colleagues:  don’t worry, Six Sigma is just the same old statistics you’ve seen elsewhere coupled with a methodology to apply these to what we do.  The system also sets up the pre-condition for us to all be on the same page with these useful tools and it allows to draw similar conclusions from similar data.

 

Six Sigma Evolved From Western Management Gurus Who Went East

 

It should be at least mentioned here that Six Sigma evolved in a unique environment.  We won’t go into the history too much in this text but you probably know post World War II Japan went through a rebuilding stage. American quality gurus went to Japan and were able to use the latest in management methods to help rebuild the organizational culture.  The focus on quantitative tools for management with a data driven culture really resonated with the local environment and over time this set of tools emerged as a slick, packaged system called Six Sigma.  Motorola is one of the most well known progenitors of Six Sigma and clarified the process greatly.

 

DMAIC, DMADV, & Tollgates

 

What are the processes of six sigma?  There are two.  The first is DMAIC, the next is DMADV. Each of these is usually pronounced as word “dah-may-ick” and “dah-mad-vee”.  This is a portion of the many acronyms and specialized language of Lean and Six Sigma that we’ll see as we walk through these processes.  In this section, we’ll focus on DMAIC.

 

DMAIC is the system utilized to improve processes.  The acronym standards for Define, Measure, Analyze, Improve, and Control.  Each represents an important step in the pathway to improving a process, and each step has certain checkpoints which we call tollgates that must be satisfied before progressing to the next step.  Let’s discuss each.

 

As we work our way through each tollgate and step, keep in mind that we haven’t discussed how to choose a project or pathway to improve.  There are many tools and criteria you can use to select which system you want to improve, and DMAIC are those steps to use once you’ve identified the target system.

 

Each Step In DMAIC With Tollgates

 

The D in DMAIC, as mentioned, stands for Define.  The Define phase has many important tollgates as shown beneath, and the first of these is the project charter.  The project charter is a tool that defines the scope of the project involved, the stakeholders (team members for the project from throughout organization) who have a role in its completion, the timeline for completion, and the expected cost savings in terms of the Cost of Poor Quality (COPQ)  that will be recouped.  For more information on the COPQ, look here.

 

From Villanova University's Lean & Six Sigma Black Belt Course (Fall 2011)
From Villanova University’s Lean & Six Sigma Black Belt Course (Fall 2011)

 

 

The next tollgate in the Define phase focuses the team on customer needs and requirements.  As usual in healthcare, we are faced with determining who exactly the customer is.  Is it the patient receiving the care?  Is it the third party payor who reimburses the health system?  Both?

 

The Voice of the Customer (VOC)

 

It is at this point in the Define phase that the customer(s) of the process being quantified is identified, and the customer’s voice is made visible as the Voice of the Customer (VOC).  As mentioned previously this can be very challenging but is central to the Six Sigma system.  The VOC is often used to determine the limits into which the system must fit.  In other words, if customers won’t accept, for some reason, waiting times more than 30 minutes then 30 minutes is the VOC and represents the upper limit for wait times.  Anything greater than that is a defect.

 

Sometimes, of course, the VOC can come from state regulatory bodies, JCAHO, or another accrediting body.  If an accrediting body was going to review a trauma center and published “trauma surgeons must be present within 15 minutes of patient arrival in the trauma bay” then that can be taken as a VOC and any arrival times greater than 15 minutes may be treated as defects.  Yes, they may be reasons which are perfectly valid for arrival time greater than 15 minutes, and the magic of Six Sigma is that it makes us look at how things lined up to get the situation where the surgeon arrives beyond 15 minutes…and it does it without pointing fingers or naming names.

 

The SIPOC Diagram

 

The next toll gate in the defined phase is the process map.  The process map, sometimes called a SIPOC diagram, helps us formalize what each step of the process is and eventually how we will obtain data.  This high-level map includes information regarding Suppliers, Inputs, Processes, Outputs, & Customers and from there we create the acronym SIPOC.  We use the process map to help select what data can and should be measured to characterize the system in the next step of the DMAIC process.

 

Next, in the measure phase, we formulate a data collection plan.  This crystallizes how many data points we will need to detect certain size changes in the system and sets up a statistically valid study.  We decide how and where we will get data, and we focus on a certain number of data elements for each portion of the SIPOC diagram.  Next we measure the process and establish a base line sigma level or CPK value.  This very clearly tells everyone involved how the system is currently performing.  For more information on the CPK visit here.

 

Data Driven Analysis

 

Next is the Analyze phase of the DMAIC pathway and so we focus the team on data analysis.  Data analysis uses the statistical tools of Six Sigma and, like the rest of the process, these take special training.  What is very useful about Six Sigma is that the participants involved will come to similar valid conclusions given similar data because they share a common body of statistics knowledge and those trained in Lean and Six Sigma know how to use the statistical tests to make valid conclusions.

 

The Ishikawa Diagram

 

Another tollgate closely follows.  A process analysis is performed whereby the process is analyzed for potential manners in which it can be revised in light of data, and a root cause analysis is performed with an Ishikawa diagram to determine what the roots are underlying the current state.  At times, a Black Belt or Master Black Belt may utilize a root cause analysis, coupled with a multiple regression, earlier in the process.

 

Six Sigma Statistics Have Ability To Change Our Minds

 

One of the tendencies typically seen in the DMAIC pathway is that staff and team members will jump to the Improve phase prior to the Measure and Analyze phase.  It is very normal, especially in the West, to think that we know what is wrong with the process because we live with it everyday. How can we ever know anything better than living in the system every day?  It’s powerful, intuitive, experiential, and (unfortunately) incorrect in terms of how a system needs improvement.  Healthcare colleagues, I’ve been there:  we work in a system everyday, are sure we know what’s wrong, and when the Six Sigma project data shows us something unexpected, well, it can’t be right.  Then changes are made based on the data and (huh) things improve greatly.  Amazing…guess it was right!

 

To climb up on the soapbox for a minute:  the power of these Six Sigma processes are that they place a cross-disciplinary team on the same page, they make the current state of performance VERY clear to everyone, they are not pejorative, AND they show a business case for why change is worthwhile.  The steps of Six Sigma, in many ways, satisfy all 8 of Kotter’s classic steps for culture change.  The DMAIC process even shows us where our every day experience of a system doesn’t line up with data.  Isn’t that the power of statistics?  To disabuse us when we think we are doing well but we aren’t and to change our minds about the way forward?

 

We have opinions and these are often strong and highly personalized.  In healthcare especially, we see opinions focused on people and the personally assignable portion of issues.  This is only one factor in meaningful quality improvement.  Therefore, I will take this moment to describe that it is important that the team wait until the data is in before jumping to analyse or suggesting solutions. That ensures this process is data driven.  Often, very often in fact, we find that the data have the ability to change our mind about what we thought was wrong, and when we listen to the data to make changes we see meaningful lasting improvement.   (Ok, stepping down off the soapbox.)

 

Rigorous Testing After Improvements Made

 

Next we pass into the Improve phase.  The Improve phase has several toll gates including stakeholder generated solutions.  The stakeholders generate different solutions based on the now visible and shared data.  Again, generating solutions based on the data that are present makes an important difference for not just project outcome but overall quality in the system.  Then, solutions are selected from among candidate solutions based on criteria like how easily they maybe implemented, resource expenditure and impact in the system.  Solutions are implemented and follow-up data is collected to ensure a significant improvement as demonstrated by statistical testing.

 

Things Are Improved, But Let’s Not Forget Maintenance

 

Next, importantly, is the control phase of the DMAIC pathway.  Once a system has been improved, it is important to have ongoing review of the system and oversight with control.  That way, if the system relapses into a poor state of quality, we are aware and can make changes.  Tools like individual moving range charts (ImR charts) and similar techniques are useful as we focus on keeping the improved system under control.  Finally, a response plan is generated so that when the system does give us the signal that it is having quality issues there is a planned response.

 

If You Don’t Have DMAIC, You Probably Just Have An Opinion

 

Above, we have described the DMAIC pathway for Six Sigma.  Remember, the point of this pathway is to promote Six Sigma’s stated goal of achieving six standard deviations of data between the upper and lower spec limit.  That may not have much meaning to you now, but it states that Six Sigma seeks to make great improvements in things like defect rates.  The DMAIC pathway is the overall scheme by which this is performed.  You have seen that this is very different than how we typically function in healthcare in that it is highly data-driven and that personally assignable, opinion-based changes are highly discouraged.  The saying, in Six Sigma, is that “if you don’t have data you just have an opinion.” The DMAIC pathway has certain tollgates which the team progresses through.  These tollgates are steps by which meaningful improvement is achieved and sustained before the final control phase.

 

Clearly the DMAIC pathway is very different than our typical approach to quality control in healthcare, and, as we close this section, let me share with you that it is much more effective.

Blogging A Book: Sharing the Secret of Lean & Six Sigma In Healthcare (Introduction)

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Editor/Author’s note:  Hello All.  As we described earlier on the blog, we’ll be releasing pieces of the e-book we’re writting:  Sharing The Secret of Lean & Six Sigma In Healthcare.  Here’s our rough copy introduction–feedback always welcome.

 

 

 

 

Introduction–Why Bother With The Hunt For Effective Quality Improvement?

 

By:  DM Kashmer, MD MBA FACS

Lean Six Sigma Master Black Belt (MBB)

 

 

Healthcare Is In A Bad Place

 

If you work in or around healthcare you know there are many challenges that we are facing as a group.  After all, we have heard the story of quality challenges in healthcare for the last decade or more.  We have heard the fact, over and over again, that the United States spends more than 14% of its Gross Domestic Product on healthcare each year.  For this amount of expenditure, our quality endpoints are poor at best according to many classic measures.  What has this translated into?  Many of us feel that we are currently in age of cost containment.  What I mean by this is there is a strong focus, now, on decreasing the cost of healthcare to the exclusion of much else.  Some assert that the cry of “quality!” really is being used as a stick to control costs.  You may or may not agree and of course that’s ok–and either way, reading on is worth your time.

 

 

Many Classic Quality Improvement Tools That Could Help Are Unknown Or Shunned

 

Unfortunately, many of the classic tools for quality improvement are either shunned or unknown in healthcare…and that’s why this book exists.  In this book, we explore many of the classic quality tools and describe how they can be deployed effectively in healthcare.  Why do we do this?  This book is written for one simple reason:  improving quality can decrease the amount of costs we endure for poor quality–and when I say “costs”, by the way, I don’t just mean financial costs.  After all, since most service industries operate at one defect in every one thousand opportunities at making a defect (yes I’m talking to you here healthcare) the issues associated with our current performance are many and go far beyond simple finance.  At the end of the day, the Cost of Poor Quality (COPQ) includes things like re-operation, wrong site surgery, and many other problems that go beyond financial considerations.

 

And remember healthcare friends:  just because you don’t know about Lean and Six Sigma (yet) doesn’t mean they don’t work.  In fact, they are very effective and (just like healthcare) they take some expertise and training.  You’ll be even more surprised to hear that much of both Lean and Six Sigma is really just utilizing tools that you’ve heard of such as histograms, multiple regressions, and other classic statistical implements you’ve learned about in healthcare training–it’s just that these are put together and arranged in certain ways to help a group own its process and implement positive change that is measurable and rigorous.

 

Quality Improvement Teaches Us Many Issues Aren’t Just People Issues

 

It’s clear that, if we are able to improve quality, waste in terms of financial waste and other waste is substantially reduced.  Currently, often, we hear the refrain of physician-work-harder or (“doc work harder”).  Don’t get me wrong–I’m no stranger to work and in fact I don’t mind it.  Work is what got me through medical school, residency, and to this point in my life.  However, “doc work harder” is not a valid solution for quality improvement.  It turns out, after all, that many quality issues that healthcare deems “personally assignable” are actually much more multi-factorial.  If we want a real way to improve quality that works, let’s try something different than the blame game we see too often in healthcare.  It divides us as physicians and practitioners.  So let’s listen to the data from the quality improvement world and actually use the tools.

 

 

Externally Imposed Endpoints Would Likely Be Satisfied With An Increased Internal Focus On Quality

 

Also, as a physician, I often feel that my time with patients and attention to detail is limited owing to system issues.  One of the reasons why is that in hospitals, the revenue side is most sensitive to patient volume.  That is, proformas in hospitals are most sensitive to patient volume.  See more patients, treat more patients, and go go go.  As a result, my ability to care for individual patients and spend time is limited.  It is easy to see the impact of volume on the hospital’s bottom line.  However, it’s not so easy to see the impact of the COPQ and this is part of why a quality focus is often brought to healthcare from without (eg the government and things like “never” events or SCIP measures) rather than from within.  If we followed the tools in this book and the philosophy of Lean and Six Sigma, SCIP measures and similar endpoints would be met or exceeded as a consequence.  More on that later.

 

I view quality improvement as one type of investment that allows me to spend more time with patients and treat more patients effectively and compassionately.

 

 

The Sibling of Quality Improvement Is Innovation

 

As I depart the soapbox and we turn toward the tools and tips, let me share that perhaps the brother or sister to quality improvement is innovation.  It’s one thing to refine a system with quality tools and yet it is quite another to build one with quality in mind in the first place.  That is, if we can innovate the business models with which we deliver care, we can perhaps break out of the “volume crunch” that we are currently experiencing in Healthcare and Surgery.

 

 

Better To Work In A System That Sets Us Up For Success

 

Disclaimer here before we go to the tools:  I love to work and operate.  I add that in because, in healthcare and surgical culture, a quality focus or mentioning quality is often taken as a synonym or code-word associated with someone who doesn’t like work.  Let me share with you all that, in fact, a high-quality, high-performing system enables more work to get done with less re-work.  That’s the aim here:  it would be better to be able to deliver higher quality care in a system that’s built to set us up for success.

 

 

Use The Tools–Don’t Reinvent The Wheel

 

Please enjoy this series of quality control tools, and their rationales, applied to healthcare.  Consider using them–don’t re-invent the wheel in the hunt for tools that already exist and work.  These are written up as vignettes with a focus on either a philosophy or tool of the process and its uses.  There is also a focus on innovation, with these tools offered to demonstrate how to take an innovative process and refine it.  After all, if we can innovate business models and evolve streams of revenue that are not sensitive to patient volume, well, we may be able to effectively deliver care and break out of the difficult cycle in which healthcare currently finds itself.  It would be great to be less focused on patient volume and more focused on quality care, innovative means of delivery, and new ideas to decrease the pressure to see more and more patients so we can be able to spend more time delivering higher quality care.

 

–David 5/16/14