Remember when you first heard of the Swiss-Cheese model of medical error? It sounds so good, right? When a bunch of items line up, we get a defect. It’s satisfying. We all share some knowledge of Swiss Cheese–it’s a common image.
That’s what makes it so attractive–and, of course, the Swiss Cheese model is a much better mental model than what came before, which was some more loose concept of a bunch of items that made an error or, worse yet, a profound emphasis on how someone screwed up and how that produced a bad outcome.
Models supplant each other over time. Sun goes around the Earth (geocentric) model was supplanted by the sun at the center (heliocentric) model–thank you Kepler and Copernicus!
Now, we can do better with our model of medical error and defect, because medical errors really don’t follow a Swiss Cheese model. So let’s get a better one and develop our shared model together.
In fact, medical errors are more like Frogger. Now that we have more Millenials in the workplace than ever (who seem to be much more comfortable as digital natives than I am as a Gen Xer), we can use a more refined idea of medical error that will resonate with the group who staff our hospitals. Here’s how medical errors are more like Frogger than Swiss Cheese:
(1) In Swiss Cheese, the holes stay still. That’s not how it is with medical errors. In fact, each layer of a system that a patient passes through has a probability of having an issue. Some layers are lower, and some are higher. Concepts like Rolled Throughput Yieldreflect this and are much more akin to how things actually work than the illusion that we have fixed holes…thinking of holes gives the illusion that, if only we could only identify and plug those holes, life would be perfect!
In Frogger, there are gaps in each line of cars that pass by. We need to get the frog to pass through each line safely and oh, by the way, the holes are moving and sometimes not in the same place. That kind of probabilistic thinking is much more akin to medical errors: each line of traffic has an associated chance of squishing us before we get to the other side. The trick is, of course, we can influence and modify the frequency and size of the holes…well, sometimes anyway. Can’t do that with Swiss Cheese for sure and, with Frogger, we can select Fast or Slower modes. (In real life, we have a lot more options. Sometimes I even label the lines of traffic as the 6M‘s.)
(2) In the Swiss Cheese Model, we imagine a block of cheese sort of sitting there. There’s no inherent urgency in cheese (unless you’re severely lactose intolerant or have a milk allergy I guess). It’s sort of a static model that doesn’t do much to indicate safety.
But ahhh, Frogger, well there’s a model that makes it obvious. If you don’t maneuver the frog carefully that’s it–you’re a goner. Of course, we have the advantage of engineering our systems to control the flow of traffic and both the size and presence of gaps. We basically have a cheat code. And, whether your cheat code is Lean, Lean Six Sigma, Six Sigma, Baldrige Excellence Framework, ISO, Lean Startup, or some combination…you have the ultimate ability unlike almost any Frogger player to change the game to your patient’s advantage. Of course, unlike Frogger, your patient only gets one chance to make it through unscathed–that’s very different than the video game world and, although another patient will be coming through the system soon, we’ll never get another chance to help the current patient have a perfect experience.
All of that is highlighted by Frogger and is not made obvious by a piece of cheese.
(3) In Frogger, the Frog starts anywhere. Meaning, not only does the traffic move but the Frog starts anywhere along the bottom of the screen. In Frogger we can control that position, but in real life the patients enter the system with certain positions we can not control easily and, for the purposes of their hospital course anyway, are unable to be changed. It may be their 80 pack year history of smoking, their morbid obesity, or their advanced age. However, the Frogger model recognizes the importance of initial position (which unlike real life we can control more easily) while the Swiss Cheese model doesn’t seem to make clear where we start. Luckily, in real life, I’ve had the great experience of helping systems “cheat” by modifying the initial position…you may not be able to change initial patient comorbid conditions but you can sometimes set them up with a better initial position for the system you’re trying to improve.
Like you, I hear about the Swiss Cheese model a lot. And, don’t get me wrong, it’s much better than what came before. Now, however, in order to recognize the importance of probability, motion, initial position, devising a safe path through traffic, and a host of other considerations, let’s opt for a model that recognizes uncertainty, probability, and safety. With more Millenials than ever in the workplace (even though Frogger predates them!) we have digital natives with whom game imagery is much more prone to resonate than a static piece of cheese.
Use Frogger next time you explain medical error because it embodies how to avoid medical errors MUCH better than cheese.
Dr. David Kashmer, a trauma and acute care surgeon, is a Fellow of the American College of Surgeons and is a nationally known healthcare expert. He serves as a member of the Board of Reviewers for the Malcolm Baldrige National Quality Award. In addition to his Medical Doctor degree from MCP Hahnemann University, now Drexel University College of Medicine, he holds an MBA degree from George Washington University. He also earned a Lean Six Sigma Master Black Belt Certification from Villanova University. Kashmer contributes to TheHill.com, Insights.TheSurgicalLab.com, and The Healthcare Quality Blog.com where the focus is on quality improvement and value in surgery and healthcare.
In America, we long ago declared that people are created with rights. Among those are Life, Liberty, and the Pursuit of Happiness. Our famous Declaration of Independence states that those well-known rights are some among others. If our Declaration were penned today, what contemporary ideas would be enshrined in the document? Would a modern Declaration list a right to healthcare among our unalienable rights?
I share this question, which I’ve asked to more than 223 (and counting) medical students and resident surgeons, to highlight a fundamental issue in America—and I won’t share my own answer. The point of the exercise here, in fact, is to put forward certain thoughts on the rights we all accept and whether there is another potential right that we don’t seem to have a consensus about as Americans.
On one hand, how exactly can we pursue a meaningful life, personal liberty, or our happiness without health? Does it lurk behind our other unalienable rights as a necessary pre-condition? If we are unable to be mobile, to experience life, or to pursue our liberty…well, isn’t health and healthcare a necessary pre-requisite “unalienable”? Would Mr. Jefferson include that in his list if he were to write it today?
Yes, asking the question like that, out of historical context, suffers from some real problems. Mr. Jefferson did not write in modern times and of course the document would be different. Times are different now as are the related issues. Healthcare is remarkably different than the field that lead to President Washington’s wooden teeth. (By the way, in fact that is a myth about George Washington. His dentures were not made of wood. Look here.)
Anyhow, the issue is not really about whether Mr. Jefferson would include healthcare in a modern rewrite of the Declaration of Independence. That question is a device used to frame a conversation. Would we make healthcare a right if we created a list of our key rights today? If humans have certain rights, and only some inalienable examples are listed in our Declaration but not all of them (remember “among these are Life, Liberty, and the Pursuit of Happiness…) would we list healthcare as another item if we revisited the list? It is the spirit of the question, and the conversation it creates, which makes me ask many of the healthcare providers with whom I work.
Of the 223 I’ve asked, the overwhelming majority have answered that yes, healthcare should be considered a right…but then things get messy. Conversation usually turns to a related question: “How much healthcare is a right?” All of it? Anything we want as patients (even if some type of care won’t do anything to help us pursue life, liberty, or happiness…) despite the healthcare provider’s judgment on efficacy? Should it be every extraordinary skill we have in modern medicine?
The conversation gets complicated, and staff consider with me the various complexities of considering healthcare as a right. Participants start to wonder what’s worse: an overly paternalistic physician deciding what’s best for the patient with a devil-may-care-what-the patient-thinks attitude (Dr. House!), or when we as patients demand anything (and everything) without really understanding how a treatment, unlikely to help, will drain the system.
Back when healthcare consisted of bleeding patients with leeches, this conversation was probably a whole lot easier and less complex! After all, when treatments were ineffective and cheap, well, it wouldn’t really be an issue to consider in writing your Declaration to King George. After all, wooden teeth and leeches don’t really do much anyways.
In fact, the conversation sometimes gets even worse. If we have time, and aren’t interrupted by a critically injured trauma patient arriving in the ER, sometimes we wonder about another important parameter of the discussion: “Is healthcare a right if it costs so much that it cripples your country’s finances? What if it’s so costly that it affects whether your society as a whole can pursue life, liberty, and its happiness?” Difficult question. It goes to the balance of individuals’ rights versus the rights of society. That’s never easy.
Right now, in the US, we spend an outrageous amount on healthcare, especially for the quality outcomes we see in terms of our longevity and infant mortality measures. By far, year after year, we spend a larger percentage of our GDP on healthcare than any other country.
Fellow citizens, this is exactly where we stand: a fundamental struggle between whether or not healthcare is a right, and, if so, how much? This issue reverberates, I think, throughout policy choices and current town halls across the US. Its consequences reach from healthcare insurance company board rooms to the halls of Congress to my own dinner table when family wants to discuss. Now, we see it in the current discussion of Obamacare versus the GOP offering of what comes next.
Do we force insurers to cover people who have legitimate issues that put them at a higher risk to those insurance companies, and make the companies do that at inexpensive prices? Do we revamp the system and attempt to foster individual responsibility for healthcare in an attempt to cut costs? Do we mandate that individuals buy insurance? Is it to be an individual solution to our healthcare issue or do society and government solve the issue? Is healthcare a right or a privilege and, if it’s a right, how much is a right?
Now I’ll share with you all how I resolve this every day in my practice as a surgeon. (Shhhh, don’t tell…)
…I don’t. I don’t solve it at all. I don’t even offer a solution.
Here’s what I do: I respect patient autonomy. I teach patients (or their proxy if the patient can’t understand or tell me what they want) and they decide what they want to do. I arm them with the relevant knowledge (as much as I can without giving them a medical education) and I ask them what they want. I do that at 3am and 3pm and every hour in-between. And I make a recommendation usually too just to let them know my thoughts. Then they decide based on what we can do and how likely it is to help them. The question is which of the options is worth it to them based on where we can predict it will get them and what they’ll need to go through to get there.
Myself and the team I’m on don’t look at whether any patient is insured and nor do we care. That’s how I do it. And even if I think the decision a patient makes is not the one I would make or recommend, we execute their plan and continue to help them. They are the boss even when the situation is difficult, great, or something else.
Usually, that clears the situation up. I understand when I read literature that paints physicians as custodians of resources and expensive tests. After all, our country has a huge problem with healthcare costs. However, the patient is the ultimate arbiter of their healthcare. It seems to me to be a strange place to be to ask the surgeon to indirectly manage the costs of healthcare and other society-level issues and yet focus clearly on the interests of the patient.
Take a minute and think about doing that. Now think about doing it at 2am. Now think about doing that many times over. Now imagine doing that with a patient who is critically ill and meeting you for the first time. It’s a tough at bat every time. That’s what we do. I want you to know that because where the rubber meets the road on this discussion is typically when you meet someone like me at 2AM in an Emergency Department, and we are forced (in our first meeting) to discuss whether your family member with late stage cancer would want a surgical procedure for an acute problem…even though fixing that problem won’t improve their quality and quantity of life. They can’t tell me because they’re “out of it” and so I turn to you as their proxy.
After I educate the patient (or you) about what can be done, I share how likely we are to improve the situation with a particular treatment. I even make a recommendation. But remember, your surgeon is up to bat at many hours of the day and night. And we are at bat a lot in situations where you or your family is critically ill and sometimes near death. It’s challenging to manage all those things in shaking your family member’s hand for the first time when time is of the essence. I can usually help give you a sense of how likely something is to help you, but imagine how that conversation would go if I went on to say “it’ll help you some, but it’s really expensive.” Ouch. It’s not a great idea to put cost management on the surgeon.
Really tough to balance the probabilities of a treatment helping you, the effort required on your part, and then asking you to balance whether it’s worth it for the cost. Tough to ask you to do that. Tough to ask me to do it. Especially if you, the patient, are really sick.
This article calls upon all of us, comfortable now in normal hours instead of in a difficult situation at a 2AM Emergency Department, to begin to make up our mind as a country about whether (and how much) healthcare is a right. In fact, the time we should decide is before we are ever faced with such a terrible decision.
Nowadays, our current state is that we do the best we can. It would help us a lot to have clarity on the topic of whether healthcare is a right or a privilege because it would make what we can do for you much more clear. The clarity of black and white, not the gray of indecision, helps us a great deal in achieving the bright lights and cold steel of the operating room should that be what you need.
My resolution for all of these complex issues is to educate whenever possible and execute the choice you make about your care. I’ve never met a patient where my concern is whether you have insurance or whether I can save money on your care. I don’t know what that patient looks like in whom I could apply the society level issues to individual care. Would that patient look like my daughter? My parent? So where I have resolved this issue in my practice by arming you with what I know in a situation, making a recommendation, and then respecting your decision, it sure would help if our society writ large would solve some of these issues. It would help at 3AM in the Emergency Department and it would help as we look to revise Obamacare.
There are cost-savings opportunities in healthcare. Lots of them. Importantly, many are not rooted in the individual conversations between patient and their doctors, and instead flow from system-level waste. With my quality improvement hat on, I can share that it’s a good idea to build better systems rather than rely on one-off conversations at odd hours that vary greatly from case to case if we have an interest in eliminating waste in our system.
Whichever approach you like to improving healthcare, a consensus on whether, and how much, healthcare is a right would make it much easier. Our indecision as Americans, I think, lurks behind our current situation and many of the interactions in healthcare every day.
So on a day to day basis with each patient, one after the next, I do not resolve the issue of whether it’s Obamacare, a GOP plan, healthcare as a right or healthcare as a privilege…but I ask that question about the Declaration to prompt discussion. I’ve asked more than 223 times now. Because, as I see every day at work, the fact that we as Americans struggle with this fundamental issue affects so much in the lives of people and their families. There is no easy answer, yet now is the time to realize for our own goods that we are called to action to solve our healthcare issue as a country in order to pursue our happiness. It is time to work to enshrine our thoughts on where and how exactly healthcare fits in our lives. Let’s get this done to make our next 3AM at bat, whether as doctor, nurse, or patient much better for everyone. Should Mr. Jefferson have one more item on his list?
http://bit.ly/2k8TDNR This episode describes some of the keys for easy collection of meaningful data. Do you find yourself so busy that data collection is difficult? (Like trying to eat wings and watch the game?) Then this is the episode for you!
Of the many barriers we face while trying to improve quality in healthcare, none is perhaps more problematic than the lack of good data. Although everyone seems to love data (I see so much written about healthcare data) it is often very tough to get. And when we do get it, much of the data we get are junk. It’s not easy to make meaningful improvements based on junk data. So, what can we do to get meaningful data for healthcare quality improvement?
In this entry, I’ll share some tools, tips, & techniques for getting meaningful quality improvement data from your healthcare system. I’ll share how to do that by telling a story about Super Bowl LI…
The Super Bowl Data Collection
About ten minutes before kickoff, I had a few questions about the Super Bowl. I was wondering if there was a simple way to gauge the performance of each team and make some meaningful statements about that performance.
When we do quality improvement projects, it’s very important to make sure it’s as easy as possible to collect data. I recommend collecting data directly from the process rather than retrospectively or from a data warehouse. Why? For one, I was taught that the more filters the data pass through the more they are cleaned up or otherwise altered. They tend to lose fidelity and a direct representation of the system. Whether you agree or not, my experience has definitely substantiated that teaching.
The issue with that is how do I collect data directly from the system? Isn’t that cumbersome? We don’t have staff to collect data (!) Like you, I’ve heard each of those barriers before–and that’s what makes the tricks and tools I’m about to share so useful.
So back to me then, sitting on my couch with a plate of wings and a Coke ready to watch the Super Bowl. I wanted data on something that I thought would be meaningful. Remember, this wasn’t a DMAIC project…it was just something to see if I could quickly describe the game in a meaningful way. It would require me to collect data easily and quickly…especially if those wings were going to get eaten.
Decide Whether You’ll Collect Discrete or Continuous Data
So as the first few wings disappeared, I decided about what type of data I’d want to collect. I would definitely collect continuous data if at all possible. (Not discrete.) That part of the deciding was easy. (Wonder why? Don’t know the difference between continuous and discrete data? Look here.)
Ok, the next issue was these data had to be very easy for me to get. They needed to be something that I had a reasonable belief would correlate with something important. Hmmm…ok, scoring touchdowns. That’s the whole point of the game after all.
Get A Clear Operational Definition Of What You’ll Collect
As wings number three and four disappeared, and the players were introduced, I decided on my data collection plan:
collect how far away each offense was from scoring a touchdown when possession changed
each data point would come from where ball was at start of 4th down
interceptions, fumbles, or change of possession (like an interception) before 4th down would NOT recorded (I’ll get to why in a minute.)
touchdowns scored were recorded as “0 yards away”
a play where a field goal was attempted would be recorded as the where the ball was on the start of the down
Of course, for formal quality projects, we would collect more than just one data point. Additionally, we specify exactly the operational definition of each endpoint.
We’d also make a sample size calculation. Here, however, I intended to collect every touchdown and change of possession where a team kicked away on fourth down or went for it but didn’t make it. So this wasn’t a sample of those moments. It was going to be all of them. Of course, they don’t happen that often. That was a big help here, because they can also be anticipated. That was all very important so I could eat those wings.
Items like interceptions, fumbles, and other turnovers can not be anticipated as easily. They also would make me have to pay attention to where the ball was spotted at the beginning of every down. It was tough enough to pay attention to the spot of the ball for the downs I was going to record.
With those rules in mind, I set out to record the field position whenever possession changed. I thought that the position the offense wound up its possession at, over time, might correlate with who won the game. Less overall variance in final position might mean that team had less moments where it under-performed and lost possession nearer to its own endzone.
Of course, it could also mean that the team never reached the endzone for a touchdown. In fact, if the offense played the whole game between their opponents 45 and 50 yard line it would have little variation in field position…but also probably wouldn’t score much. Maybe a combination of better field position (higher median field position) and low variation in field position would indicate who won the game. I thought it might. Let’s see if I was right.
Data Collection: Nuts and Bolts
Next, I quickly drew a continuous data collection sheet. It looked like this:
Sounds fancy, but obviously it isn’t. That’s an important tool for you when you go to collect continuous data right from your process: the continuous data collection sheet can be very simple and very easy to use.
Really, that was about it. I went through the game watching, like you, the Patriots fall way behind for the first two quarters. After some Lady Gaga halftime show (with drones!) I looked at the data and noticed something interesting.
The Patriots data on distance from the endzone seemed to demonstrate less variance than the Falcons. (I’ll show you the actual data collection sheets in a moment.) It was odd. Yes, they were VERY far behind. Yes there had been two costly turnovers that lead to the Falcons opening up a huge lead. But, strangely, in terms of moving the ball and getting closer to the endzone based on their own offense, the Patriots were actually doing better than the Falcons. Three people around me pronounced the Patriots dead and one even said we should change the channel.
If you’ve read this blog before, you know that one of the key beliefs it describes is that data is most useful when it can change our minds. These data, at least, made me unsure if the game was over.
As you know (no spoiler alert really by now) the game was far from over and the Patriots executed one of the most impressive comebacks (if not the most impressive) in Super Bowl history. Data collected and wings eaten without difficulty! Check and check.
Here are the final data collection sheets:
Notice the number in parenthesis next to the distance from the endzone when possession changed? That number is the possession number the team had. So, 52(7) means the Falcons were 52 yards away from the Patriots endzone when they punted the ball on their seventh possession of the game. An entry like 0(10) would mean that the team scored a touchdown (0 yards from opposing team’s endzone) on their tenth possession.
Notice that collecting data this way and stacking similar numbers on top of each other makes a histogram over time. That’s what let me see how the variation of the Patriot’s final field position was smaller than the Falcon’s by about halfway through the game.
Anything To Learn From The Data Collection?
Recently, I put the data into Minitab to see what I could learn. Here are those same histograms for each offense’s performance:
Notice a few items. First, each set of data do NOT deviate from the normal distribution per the Anderson-Darling test. (More info on what that means here.) However, a word of caution: there are so few data points in each set that it can be difficult to tell which distribution they follow. I even performed distribution fitting to demonstrate that testing will likely show that these data do not deviate substantially from other distributions either. Again, it’s difficult to tell a difference because there just aren’t that many possessions for each team in a football game. In a Lean Six Sigma project, we would normally protect against this with a good sampling plan as part of our data collection plan but, hey, I had wings to eat! Here’s an example of checking the offense performance against other data distributions:
Just as with the initial Anderson-Darling test, we see here that the data do not deviate from many of these other distributions either. Bottom line: we can’t be sure which distribution it follows. Maybe the normal distribution, maybe not.
In any event, we are left with some important questions. Notice the variance exhibited by the Patriots offense versus the Falcons offense: this highlights that the Patriots in general were able to move the ball closer to the Falcons endzone by the time the possession changed (remember that turnovers aren’t included). Does that decreased variation correlate with the outcome of every football game? Can it be used to predict outcomes of games? I don’t know…at least not yet. After all, if stopping a team inside their own 10 yard line once or twice was a major factor in predicting who won a game, well, that would be very useful! If data is collected by the league on field position, we could apply this idea to previous games (maybe at half time) and see if it predicts the winner routinely. If it did, we could apply it to future games.
In the case of Super Bowl LI, the Patriots offense demonstrated a better median field position and less variation in overall field position compared to the Falcons.
Of course, remember this favorite quote:
All models are wrong, but some are useful. — George E.P. Box (of Box-Cox transform fame)
Final Recommendations (How To Eat Wings AND Collect Data)
More importantly, this entry highlights a few interesting tools for data collection for your healthcare quality project. At the end of the day, in order to continue all the things you have to do and collect good data for your project, here are my recommendations:
(1) get data right from the process, not a warehouse or after it has been cleaned.
(2) use continuous data!
(3) remember the continuous data check sheet can be very simple to set up and use
(4) when you create a data collection plan, remember the sample size calculation & operational definition!
(5) reward those who collect data…maybe with wings!
In the last entry, you saw a novel, straightforward metric to capture the value provided by a healthcare service called the Healthcare Value Process Index (HVPI). In this entry, let’s explore another example of exactly how to apply the metric to a healthcare service to demonstrate how to use the index.
At America’s Best Hospital, a recent quality improvement project focused on time patients spent in the waiting room of a certain physician group’s practice. The project group had already gone through the steps of creating a sample plan and collecting data that represents how well the system is working.
From a patient survey, sent out as part of the project, the team learned that patients were willing to wait, at most, 20 minutes before seeing the physician. So, the Voice of the Customer (VOC) was used to set the Upper Specification Limit (USL) of 20 minutes.
A normality test (the Anderson-Darling test) was performed, and the data collected follow the normal distribution as per Figure 1 beneath. (Wonder why the p >0.05 is a good thing when you use the Anderson-Darling test? Read about it here.)
The results of the data collection and USL were reviewed for that continuous data endpoint “Time Spent In Waiting Room” and were plotted as Figure 2 beneath.
The Cpk value for the waiting room system was noted to be 0.20, indicating that (long term) the system in place would produce more that 500,000 Defects Per Million Opportunities (DPMO) with the accompanying Sigma level of < 1.5. Is that a good level of performance for a system? Heck no. Look at how many patients wait more than 20 minutes in the system. There’s a quality issue there for sure.
What about the Costs of Poor Quality (COPQ) associated with waiting in the waiting room? Based on the four buckets of the COPQ, your team determines that the COPQ for the waiting room system (per year) is about $200,000. Surprisingly high, yes, but everyone realizes (when they think about it) that the time Ms. Smith fell in the waiting room after being there 22 minutes because she tried to raise the volume on the TV had gotten quite expensive. You and the team take special note of what you items you included from the Profit and Loss statement as part of the COPQ because you want to be able to go back and look after changes have been made to see if waste has been reduced.
In this case, for the physician waiting room you’re looking at, you calculate the HVPI as
(100)(0.20) / (200) or 0.1
That’s not very good! Remember, the COPQ is expressed in thousands of dollars to calculate the HVPI.
Just then, at the project meeting to review the data, your ears perk up when a practice manager named Jill says: “Well our patients never complain about the wait in our waiting room which I think is better than that data we are looking at. It feels like our patients wait less than 20 minutes routinely, AND I think we don’t have a much waste in the system. Maybe you we could do some things like we do them in our practice.”
As a quality improvement facilitator, you’re always looking for ideas, tools, and best practices to apply in projects like this one. So you and the team plan to look in on the waiting room run by the practice manager.
Just like before, the group samples the performance of the system. It runs the Anderson-Darling test on the data and they are found to be normally distributed. (By the way, we don’t see that routinely in waiting room times!)
Then, the team graphs the data as beneath:
Interestingly, it turns out that this system has a central tendency very similar to the first waiting room you looked at–about 18 minutes. Jill mentioned how most patients don’t wait more than 18 minutes and the data show that her instinct was spot on.
…but, you and the team notice that the performance of Jill’s waiting room is much worse than the first one you examined. The Cpk for that system is 0.06–ouch! Jill is disappointed, but you reassure her that it’s very common to see that how we feel about a system’s performance doesn’t match the data when we actually get them. (More on that here.) It’s ok because we are working together to improve.
When you calculate the COPQ for Jill’s waiting room, you notice that (although the performance is poor) there’s less as measured by the costs to deliver that performance. The COPQ for Jill’s waiting room system is $125,000. (It’s mostly owing to the wasted time the office staff spend trying to figure out who’s next, and some other specifics to how they run the system.) What is the HVPI for Jill’s waiting room?
(100)(0.06) / (125) = 0.048
Again, not good!
So, despite having lower costs associated with poor quality, Jill’s waiting room provides less value for patients than does the first waiting room that you all looked at. It doesn’t mean that the team can’t learn anything from Jill and her team (after all, they are wasting less as measured by the COPQ) but it does mean that both Jill’s waiting room and the earlier one have a LONG way to go to improve their quality and value!
Fortunately, after completing the waiting room quality improvement project, the Cpk for the first system studied increased to 1.3 and Jill’s waiting room Cpk increased to 1.2–MUCH better. The COPQ for each system decreased to $10,000 after the team made changes and went back to calculate the new COPQ based on the same items it had measured previously.
The new HVPI (with VOC from the patients) for the first waiting room? That increased to 13 and the HVPI for Jill’s room rose to 12. Each represents an awesomeincrease in value to the patients involved. Now, of course, the challenge is to maintain those levels of value over time.
This example highlights how value provided by a system by a healthcare system for any continuous data endpoint can be calculated and compared across systems. It can be tracked over time to demonstrate increases. The HVPI represents a unique value measure comprised of a system capability measure and the costs of poor quality.
Questions or thoughts about the HVPI? Let me know & let’s discuss!
You’ve probably heard the catchphrase “volume to value” to describe the current transition in healthcare. It’s based on the idea that healthcare volume of services should no longer be the focus when it comes to reimbursement and performance. Instead of being reimbursed a fee per service episode (volume of care), healthcare is transitioning toward reimbursement with a focus on value provided by the care given. The Department of Health and Human Services (HHS) has recently called for 50% or more of payments to health systems to be value-based by 2018.
Here’s a recent book I completed on just that topic: Volume to Value. Do you know what’s not in that book, by the way? One clear metric on how exactly to measure value across services! That matters because, after all
If you can’t measure it, you can’t manage it. –Peter Drucker
An entire book on value in healthcare and not one metric which points right to it! Why not? (By the way, some aren’t sure that Peter Drucker actually said that.)
Here’s why not: in healthcare, we don’t yet agree on what “value” means. For example, look here. Yeesh, that’s a lot of different definitions of value. We can talk about ways to improve value by decreasing cost of care and increasing value, but we don’t have one clear metric on value (in part) because we don’t yet agree on a definition of what value is.
In this entry, I’ll share a straightforward definition of value in healthcare and a straightforward metric to measure that value across services. Like all entries, this one is open for your discussion and consideration. I’m looking for feedback on it. An OVID, Google, and Pubmed search revealed nothing similar to the metric I propose beneath.
First, let’s start with a definition of value. Here’s a classic, arguably the classic, from Michael Porter (citation here).
Ok so there are several issues that prevent us from easily applying this definition in healthcare. Let’s talk about some of the barriers to making something measurable out of the definition. Here are some now:
(1) Remarkably, we often don’t know how much (exactly) everything costs in healthcare. Amazing, yes, but nonetheless true. With rare exception, most hospitals do not know exactly how much it costs to perform a hip replacement and perform the after-care in the hospital for the patient. The time spent by FTE employees, the equipment used, all of it…nope, they don’t know. There are, of course, exceptions to this. I know of at least one health system that knows how much it costs to perform a hip replacement down to the number and amount of gauze used in the OR. Amazing, but true.
(2) We don’t have a standardized way for assessing health outcomes. There are some attempts at this, such as QALYs, but one of the fundamental problems is: how do you express quality in situations where the outcome you’re looking for is different than quality & quantity of life? The QALY measures outcome, in part, in years of life, but how does that make sense for acute diseases like necrotizing soft tissue infections that are very acute (often in patients who won’t be alive many more years whether the disease is addressed or not), or other items to improve like days on the ventilator? It is VERY difficult to come up with a standard to demonstrate outcomes–especially across service lines.
(3) The entity that pays is not usually the person receiving the care. This is a huge problem when it comes to measuring value. To illustrate the point: imagine America’s Best Hospital (ABH) where every patient has the best outcome possible.
No matter what patient with what condition comes to the ABH, they will have the BEST outcome possible. By every outcome metric, it’s the best! It even spends little to nothing (compared to most centers) to achieve these incredible outcomes. One catch: the staff at ABH is so busy that they just never write anything down. ABH, of course, would likely not be in business for long. Why? Despite these incredible outcomes for patients, ABH would NEVER be re-imbursed. This thought experiment shows that valuable care must somehow include not just the attention to patients (the Voice of the Patient or Voice of the Customer in Lean & Six Sigma parlance), but also to the necessary mechanics required to be reimbursed by the third party payors. I’m not saying whether it’s a good or bad thing…only that it simply is.
So, where those are some of the barriers to creating a good value metric for healthcare, let’s discuss how one might look. What would be necessary to measure value across different services in healthcare? A useful value metric would
(1) Capture how well the system it is applied to is working. It would demonstrate the variation in that system. In order to determine “how well” the system is working, it would probably need to incorporate the Voice of the Customer or Voice of the Patient. The VOP/VOC often is the upper or lower specification limit for the system as my Lean Six Sigma and other quality improvement colleagues know. The ability to capture this performance would be key to represent the “health outcomes” portion of the definition.
(2) Be applicable across different service lines and perhaps even different hospitals. This requirement is very important for a useful metric. Can we create something that captures outcomes as disparate as time spent waiting in the ER and something like patients who have NOT had a colonoscopy (but should have)?
(3) Incorporate cost as an element. This item, also, is required for a useful metric. How can we incorporate cost if, as said earlier, most health systems can’t tell you exactly how much something costs?
With that, let’s discuss the proposed metric called the “Healthcare Value Process Index”:
Healthcare Value Process Index = (100) Cpk / COPQ
where Cpk = the Cpk value for the system being considered, COPQ is the Cost of Poor Quality for that same system in thousands of dollars, and 100 is an arbitrary constant. (You’ll see why that 100 is in there under the example later on.)
Yup, that’s it. Take a minute with me to discover the use of this new value metric.
First, Cpk is well-known in quality circles as a representation of how capable a system is at delivering a specified output long term. It gives a LOT of useful information in a tight package. The Cpk, in one number, describes the number of defects a process is creating. It incorporates the element of the Voice of the Patient (sometimes called the Voice of the Customer [VOC] as described earlier) and uses that important element to define what values in the system are acceptable and which are not. In essence, the Cpk tells us, clearly, how the system is performing versus specification limits set by the VOC. Of course, we could use sigma levels to represent the same concepts.
Weaknesses? Yes. For example, some systems follow non-normal data distributions. Box-Cox transformations or other tools could be used in those circumstances. So, for each Healthcare Value Process Index, it would make sense to specify where the VOC came from. Is it a patient-defined endpoint or a third party payor one?
That’s it. Not a lot of mess or fuss. That’s because when you say the Cpk is some number, we have a sense of the variation in the process compared to the specification limits of the process. We know how whatever process you are talking about is performing, from systems as different as time spent peeling bananas to others like time spent flying on a plane. Again, healthcare colleagues, here’s the bottom line: there’s a named measure for how well a system represented by continuous data (eg time, length, etc.) is performing. This system works for continuous data endpoints of all sorts. Let’s use what’s out there & not re-invent the wheel!
(By the way, wondering why I didn’t suggest the Cp or Ppk? Look here & here and have confidence you are way beyond the level most of us in healthcare are with process centering. Have a look at those links and pass along some comments on why you think one of those other measures would be better!)
Ok, and now for the denominator of the Healthcare Value Process Index: the Cost of Poor Quality. Remember how I said earlier that health systems often don’t know exactly how much services cost? They are often much more able to tell when costs decrease or something changes. In fact, the COPQ captures the Cost of Poor Quality very well according to four buckets. It’s often used in Lean Six Sigma and other quality improvement systems. With a P&L statement, and some time with the Finance team, the amount the healthcare system is spending on a certain system can usually be sorted out. For more info on the COPQ and 4 buckets, take a look at this article for the Healthcare Financial Management Association. The COPQ is much easier to get at than trying to calculate the cost of an entire system. When the COPQ is high, there’s lots of waste as represented by cost. When low, it means there is little waste as quantified by cost to achieve whichever outcome you’re looking at.
So, this metric checks all the boxes described earlier for exactly what a good metric for healthcare value would look like. It is applicable across service lines, captures how well the system is working, and represents the cost of the care that’s being rendered in that system. Let’s do an example.
Pretend you’re looking at a sample of the times that patients wait in the ER waiting room. The Voice of the Customer says that patients, no matter how not-sick they may seem, shouldn’t have to wait any more than two hours in the waiting room.
Of course, it’s just an example. That upper specification limit for wait time could have been anything that the Voice of the Customer said it was. And, by the way, who is the Voice of the Customer that determined that upper spec limit? It could be a regulatory agency, hospital policy, or even the director of the ER. Maybe you sent out a patient survey and the patients said no one should ever have to wait more than two hours!)
When you look at the data you collected, you find that 200 patients came through the ER waiting room in the time period studied. That means 2 defects per 200 opportunities, which is a DPMO (Defects Per Million Opportunities) of 10,000. Let’s look at the Cpk level associated with that level of defect:
Ok, that’s a Cpk of approximately 1.3 as per the table above. Now what about the costs?
We look at each of the four buckets associated with the Cost of Poor Quality. (Remember those four buckets?) First, the surveillance bucket: an FTE takes 10 minutes of their time every shift to check how long people have been waiting in the waiting room. (In real life, there are probably more surveillance costs than this.) Ok, so those are the costs required to check in on the system because of its level of function.
What about the second bucket, the cost of internal failures? That bucket includes all of the costs associated with issues that arise in the system but do not make it to the patient. In this example, it would be the costs attributed to problems with the amount of time a person is in the waiting room that don’t cause the patient any problems. For example, were there any events when one staff member from the waiting room had to walk back to the main ED because the phone didn’t work and so they didn’t know if it was time to send another patient back? Did the software crash and require IT to help repair it? These are problems with the system which may not have made it to the patient and yet did have legitimate costs.
The third bucket, often the most visible and high-profile, includes the costs associated with defects that make it to the patient. Did someone with chest pain somehow wind up waiting in the waiting room for too long, and require more care than they would have otherwise? Did someone wait more than the upper spec limit and then the system incurred some cost as a result? Those costs are waste and, of course, are due to external failure of waiting too long.
The last bucket, my favorite, is the costs of prevention. As you’ve probably learned before, this is the only portion of the COPQ that generates a positive Return On Investment (ROI) because money spent on prevention usually goes very far toward preventing many more costs downstream. In this example, if the health system spent money on preventing defects (eg some new computer system or process that freed up the ED to get patients out of the waiting room faster) that investment would still count in the COPQ and would be a cost of prevention. Yes, if there were no defects there would be no need to spend money on preventative measures; however, again, that does not mean funds spent on prevention are a bad idea!
After all of that time with the four buckets and the P&L, the total COPQ is discovered to be $325,000. Yes, that’s a very typical size for many quality improvement projects in healthcare.
Now, to calculate the Healthcare Value Process Index, we take the system’s performance (Cpk of 1.3), multiple it by 100, and divide by 325. We see a Healthcare Value Process Index of 0.4. We carefully remember that the upper spec limit was 120 and came from the VOC who we list when we report it out. The 100 is there to make the results easier to remember. It simply changes the size of the typical answer we get to something that’s easier to remember.
We would report this Healthcare Value Process Index as “Healthcare Value Process Index of 0.4 with VOC of 120 min from state regulation” or whomever (whichever VOC) gave us the specification limits to calculate the Cpk. Doing that allows us to compare a Healthcare Value Process Index from institution to institution, or to know when they should NOT be compared. It keeps it apples to apples!
Now imagine the same system performing worse: a Cpk of 0.7. It even costs more, with a COPQ of 425,000. The Healthcare Value Process Index (HVPI)? That’s 0.0165. Easy to see it’s bad!
How about a great system for getting patient screening colonoscopies in less that a certain amount of time or age? It performs really well with a Cpk of 1.9 (wow!) and has a COPQ of $200,000. It’s HVPI? That’s 0.95. Much better than those other systems!
Perhaps even more useful than comparing systems with the HVPI is tracking the HVPI for a service process. After all, no matter what costs were initially assigned to a service process, watching them change over time with improvements (or worsening of the costs) would likely prove more valuable. If the Cpk improves and costs go down, expect a higher HVPI next time you check the system.
At the end of the day, the HVPI is a simple, intuitive, straightforward measure to track value across a spectrum of healthcare services. The HVPI helps clarify when value can (and can not) be compared across services. Calculating the HVPI requires knowledge of system capability measures and clarity in assigning COPQ. Regardless of initial values for a given system and different ways in which costs may be assigned, trending HVPI may be more valuable to track the trend of value for a given system.
Questions? Thoughts? Hate the arbitrary 100 constant? Leave your thoughts in the comments and let’s discuss.
How would you evaluate a healthcare quality improvement program? Let’s say you’re looking at your healthcare system’s process improvement system and wondering “How good are we at process improvement?” How would you know just how well the quality system was performing?
I’ve sometimes heard this called “PI-ing the PI”, and it makes sense–after all, the idea of building a quality system even extends to learning how well the process improvement (PI) system works.
In the many systems I’ve either worked in, helped design, or have consulted for I’ve found the question of “How good are we at PI?” can often be boiled down to a matter of efficiency and effectiveness.
This dimension of the PI process can be thought of as how little waste there is in the PI process. What is the cycle time from issue identification until closure? How much paper & cost does the PI process incur? Do projects take more than 120 days?
The efficiency question is very difficult to answer in healthcare process improvement, and I think that’s because our systems are not so well developed yet as to have many benchmarks for how long things should take from identification until closure (for example). I often use three months (90 days) as the median time from issue identification to closure, because there are a few papers that cite that number for formal DMAIC projects.
Now, there are a few important statements here: (1) when I say 90 days to issue closure I mean meaningful closure & (2) if 90 days is a median target…what’s the variance of the population?
Let me explain a bit: Lean Six Sigma practitioners are often comfortable with thinking of continuous variables as a distribution with a measure of variance (like range or standard deviation) to indicate just how wide the population at hand is. Quality projects often focus on decreasing the standard deviation to make sure things go better in general. This same approach can be used to “PI the PI” effectiveness. What is the standard deviation of how long it takes to identify and close out an issue for the PI system, for example? How can it be reduced?
These are some of the key questions when it comes to measuring the efficiency of the PI system.
This dimension is, arguably, more important than efficiency. For example, imagine working really hard to decrease the amount of time it takes someone to throw something away. Yup, imagine working hard on improving how well someone throws away a piece of trash. Making a process efficient, but ultimately ineffective, probably isn’t worth your time. (I’m sure there’s some counter example that describes a situation where waste disposal efficiency is very important! I just use that example to show how efficiency can be very far removed from effectiveness.)
When it comes to measuring the effectiveness of your PI system, where would you start? Being busy is one thing, but being busy about the right things is likely more important.
One important consideration is issue identification. How does your PI system learn about its issues? Does it just tackle each item that comes up from difficult cases? How do staff highlight issues to PI staff? Is that easy to do? Does your system gather data and decide which issues are a “big enough deal” to move ahead? Does it use a FMEA and COPQ to look at factors that help prioritize issues?
These are some of the most important issue identification factors for your PI system, but by no means are the only ones related to effectiveness.
Once the right issues are acquired in the right way at the right time, where do they go from there? Are all the stakeholders involved in a process to make improvement? Does the system use data and follow those data to decide what really needs to happen, or does it only use its “gut”? Is the PI system politicized, so that data aren’t used, aren’t important, aren’t regarded, or just aren’t made?
The staff at the “tip of the sword” (the end of the process that touches patients) and even those who never see a patient but whose efforts impact them (that’s every staff member right?) are armed with data they can understand that describe performance. Even better, the staff receive data that they’ve asked for because the PI/QI process tailor made what data the staff receive. (More on that a little later.)
Once issues are identified, and the PI system performs, what happens with the output? This is another key question regarding effectiveness that can let you know a lot about the health system. There’s an element of user design (#UX) in good PI systems. Do the data get to the staff who need to know? Do the staff understand what the data mean? Are the data in a format that allow the data to impact performance? Are the data endpoints (at least some of them) something unique and particular that the staff asked about way-back-when?
Lean Six Sigma is 80% people and 20% math.
You may have heard that old saying. In fact, it’s been said about several quality programs. (I’ve discussed previously that, yes, the system is 80% people but getting the 20% math correct is essential–otherwise the boat won’t float!) It is on this point about effectiveness that I’d like to take a second with you before we go:
One of the major items with quality improvement is the ability to use trusted data to impact what we do for patients for the better.
That’s the whole point right? If the data don’t represent what we do, are the wrong data at the wrong time, or are beautiful but no one can understand them, well, the PI process is not effective.
This, to my mind, is the key question to gauge PI / QI success:
Do we see data impact our behavior on important topics in a timely fashion?
If we do, we have checked many of the boxes regarding efficiency and effectiveness, because, for that to happen, we must have identified key issues, experienced a process that somehow takes those issues and creates meaningful data, taken that data in a format that is understood by the organization, and we must have done it all in a timely fashion that actually changes what we do. That is efficient and effective.