http://ift.tt/2gHpcLY Here’s a useful story of how one team improved ED throughput by walking through system and finding an unexpected delay.
By: David Kashmer, MD MBA (@DavidKashmer)
Most hospitals want to improve throughput…
Have you ever worked at a hospital that wanted to improve its ED throughput? I bet you have, because almost all do! Here’s a story of how advanced quality tools lead a team to find at least one element that added 20 minutes to almost every ED stay…
Once upon a time…
At one hospital where I worked, a problem with admission delays in the emergency department led us far astray when we tried to solve it intuitively. In fact, we made the situation worse. Patients were spending too much time in the emergency room after the decision to admit them was made. There was a lot of consternation about why it took so long and why we were routinely running over the hospital guidelines for admission. We had a lot of case-by-case discussion, trying to pinpoint where the bottleneck was. Finally, we decided to stop discussing and start gathering data.
Follow a patient through the value stream…
We did a prospective study and had one of the residents walk through the system. The observer watched each step in the system after the team mapped out exactly what the system was. What we discovered was that a twenty-minute computer delay was built into the process for almost every patient that came through the ED.
The doctor would get into the computer system and admit the patient, but the software took twenty minutes to tell the patient-transport staff that it was time to wheel the patient upstairs. That was a completely unexpected answer. We had been sitting around in meetings trying to figure out why the admission process took too long. We were saying things like, “This particular doctor didn’t make a decision in a timely fashion.” Sometimes that was actually true, but not always. It took using statistical tools and a walk through the process to understand at least one hidden fact that cost almost every patient 20 minutes of waiting time. It’s amazing how much improvement you can see when you let the data (not just your gut) guide process improvement.
The issue is not personal
We went to the information-technology (IT) people and showed them the data. We asked what we could do to help them fix the problem. By taking this approach, instead of blaming them for creating the problem, we turned them into stakeholders. They were able to fix the software issue, and we were able to shave twenty minutes off most patients’ times in the ER. Looking back, we should probably have involved the IT department from the start!
Significant decrease in median wait time and variance of wait times
Fascinatingly, not only did the median time until admission decrease, but the variation in times decreased too. (We made several changes to the system, all based on the stakeholders’ suggestions.) In the end, we had a much higher quality system on our hands…all thanks to DMAIC and the data…
Excerpt originally published as part of Volume to Value: Proven Methods for Achieving High Quality in Healthcare
By: David Kashmer MD MBA FACS (@DavidKashmer)
What’s dangerous is not to evolve. –Jeff Bezos
Once upon a time, a young man went to work every day providing an invaluable service for his local community. The work was considered essential, in fact, to help make sure people were safe and were able to get done what they needed to get done in order to live their lives. Now, that position no longer exists in our society. The job: lamplighter. It could’ve been milkman or a host of others.
Oh, did you think I was leading up to a job in Healthcare? No problem! Insert radiology file room clerk (not many around since the dawn of the electronic medical record and PACS integration). Colleagues, here’s the point: if you think of Healthcare as static, well…stop! The story I share above about lamplighters could easily be another role in the hospital or perhaps, some say, an entire medical specialty.
I invite you to think of Healthcare, and your role in it, as more like navigating an ocean instead of walking a beaten path. And in oceans, my friends, things happen. Unexpected weather, accidents at sea, and moments of amazing calm are each represented in different measures at different times.
Let’s talk about the tumultuous state of Healthcare. Like me, you’ve seen:
- increasing numbers of employed physicians and declining numbers of private practices
- significant time spent (more than 20% of our days in many reports) on documentation in electronic health records.
- increasing focus on defensive medicine owing to many factors including the modern climate of tort law
Now, let me be clear: I’m not commenting on whether this is bad or good…I’m only saying that this just is.
Ok, now let’s get to where we’re going: in order to navigate the highly complex ocean of Healthcare, physicians need tools. And, unfortunately, we often weren’t given these tools in medical school. Now, I agree that medical school should help us understand disease & its treatment. We should focus on the basics of baking the cake of how to deliver excellent, compassionate care to people. Much of the rest is icing. We may even learn how to be lifelong learners…but what then?
Nowadays, we have needs that medical school didn’t directly address: we need a different mental model because times in healthcare have changed. Why? Because the only constant thing is change. The Affordable Care Act, the ongoing transition from a system focused on volume of services delivered to one centered on value of care delivered, and a swell of other influencers have made the practical side of what it means to provide care very different than what the tools we took from medical school were designed to address.
So what about these situations where the waves surge so high that our boat is threatened? What about situations where we have no map or compass?
Tools for the ever-changing landscape, ones that build strategies, teach us how to maintain the financial viability of our practice, or otherwise guide us in this often-challenging ocean…well, those tools are not included in our medical textbooks. Those tools, ones that enable us to provide high quality care, create a new practice of our own, or to allow us to practice at a higher level as an employed physician…those tools are more typically found in business textbooks.
How exactly is a Relative Value Unit (RVU) defined? What exactly is an acid test ratio, and what does it tell me about my practice? How can I create a system in my hospital as an employed physician that helps me provide routine, excellent care? These questions, and others, are answered by a toolset that we’ve often seen little of in Healthcare. These are more commonly found in the business world and those are the ones that help us navigate amidst an uncertain future.
So, once upon a time, an entire job disappeared. Don’t be the next lamplighter and wind up snuffed out by a towering wave of disruption. Build a better map to navigate what are sometimes treacherous waters–waters which will likely become only more challenging to traverse in the years to come.
David Kashmer (@DavidKashmer, LinkedIn profile here.)
Once upon a time, a healthcare quality improvement team celebrated: it had solved a huge problem for its organization. After months of difficult work, the team had improved the hospital’s Length of Stay incredibly. But, three months later, the Length of Stay slid back to exactly where it was before the team spent an entire year of work on the project. What happened!?
One Of The Most Important Steps In A Project
The final part of a quality improvement project is setting it up so you get feedback from the system on a regular basis. If you don’t do that last part correctly, you don’t know that things have gone haywire until a problem jumps out at you. All quality improvement projects need a control phase that lets the system signal you somehow to tell you when things aren’t going right anymore. All the work you did on your quality improvement project isn’t really over until you answer the final question, “How do we sustain improvement?”
The answer is using the right tools in the control phase. In healthcare, patients come through the system one at a time, but to get the big picture, Lean Six Sigma often uses control charts after the quality improvements have been implemented. All a control chart can tell you is that a system is functioning at its routine level of performance over time. It can’t tell you whether that routine level of performance is acceptable or not. If you look only at the control chart (especially if you do that too early), everything may look like it’s going fine, but in fact, the performance may be totally unacceptable. This is why control charts shouldn’t be applied 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.
How To Choose The Right Chart
Control charts vary, depending on what you’re measuring and how your data is distributed. Your Lean Six Sigma blackbelt is the right person to help you decide which type of chart to use and understand what it’s telling you. You would use a different control for averages over time than you would for proportions over time, for example.
Specifically, in healthcare, we often use a control chart that tracks individuals as they come through the system. It’s called an individual moving range (ImR) chart. (There’s some advice on how to choose a control chart here.) It plots patients and people or events as they come through the system one at a time.
The range is an important part of the ImR chart. Range is a measure of variance between data points. In other words, range shows you how wide the swings are in your data. If you see an unusual amount of variance between data points, the question becomes “Why such a wide swing? What is it telling us?”
Applying the Rules
If you don’t build a control chart into the ongoing phase of your quality improvement project, and look at it on a regular basis, you won’t pick up the signals that say, “This case is beyond the upper control limit. Something must have gotten out of whack with this case. We have to look into it.” The power of the control chart is it will tell you when things are going off the rails.
To understand what’s going on with your control charts, Lean Six Sigma applies what are known as the Shewhart Rules, which are rooted in the Westinghouse Rules originally devised by Westinghouse Electric. The rules tell what to look for in the control charts to see if a problem is on the way or is already there. Often, obvious signs tell you about a problem. A data point might be above or below the limits set in the chart. In healthcare, we mostly look for variants above the limit, because that often indicates something took too long or didn’t go smoothly. If something is more than three standards deviations beyond what’s expected, that means there’s less than a 1 percent chance it happened at random. You need to look into it.
Check The Control Chart On A Regular Schedule
Control charts need to be checked on a regular schedule, but they also need to be reviewed if anything external changes the system. The chief of the department might leave as part of personnel shuffle. That means new people who may not understand the system well come in. The control chart should be checked more often to see where the personnel changes may be affecting quality. Remember to make it clear, before the project’s end, exactly who will look in on the chart, when they will do it, and who they should call when there’s an issue. It’s important that this be someone who lives with the new process as it will be after changes.
A lot can change quickly in just a month or two. The control phase provides feedback from the system when something has gone wrong, or something needs maintenance, or the weeds need trimming.
The bottom line: plan to maintain the gains you’ve made with your important quality improvement project by designing in a control phase from the beginning!
Excerpt above was originally published as part of Volume To Value: Proven Methods For Achieving High Quality In Healthcare.
Want to read more about advanced quality tools and their uses in healthcare? Click here.
By: David Kashmer (@David Kashmer)
I was recently part of a team that was trying to decide how well residents in our hospital were supervised. The issue is important, because residency programs are required to have excellent oversight to maintain their certification. Senior physicians are supposed to supervise the residents as the residents care for patients. There are also supposed to be regular meetings with the residents and meaningful oversight during patient care. We had to be able to show accrediting agencies that supervision was happening effectively. Everyone on the team, myself included, felt we really did well with residents in terms of supervision. We would answer their questions, we’d help them out with patients in the middle of the night, we’d do everything we could to guide them in providing safe, excellent patient care. At least we thought we did . . . .
We’d have meetings and say, “The resident was supervised because we did this with them and we had that conversation about a patient.” None of this was captured anywhere; it was all subjective feelings on the part of the senior medical staff. The residents, however, were telling us that they felt supervision could have been better in the overnight shifts and also in some other specific situations. Still, we (especially the senior staff doing the supervising) would tell ourselves in the meetings, “We’re doing a good job. We know we’re supervising them well.”
We weren’t exactly lying to ourselves. We were supervising the residents pretty well. We just couldn’t demonstrate it in the ways that mattered, and we were concerned about any perceived lack in the overnight supervision. We were having plenty of medical decision-making conversations with the residents and helping them in all the ways we were supposed to, but we didn’t have a critical way to evaluate our efforts in terms of demonstrating how we were doing or having something tangible to improve.
When I say stop lying to ourselves, I mean that we tend to self-delude into thinking that things are OK, even when they’re not. How would we ever know? What changes our ability to think about our performance? Data. When good data tell us, objectively and without question, that something has to change–well, at least we are more likely to agree. Having good data prevents all of us from thinking we’re above average . . . a common misconception.
To improve our resident supervision, we first had to agree it needed improvement. To reach that point, we had to collect data prospectively and review it. But before we even thought about data collection, we had to deal with the unspoken issue of protection. We had to make sure all the attending physicians knew they were protected against being blamed, scapegoated, or even fired if the data turned out to show problems. We had to reassure everyone that we weren’t looking for someone to blame. We were looking for ways to make a good system better. There are ways to collect data that are anonymous. The way we chose did not include which attending or resident was involved at each data point. That protection was key (and is very important in quality improvement projects in healthcare) to allowing the project to move ahead.
I’ve found that it helps to bring the group to the understanding that, because we are so good, data collection on the process will show us that we’re just fine—maybe even that we are exceptionally good. Usually, once the data are in, that’s not the case. On the rare occasion when the system really is awesome, I help the group to go out of its way to celebrate and to focus on what can be replicated in other areas to get that same level of success.
When we collected the data on resident supervision, we asked ourselves the Five Whys. Why do we think we may not be supervising residents well? Why? What tells us that? The documentation’s not very good. Why is the documentation not very good? We can’t tell if it doesn’t reflect what we’re doing or if we don’t have some way to get what we’re doing on the chart. Why don’t we have some way to get it on the chart? Well, because . . . .
If you ask yourself the question “why” five times, chances are you’ll get to the root cause of why things are the way they are. It’s a tough series of questions. It requires self-examination. You have to be very honest and direct with yourself and your colleagues. You also have to know some of the different ways that things can be—you have to apply your experience and get ideas from others to see what is not going on in your system. Some sacred cows may lose their lives in the process. Other times you run up against something missing from a system (absence) rather than presence of something like a sacred cow. What protections are not there? As the saying goes, if your eyes haven’t seen it, your mind can’t know it.
As we asked ourselves the Five Whys, we asked why we felt we were doing a good job but an outsider wouldn’t be able to tell. We decided that the only way an outsider could ever know that we were supervising well was to make sure supervision was thoroughly documented in the patient charts.
The next step was to collect data on our documentation to see how good it was. We decided to rate it on a scale of one to five. One was terrible: no sign of any documentation of decision-making or senior physician support in the chart. Five was great: we can really see that what we said was happening, happened.
We focused on why the decision-making process wasn’t getting documented in the charts. There were lots of reasons: Because it’s midnight. Because we’re not near a computer. Because we were called away to another patient. Because the computers were down. Because the decision was complicated and it was difficult to record it accurately.
We developed a system for scoring the charts that I felt was pretty objective. The data were gathered prospectively; names were scrubbed, because we didn’t care which surgeon it was and we didn’t want to bias the scoring. To validate the scoring, we used a Gage Reproducibility and Reliability test, which (among other things) helps determine how much variability in the measurement system is caused by differences between operators. We chose thirty charts at random and had three doctors check them and give them a grade with the new system. Each doctor was blinded to the chart they rated (as much as you could be) and rated each chart three times. We found that most charts were graded at 2 or 2.5.
Once we were satisfied that the scoring system was valid, we applied it prospectively and scored a sample of charts according to the sample size calculation we had performed. Reading the chart to see if it documented supervision correctly only took about a second. We found, again, our score was about 2.5. That was little dismaying, because it showed we weren’t doing as well as we thought, although we weren’t doing terribly, either.
Then we came up with interventions that we thought would improve the score. We made poka-yoke changes—changes that made it easier to do the right thing without having to think about it. In this case, the poka-yoke answer was to make it easier to document resident oversight and demonstrate compliance with Physicians At Teaching Hospitals (PATH) rules; the changes made it harder to avoid documenting actions. By making success easier, we saw the scores rise to 5 and stay there. We added standard language and made it easy to access in the electronic medical record. We educated the staff. We demonstrated how, and why, it was easier to do the right thing and use the tool instead of skipping the documentation and getting all the work that resulted when the documentation was not present.
The project succeeded extremely well because we stopped lying to ourselves. We used data and the Five Whys to see that what we told ourselves didn’t align with what was happening. We didn’t start with the assumption that we were lying to ourselves. We thought we were doing a good job. We talked about what a good job looked like, how we’d know if we were doing a good job, and so on, but what really helped us put data on the questions was using a fishbone diagram. We used the diagram to find the six different factors of special cause variation…
Want to read more about how the team used the tools of statistical process control to vastly improve resident oversight? Read more about it in the Amazon best-seller: Volume To Value here.