Don’t Sweat the Small Stuff – Why Turnover and FCOTS Are Not Your Most Important Metrics Transcript
I’d like to introduce today’s two presenters. First, we have a surprise guest, Katherine Halverson-Carpenter, who is the Executive Director of perioperative services at UCHealth for over 10 years. She has had experience leading perioperative services teams across the nation. She now serves as LeanTaas’ VP of clinical operations. We are also joined by Ashley Walsh, who is a perioperative Business Manager at UCHealth for eight years. And now, is the UCHealth’s Senior Financial Analyst for perioperative services. She is currently on a rotational program here at LeanTaas, where she is helping us improve our overall client services. Let’s go ahead and get started. Katherine, the floor is yours. Katherine, can you hear us?
HALVERSON-CARPENTER: Can you hear me? OK, thanks, Katie. Good morning. Thank you for joining our webinar. A quick introduction about LeanTaas, we’re an eight-year-old software company started by ex-Google, McKinsey Executive, our DNA is data science. We mine EHR data to make hospital operations better. We have two products, iQueue for infusion center, which uses data science to lower wait time at infusion centers. And iQueue operating rooms using data science to improve OR utilization. Both products have strong adoption and solid ROI. We also have a lab unit that’s exploring other problems, like imaging clinics, et cetera. We have some of the brightest minds in tech from Stanford MIT both McKinsey, Yahoo, Symantec, and more. And they work hard to make our customers successful. Next slide, we’re seeing very strong interest from hospital networks. We have more than 50 health systems as happy customers, including Stanford, UCHealth, and more. More than 14 out of the top 25 US news and world report cancer centers trust us. And we strongly believe in partner-lead innovation.
We listen to our customers and carefully and relentlessly solve their problems. The OR customer include academic centers as well as community hospitals– Parkview, in Southern Colorado, UCHealth, Cleveland Clinic, Ohio Health, and New York Presbyterian. We are in the process of finalizing contracts with Multi-care Dignity Health, and the University of Utah, to name a few. Next slide, a brief overview of UCHealth. UCHealth began as a system in January of 2012 consisting of the university hospital, which is an academic center, and Poudre Valley Health System in Northern Colorado combined together to form UCHealth. In October of that year, Memorial Health System in southern Colorado joined UCHealth Health. UCHealth is comprised of five hospitals, soon to be seven by fall, employs over 16,000 employees, does greater than 66,000 surgeries. We have over 1,600 beds and over a hundred and four thousand admissions. Our clinic visits exceed 2.6 million, and e-deficits are almost a half million. And to note, that we have one level one trauma center and two-level, two trauma centers. The net revenues were 3.61 billion. And we began our relationship with LeanTaas in January of 2016 with the implementation of the iQueue infusion product. That resulted in improved patient access, reduced patient waiting room times, increases the more predictable infusion schedule, allowing the nursing staff to have time for lunch, which was a great satisfier and reduced death overtime.
In January, we also began the development of the OR product at university hospital. As Executive Director of perioperative services, I oversaw the initiation of the project and implementation of the program. It’s now my pleasure to introduce Ashley Walsh, my colleague who was a key contact with LeanTaas during the product development phase in our implementation at UCHealth. Ashley?
ASHLEY WALSH: Thanks, Katherine. Good morning, everyone. Thanks for joining us today. So I’m excited today to share with you some great things that we found out in this journey of partnering with LeanTaas. And specifically, today, what I really want to talk to you about is why first case on-time starts and turnover while still very important metrics. Well, yes, we do want to optimize those metrics and to improve the patient throughput, collaboration among our teams. But why maybe they’re not our most important metrics that we should focus on? And so I’m going to share with you some results from experience and case studies that we’ve done. So what first I want to talk about block utilization at UCHealth, Katherine and I, oversaw 38 operating rooms. It was an academic medical campus. We had 25 inpatient rooms. We had eight outpatient rooms, an eye surgery center, and another AFC. For many, many years, we really work to improve our block utilization. Block utilization or having blocks, I should say, it was a satisfying for our physicians. They liked having block time. They liked know in what days we allocated for them, but we struggled to really get that block utilization up. And as a result, even on our room utilization was quite high. And for us, high was above 75%. Our block utilization mimics that, so what that meant was our teams were doing a lot of scrambling on the day of surgeries to add more add-on cases. And when we looked at those add-on cases, a lot of them were planned and predicted cases.
So we wanted to really set out a mission to improve our block utilization, improve our plan-predicted cases, and really minimize that downtime. So there are tons of ways to calculate block time. A very simple version, and I think it’s a widely used version across the country, it’s to look at the total block minutes that are allocated to a provider a group or a service. And then calculate that by dividing by that block that in the minutes of patients in room. And oftentimes, we include turnover in that because we know that turnover is a part of the day. The physicians, physically, can’t go one case to the next with no downtime, so we know there will be some turnover time. Now, as far as what turnover you add into that, personally, at UCHealth, we added in the goal turnover, so not always the actual unless the actual less than the goal. So that’s one way we calculate a block utilization. The metrics that we often evaluated to determine what was leading to the downtime was our First Case On-time Start and our Turnover Delay. In networking across the country, I find that many, many hospitals put a lot of effort into these two delay metrics. Our rapid improvement events are focused around them, which is not only time and money of our employees, but also physicians leading to it. And that can also lead to additional downtime, really optimizing out these delay metrics. So what I was curious about was I really wanted to know, knowing how much time and effort we were putting into optimizing first case on-time starts and turnovers, how much did that really contribute to the underutilization of block time? As I mentioned previously, our block time did not actually match our room utilization, so our room utilization would hover somewhere around 75%.
Our block time would have here somewhere around the low 60s to mid-60s. So I wanted to know how much of first case on-time starts and turnover delays really contributed to the lower block utilization. And if it wasn’t those metrics, what should we focus on? So we decided to conduct a case study. We conducted a case study over an entire year’s worth of data. We decided to focus on the Inpatient Academic Medical Center where there were 25 operating rooms in one location. I felt this was a good location to focus on because in addition to it being large, high volume trauma cases. It is a transplant center, but in addition, we were blocked out almost 90% in this operating room, so I felt it was a very good operating room to evaluate how those metrics played into the underutilization of block time. So we looked at an entire year’s worth of data. We looked at calendar year 2016. And on average, what we found was we were allocating about 500 minutes per room of those 25 operating rooms, 2 blocks per day. And when we looked at all the patient in room minutes, during those same times, about 357 minutes per day were used within the block. Now, this led to about 71% utilization total. I am including in some of our cases that we did during business hours that were really attributed to our trauma acute care surgery rooms, which were not elective block rooms. So you can see that that number was inflated a little bit by the total patient in room minutes because we really wanted to see total patient in room minutes. So now, what we decide to look at was what happened to that other 29%? And how much did delays from first case on-time starts impact that 29% underutilization? So the way we evaluated first case was we looked at the first case in each room that was scheduled to start between 7:00 and 9:30, and whether or not, there was a delay.
So when we aggregated all of those delays for an entire calendar year, we came up to the sum of 16,331 minutes were attributed to first case on-time start delays for a year. So that was only about three minutes per day, per block room, which accounted for about 2.1% out of that 29% unused block time. So next, we turn to turnovers. We wanted to know how much were turnovers contributing to the unused time. In this location, the goal for turnover with 30 minutes. We define turnover by wheels out to wheels in, and we did have a set of exclusions. We looked at turnover very, very closely in this location. For example, in the prior cases finish much earlier than expected. Do we still count the turnover of the second case? We have a set of rules set in place for when we do and when we don’t include that. So when we aggregated all of the delays for turnover on events that we did consider turnovers, and how much time over 30 minutes were taking place in those turnovers, the number was higher than first case on-time starts for an entire year is about 97,000 minutes. But an average of about 18 minutes per day, per block room. We were attributing to unused time from delays in our turnovers. In the big picture, though, that was about 3.5% of blocked time or 12.3% of total unused time. If you remember our first case on-time start, it was only about 2% of unused time. And turnover delays was about 12%, so in total, our total amount of unused time and block time attributed to first case on-time starts, and turnover only was about 14%. So then we started to question, well, what is leading to all of the other unused block time? So the other 86 percent, we really wanted to evaluate how and why was that going unused. So the third step in this case study was we identified other factors behind underutilization.
There’s lots of things that lead to underutilization, but these three stood out to us the most. First, scheduled downtime, second, last-minute cancellations, and third, when we overestimated our case length. We know that this is frustrating for our providers, frustrating for our staff. And often, the feel is that this is very great in the operating room. Now, we calculated the impact of those other three factors. So for scheduled downtime, we calculated the total case schedule length within the block including our cases that got canceled. And the remaining portion of the block time, we really considered the scheduled downtime. But even if the case got canceled, first, we evaluated just total schedule downtime. Second, we did look at cancellations, though. We wanted to know how frequent our cancellations happening. And we looked at cancellations as anything canceled within one week. And third, the case length overestimation. Block by block is the total scheduled time was larger than the actual used time within the block. We aggregated all those minutes into that bucket of case length overestimation. So here’s the results of those three. If you remember, delays accounted for 14%. And delays from first case on-time starts and turnovers. For overestimation, we only found that attributed to about 11% of that unused block time, that original 29% of unused time. Cancellations, that was a somewhat significant amount, 21%. So right there, we realized that cancellations were affecting our unused time more than the delays were. So that was something we really wanted to focus on, but the largest one was that scheduled downtime. You can see that 53% of our unused times, that was attributed to blocks was because of schedule downtime. So until we did this case study, we had a suspicion that this was high. We also had a suspicion that overestimation might have been an issue, and that cancellations, but we didn’t have data backing it up. So upon completion of this case study, we realized that the largest impact was that scheduled downtime. And so that was what we really wanted to focus on, first and foremost, to reduce the scheduled downtime. So we realized that block allocation was really not aligning very well with our demand. You know, just working with colleagues across the country, I think that this is a struggle for a lot of us.
How, what, when, and why we give block time to providers. You know, if I may, it’s a bit of a guessing game. We often run reports, monitor volume trends, et cetera, et cetera, and discuss with the surgeons, of course, needs, when they have clinic when they’re available to operate. But it’s very hard to– without strong analytics that and predictive analytics to really understand how much block time a provider or a service line is likely to need. When we’ve looked at that scheduled downtime even deeper, we found that often there were a lot of blocks that didn’t even have one case performed in them. So scheduled downtime was many different things. It was an eight-hour block that had a two-hour case scheduled. But a lot of times, it was actually an eight-hour block that had not one case scheduled. That was frustrating for us to realize because we knew that we were staffing for that blocked time. We were planning for equipment use of that block time, teams, resources, et cetera. And when we realized that the block time had gone unused completely, we realized that we needed to figure some things out, and figure out a way to communicate better to our providers. Increase that transparency of the block schedule and unused time, so we can increase our utilization overall. The primary solution we use to identify all of this schedule downtime is a new concept. And it’s a concept that was really developed in partnering with LeanTaas, and that concept is Collectable Time.
Collectable Time is how we wanted to identify these large amounts of unused time, unscheduled time, I should say, so we can reduce the downtime in the day. I want to talk a little bit more about Collectable Time and what Collectable time is. So if you look at the screen I have in front of you, and you look at each one of those bars, that would represent a day of block allocated to a provider group or service. Dark blue indicated when patients were in the room. Gray indicated when there were no patients in the room. You can see like on 2/1, there are periodic cases in between the entire day, and there are spaces, and those spaces are attributed to turnover. What we really focused on, though, were delays from turnover or turnover time segments. As we learned in the case study, these really only attributes to 14% of our unused time. Again here, the delay from the first case start. So in Collectable Time, what we are focusing on is looking at for these red diamonds. When does large amount of time happen? And how can we use math and predictive analytics to identify the red diamonds and identify the blue triangles? And then put that together in analysis to say, here’s the information on the blue triangles although that is not leading, adding to your significant amount of Collectable Time. But rather, here’s the information on how often the red diamonds happen. So identifying Collectable time was a great step in the right direction for us to figure out which blocks really were going either unused completely or had significant amounts of scheduled downtime.
Talk about a few solutions that we’ve used, both tactical and strategic, at UCHealth, to not only reduce the Collectable Time, but to drive behaviors that really improved block utilization. Drove behaviors do not only meet surgeons needs, make them happier, but also make the OR happier, and knowing these planned and predicted cases well in advance so that the day of shuffle decreased. The first solution was a concept with developed with LeanTaas of mobile block exchange. We like to think of mobile walk exchange as an Open Table for operating rooms. It allows physicians access into the schedule to identify when there are blocks that are going unused and available, and that physicians groups or practices could request if they need additional block time. This is a concept that is very easy for both a surgeon or surgeon’s office, the surgeon scheduler to use. What I’m showing you right here is the view on the mobile phone. Having data available on a mobile device for a physician gets them much more involved and provides information to them real-time. We know, though, that oftentimes, the surgeons might not be the ones requesting these block days or releasing, and therefore there’s also the ability to do this on the web. So surgeons and schedulers can not only immediately discover the blocks that are available and request them, they are also getting proactive alerts through text messages asking them if they need more time, or reminding them that they have an upcoming block. And that there are no cases booked into that block. So really prompting them to do the right thing, which is release it, and increase that Open Table for other providers. Let’s play a little demo for you, so you can see what this looks like. So the physician can go into their mobile device, launch their mobile browser, look at a location that they would like to request block time for, see if the time available, then if by which time slots he or she would like. Notify various amounts of people, and also request any specific equipment or other needs that they need for the case. Then that gets sent off to a centralized scheduling department typically to approve or deny. In that instance, if you know you need a block time on a certain day and it doesn’t show is available, a physician can set an alert, kind of like an Amazon Wish List. Please let me know when that block is available. The release functionality is very easy. A physician can jump in and see when they have block time and decide if they want to release it. See if I had any cases booked on just to make sure that they want to release it or not. Go ahead and release it and send that information off, so it makes it very, very easy to do the right thing, and to notify the scheduling department of when, he or she, would not use a blocked time that has been allocated to them.
This is very important for us at UCHealth because while we do have a centralized scheduling department, that scheduling department was monitoring blocked releases 10 different ways– phone calls, emails, text messages you name it, it was coming in from all directions. By providing a very tactical solution for them, it came in one streamlined form that they could track, monitor, and act upon. What we found was this was much smoother communication, much fewer emails. Many more blocks were saved, meaning we had we’re finding that there were less blocks where nothing was scheduled then too. So ultimately, we were getting more cases done, more planned predicted cases, and planned predicted time. For the metric scheduling department, just to show you what that looks like, how it streamline, I’ll play another little demo before you. The settlers go into a web browser as well. And they can see basically a task list. They can see when they have block time if they want to release a blocked time. This is showing you a view from a clinic scheduler, so they can see when their providers are allocated time if they want to release them. So this is just simply the web version of the mobile demo we just played for you previously.
You can get warnings if there are cases already scheduled, so we’re getting information from EHRs to know are there cases scheduled, are there no cases scheduled to make sure that we’re releasing the right days. It’s just another form of checks and balances for the clinic’s scheduling department. If they want to request time, similar workflow, the clinic’s scheduler, or office administrator for the provider can go in. Select that their provider that they are responsible for. Select location that they’re interested in requesting block time for. Select the date. It might be a date when there is no block time available, but they can set an alert. Also, another day when maybe there is block time already available. You may select that time or that day. They can see the times that are open. And these are all rules that can be configured to any location. If the rules are– they can request two-hour blocks, four-hour blocks. If the rule is only– you can request an eight-hour block. All of those rules can be set in for any given location. So this communication then goes back to the OR scheduling department. It makes it very nice for the OR scheduling department to then monitor all of the requests in one location, approve or deny them, communicate to any necessary personnel, be it service line specialist, operating room managers, vendors, et cetera. So that was really the tactical solution that we put in place to decrease scheduled downtime. From a strategic solution, we wanted to look at something as an aggregate as a whole. And be able to make very good decisions as a leadership committee on how we’re giving out block time, who were giving black time to, how frequently we are evaluating it, et cetera.
Personally, I was someone that was involved in block time allocation for about eight years for a couple hundred providers. That is a very complex game to play and manage on a month-to-month basis. All the requests that come in. While we had a very strong leadership team in place comprised of both perioperative administrators and physicians, we found that it was still challenging to allocate time, approve requests, deny requests, and come up with a very fluid solution for how we monitored our block allocation. I do think that for many years, block utilization was a bit of a broken metric. It’s a stoichiometric, it doesn’t always account for many other factors such as how often a physician is releasing time. If a physician is continuously delayed, perhaps they only have an afternoon block, and the morning provider’s going over. While you can find all this information, it required many, many different reports. What was great about the concept of Collectable Time is it’s really combining all of these different reasons for blocks to go and utilized into one metric. So there was one metric for our leadership team to evaluate and utilize in order to determine who, what, when, and where should have block time. It’s very configurable too, so not only can it be surgeon-centric, and that we want it to be conservative. We don’t want to under allocate block time for our providers. We want to make sure that they have the right time available for their patient demands. But we can also configure different things as a leadership committee. For example, if we really want to cut down on too much time being released, we can configure how much release time is acceptable time within our collectable calculation. Starting to talk about Collectable Time and really marketing it to the surgeons was important. So not only did we start to do that through the tactical solution, which was providing them real-time information through mobile text, email, all different mediums to get through them, but also using Collectable Time in the written communication we used to our block owners. Letting them know how well that they were managing their time, how well they were utilizing their time, and where time perhaps was not utilized as effectively. So we would use this letter as a reference point for face-to-face conversation with a block owner or group of black owners to provide them a summary of simply what we talked about before, how often delays were happening, and what portion that was attributing to their unused time. How often they were leaving, continuous amounts of time.
So if you remember those red diamonds in the Collectable Time image, how often those were going underutilized to really increase the communication and the transparency using data and strong analytics with our provider. Hope you enjoyed learning about what we did, the lessons that we’ve learned, the case study that we performed. And ultimately, I’d like you to take this as an opportunity to take this back to your own locations. And perhaps, use this as an opportunity to evaluate how much first case on-time starts and turnovers add to your delay. And evaluate how much effort, time, cost, resources are being put into those metrics. As well as really look at what is that large driver of unused time and schedule downtime– is it scheduled downtime? Is it cancellations is it overestimation? I hope you enjoyed our presentation today. I hope you enjoyed learning about the tools that we use to really effectively allocate block time and improve utilization at UCHealth. I’d love to field any questions that some of you may have, and continue the conversation. KATIE MCDERMOTT: Great, we have one question for Katherine. The question is, did you try and fix the problem of unused time internally? How did you decide to move forward with LeanTaas, iQueue?
KATHERINE HALVERSON-CARPENTER: Probably tried it internally for years with our IT department to develop scorecards looking at black utilization, but it was really continuous of excel spreadsheets in pivot format. You know we didn’t give us any new information. I actually spent hours to develop the search and block utilization report. There was a lack of trust in data and to send out the informations. So we did look at other vendors, but one of the things that we [INAUDIBLE] see at the vendors is, it was the same, only their charts for prettier and graphics than we were able to do. And LeanTaas was very different. And this is why it was very intriguing. When Ashley talked about doing a retrospective and using machine learning and doing the analysis of what were unused times, and it wasn’t always first case. And turnover time that was contributing to our gap, it was poor utilization and estimation of case length that was contributing to it, so it really provided a different perspective for us to look at. And also discovered why we went with LeanTaas is, besides the differentiation of using machine learning data science and predictive analytics, they were very smart data scientists. They partnered with us to be successful. We throw questions at them. And they would have a very fast turnover of looking at our data differently. And that’s really why we decided to select LeanTaas as our partner.
KATIE MCDERMOTT: Thank you.
KATHERINE HALVERSON-CARPENTER: Is that OK?
ASHLEY WALSH: Couple questions about mobile and getting providers interested in looking at their data. There was a comment that my provider probably won’t look at the data that we send them. I can definitely relate to that. So having, as Katherine mentioned, been the person that was really driving a lot of the report building, creating, messaging for our providers, messaging that through monthly meetings, I know that providers didn’t often look at the data that was sent out to them. What I’ve learned now in hindsight is we were sending out too much information, and we were sending it out through a form of communication email that was being overused. So providers were less likely to open up their communication, open up the PowerPoint or the PDF, read through it to find specifically what they wanted to know about their volume, their metrics, et cetera. Even when we tailor that to be more driven towards surgeon-centric for them, we still found that they weren’t often opening up those emails, opening up those messages. I just really want to emphasize to this group the power of mobile, and how impactful sending very simple text messages to our providers, providing a very easy to use, accessible portal into data. And when I say portal, there’s no app that we required our physicians to download. They didn’t have to register, put software on their phone or their computer. It is all linked through cloud, and so it is a web application. We provided the link in the text message that they could just open up on their mobile browser. Aside from even that, we tailor the text messages to be very specific for them. So I would send to Dr. John, Dr. John, here was your volume last week.
Here was your delays, your first case on-time starts, and your block utilization. Immediately, I was hearing from our perianesthesia managers how frequently the providers were standing there in the periop checking out their phone before cases start and chatting about their volume, their metrics, where they were standing on the leader board. I mean physicians are very competitive by nature. And so that was a huge win for us and very insightful because we realized that the medium we were using to communicate messages was not successful. But by tailoring that to a simple text message, we found it was so much more effective. So that was a very exciting win for us. Another question came through our blocks by physicians, or do we have groups that have blocks. If you have groups, how do you manage the communication with them? Great question. So yes, actually this location that we shared with you today that the case study was conducted on actually has three types of blocks. The location does individual surgeon blocks, the surgeon group blocks, and sometimes the groups were comprised of two or sometimes six members, and also a service line blocks. Service line blocks contained up to 150 members for some service line. So we had all three at this location and the second part of that question was the thing about communication, I’m sorry. How did we manage the communication? So anyone that was a member of a block was receiving information through text messages and emails. Here was your volume, here was your performance. For the people that were involved in groups or service line blocks, they were also, in addition, receiving information on how much their performance contributed to the overall performance of that group. So for example, the group of six members, Dr. John, would get a message that the block utilization of the group was x. He performed the amount of minutes, which contributed to an overall percentage of the total blocks utilization. So then they were also realizing who was doing, what, and when was in these group blocks. Now, what we decided to do was we decided to open up mobile block exchange to everyone. Meaning a physician that was a part of a service line block. If they felt that they weren’t able to book cases as effectively as they wanted to, and they didn’t have enough allocated time within the service line block, we allowed them the ability to request an individual block for him or herself through mobile back exchange. We did it initially as a bit of an experiment, and we found that it was very successful because some people, although, in a service line block, did need more time.
We were able to allow them access to get more time for him or herself. And then what that helped on the strategic part was we were then seeing from a strategic view, which surgeons really needed their own time within the service line blocks. How well they could utilize a block on his or her own. So I hope that answered your question. Thanks for the questions I don’t see any more questions. If you do have additional questions, my email is on the screen. As Katie mentioned, we are going to record– or we are recording this webinar, and we’ll be happy to send it out to share with you and you can share it with your colleagues. But please don’t hesitate to email or contact at anytime. We really appreciate your attendance. on your email for future webinar and announcements as well. Thanks so much.