OR Insights for Every Occasion Transcript

SOPHIE YING: Hello, everyone.

My name is Sophie, and I’m a product manager at LeanTaaS. So just a little bit about myself– I’ve been with LeanTaaS for about a year and a half now with the focus on the analytics portion in the OR product suite.

So the topic for today’s webinar is called OR Insights for Every Occasion.Just like what it sounds, we’re going to talk about how different stakeholders in the operating room space can use this tool called Analyze within the iQueue product suite to gain insights into the data and make data driven decisions in the day-to-day OR operations.

So here is a quick snapshot of the things on the agenda for today’s webinar.

We’re first going to go over really quick about what is LeanTaaS and what is iQueue.

Then I’ll introduce you to the Analyze module in a bit more detail.

And after that comes the fun part, which we’ll dive into lots of product demos and examples to show you how people at various roles and positions in the OR, let it be the CEO of the hospital, the OR directors, business analysts, data analysts, nurse managers, et cetera, et cetera– how all those people can use Analyze differently to make data-driven decisions in the OR today.

So here is a LeanTaaS and iQueue overview.

LeanTaaS is a Silicon Valley-based software company. We have a diverse set of talents working for us, including mathematicians, data scientists, software engineers, and product managers like me who are more technology focused.

We also have people coming from a healthcare background with lots of experience in the perioperative operation space.

We have $15 million invested right now in the product. And we are working on a software platform called iQueue, which you’ll hear frequently in and out of the webinar today.

Currently, we have a few products that are commercially available, including iQueue for infusion centers and iQueue for Operating Rooms.

And we’re also working towards new, exciting products, including the clinic inpatient beds and so on.

 So currently, the OR product–for the OR products, we have a 90-plus hospitals, which include–

So currently for the OR products, we have 90-plus hospitals live as our customers, which includes 19 health systems and more than 900 ORs live and using the product.

In some of the logo here you can see, including University of Colorado Health, Ohio Health, Dignity, NewYork-Presbyterian, and so on. So moving on, here is the overview of the iQueue for Operating Rooms product.

We have three modules here.

First, Exchange, which is a tool that is similar to the OpenTable idea that allows people to easily request and release time in the OR, which increases the liquidity of OR time in the hospital.

The second module is called Collect, which helps drive surgeons accountability of their block time and provide an easy way for OR leadership to decide which surgeons to take blocks away by how much.

And the third module is Analyze, which is what we’re going to focus on today.

So this slide is a detailed introduction of Analyze. So Analyze really focuses on giving people access to credible and meaningful data insights to make decisions in the OR operations.

To be a bit more specific, we have a dashboard which serves as the single source of truth when it comes to data and reporting. And it contains a standardized set of KPIs that every leader in the OR should care about.

What’s unique about Analyze that’s different from any traditional hospital reporting tools is that you’ve probably– that’s different from any hospital reporting tool that you’ve probably seen or used is that the data is credible, timely, and actionable. And by that, I mean we have a whole team of data engineers dedicated to clean and mine the data source coming from the EHR to ensure data quality. And we make sure each metric is well socialized across admins and surgeons by providing them the ability to drill down very deeply and slice and dice each metric to the most granular case-by-case level if needed and also provide clear visibility into calculations and definitions of each metric so that everyone can trust the data and be on the same page. We also have multiple channels of access, including mobile and web. And we have a push mechanism to actively engage providers and admins through emails and text messages. So with all that being said, let’s jump into today’s topic. In the OR operations environment, data reporting can be really challenging, especially when different people are looking for different things at different times to answer different questions.

With the rich information existing in the Analyze module,and the easy access to theplatform anytime, anywhere,and the flexibility for people to slice and dice the data,I have prepared a few examples that show you how different stakeholders can use Analyze to easily find answers to their specific questions and tailored towards their need.So with that being said,let’s jump into the examples.

First set of examples is more high level for the leadership team,so let’s say you’re the CEO of the hospitalor the OR director.So now I’ll be sharing my screen and doing a live product demo. So you probably need to hit the red radio button on the console.

This is going to be reflected there.

OK.

Now what you’re seeing is a dashboard.It’s the Analyze dashboard.On the very top, you have the time range filter to choose past year,or past quarter,or custom time range and the location filter to choose the region or the hospital.So let’s say we’re interested in Hollywood BurbankHospital within the region of Los Angeles.We have anonymized this demo account.So you’re saying the name being kind of funny.You can navigate across all those tiles,and each card represents a KPI that the leaders should care about.So my volume, ORminutes, utilization,block utilization, and the efficiency metrics such as first case on-time starts, add-on ratio, turnover,and so on.

As you can see, as Ihover over the card,the definition is popping up.So we make it really easy for people to know how exactly are we defining each metric.So going back to the example, the CEOwants to know on a high level how are my business growing?How are my volume and primetime utilizations been trending?So let’s click into this primetime utilization and see.Here is a year-over-year view of my prime time utilization within this facility.And what we are doing is that we’re grabbing it by month and compare for each month how different years–how the metric has been trending across different years to account for seasonality.So let’s toggle 2019 off and focus on 2017 and 2018 here.We’re seeing almost for every single month that the metric has been improving,so that’s a very good sign.We can also toggle back to a flat, one-dimensional trendview to see a full historical progression.We can see in 2017 we have 62.2018, aggregately, we have 67.And this keeps growing,which is very good.

Now that we have checked primetime utilization, let’s go back and maybe check case volume as well.Similarly, you’re seeing a year-over-year view of the case volume and seeing that it’s been growing as well.

So as the CEO or the OR director, my next question might be, what are the drivers behind the volume increase. And seeing, for example, December 2017 to 2018 has this jump from 952 to more than 1,000. I may want to investigate why, just like behind the December– the month of December. And maybe a different service line has a different rate of growth, and I want to see which service line has the most growth. So an easy way to do that is to navigate to the Comparison tab. And here we are breaking down the metric by service line. And let’s see, let’s filter this by December– the month of December in 2017 first. So you can use the custom time range and type in the month of December– December 1st to December 31st and hit space. And here’s the result in the month of December 2017. And you can choose to download the image, to save for future use, or for presentation, or share with others. And once we have done that, let’s also check 2018, December. Here is the result for 2018. We can also download the graph. So from this result, we can see that general surgery, orthopedics, and pediatric surgery seems to be the biggest service line in this facility. And among those three, general service lines have grown from 222 to over 300. So it seems like general surgery is the biggest volume driver in the increase in the month of December, at least. So here is just the starting point for you to investigate more behind those volume growth, prime time utilization growth, and so on. OK.

So I’m going to go back to the slides now. Going to our second set of examples, which is for robotic surgery. So we all know that robots are very expensive, valuable assets in the hospital. And though typically for our customers, the hospital will have an established robot committee. And those people are specifically focused on how well are we using a robot, do we need to buy new ones and set policies around the robots, et cetera. So for the robot committee, they really need to know the metrics centered around robots. And let me go to the screen share mode and demonstrate how you can use Analyze to see robot-centric metrics. So here’s the dashboard again. And if you click into any of the metrics, notice how we have a Show Robot Cases Only toggle here for every single view. For example, the number of cases– after you click this, it’s being filtered by robotic cases only. And you can see a trend of robot case volume throughout history. And also similarly for leaderboard– in leaderboard, we’re ranking the providers based on the metric. And you can see a list of providers who have done the most robot cases in the given frame and similarly for comparison and so on. And you can also see, for example, first case on-time starts but only specific to robot cases. So once you check this, it’s the robot first case on-time starts. So just a quick navigation tour here. And also, if you–.here’s the scenario. For example. I have two robots being placed in my two rooms. And as the director of robot committee, I might want to know how well are those rooms being used. Are they even being used by robotic cases or what’s the situation here? So in order to find out about that, let’s go to prime time utilization, visualizer. And we’re still on the Hollywood Burbank facility. That’s good. And on the right side, we can see a list of rooms within the facility. And the ones with the robot tag are where the robot is living at. And let’s see, if I have two robots, one in OR four and one in OR nine– and let’s check OR four first. So here’s the view in the past quarter showing you by day of week, by time of day, how is the room being utilized. If you hover over these visualizers, you can see in the past quarter at 8:50 to 8:55, the time of the day on Mondays, 11 out of 14 times this room is being utilized. And the aggregate room utilization is roughly 79%. So what’s cool about this is that you can actually turn on the Show Robot Usage toggle to see how the occupancy is being broken down by robot versus non-robot cases. In this light blue color, we’re showing you the robot case occupancy. And for this room, you can see that Monday seems to– very few robot cases are being performed on Mondays even though the robot is in the room. And Tuesday is slightly better, and Wednesday is– only 1/3 of the robot volume is being done. And maybe– there could be various reasons. Maybe some block owner– maybe some provider has blocking this room that is not necessarily doing robot cases or various other reasons. So let’s go to check OR nine, the other robot, and turn on this toggle. We’re seeing the OR nine seems to be much more at capacity for robot cases. So just by comparing these two gives me some insight into my next step. If I want to optimize the robot usage, I probably want to focus on OR four and see who are being blocked in that room, and whether there’s more flexibility to account more robot cases, and so on. So going back to the slides– moving on to our next set of examples– so for business managers and data analysts, they typically– on their day-to-day job, they need to run a lot of customized, ad-hoc reports and answering various data questions coming from people. So what they need is a very flexible way to query the data and get down to that number really quickly. And also, sometimes they just need the raw data source in Excel format to do further customized reports. So we actually support both, and let me show you how they can do that. So I’m going to the screen sharing mode again. I want to show you this cool tool called the Explorer within the Analyze module. As you can see on the left, we have dashboard, which I just went over, the Explorer, and the Surgeon Section, which will show surgeon-centric metrics. And let’s go back to the robot topic. Let’s say the director of the robot committee asked me, what’s the volume in a past year for urology service line on Wednesdays? Maybe he wants to know that because urology has a block on Wednesday or something. So let me just show you how you can get down to that number really quickly. You choose the metric here, number of cases, and you add the filter time range to be past year. Let’s choose the facility to be Civic Center. Let’s choose the surgeon or the service line to be urology. And robot cases– select robot cases only so that we’re counting the robot volume. And on Wednesdays, day of the week– So that’s Wednesday. So you can see how easy it is to add and remove a filter and combine it in whatever way you like. And let’s hit and Run once we’ve selected all the filter. And here’s a trend view by month showing you how urology has been doing robotic cases on Wednesdays. And you can choose to see a line graph, or bar graph, or download the results as you wish. And after you run the reports, you can save the report. And whenever you go back to the Saved Reports, you can see this one– this is the report I just ran– and you can schedule it to run any time that you want. So here’s Explorer. And also, I just wanted to mention that we have enabled download CSV functions here in the table. For example, you go to the number of cases you want to know. You just want to see every single cases and all the details that has been performed in, say, February 2019. You just click on this number, and it will download the CSV to your local. And opening up the file gives you every single case, including all the details, surgeons, service lines, room, patient, class, timestamps, schedule time, procedure name, and so on. So you can do a lot of further customization reports on this if you’re a data analyst. OK. Going back to our next and our last set of example, which is for nurse managers. So nurse managers and anesthesia leaders– they will be making a lot of staffing decisions across teams and service lines. So it will be useful for them if it’s showing– maybe seeing a historical trend of the room occupancy pattern for them to make decisions for future staffing. And then let me share my screen again The dashboard– OK. So I want to show how the information can be useful for nurse managers to make staffing decisions and showing you a historical trend of room occupancy.

Let’s go to prime time utilization again and visualizer. And we have this Occupied Room thing on the left, which is an aggregate room– room occupancy data for this entire facility. Let’s click on it. And this view brings you to very colorful line graphs. Let’s maybe focus on– so showing you each day of the week the room occupancy pattern. And since it’s pretty busy, let’s only focus on Monday. And toggle these lines off. So what this is telling me is that we have chosen past quarter, meaning that in the past quarter– there are 14 Mondays in the past quarter. On average, at 8:30, which is where I’m hovering at, it’s showing you 9.9 or roughly 10 rooms are being occupied in the past quarter. And as you move around, you can see how the rooms are using less and less as we get later throughout the day. And you can also choose from 5:00 PM to 7:00 AM next morning to see how are we using a room in the after hour. So what this means is that if you assume that the demand pattern or the case volume is not going to change drastically, you can actually staff 10 rooms, which is the average in the past quarter. But what this will do is that 10 room is only going to cover all of your OR cases half of the time, because the other half of the time, there are actually more than 10 rooms running at that time. So what we did is that we actually give you the 75th percentile room, which is a little bit more conservative, meaning that if you staff 12 rooms at 8:30, you’ll be able to cover all your OR volumes 75% of the time. So similarly, the 90th percentile number is even more conservative. And we show you all the way till the maximum. And you can see we never run above 12 rooms. Basically, that’s what it’s saying. And similarly for a minimum number– and you can see some other days of the week as you like. You can download the image as usual for sharing with other people and so on. So this is on a more aggregate level throughout the facility. We can also use the historical usage pattern on a individual block on their level to be more granular. So another hypothetical scenario here– let’s say someone from the neuro team actually complained to me several times that they’re always working late because the neurosurgery director, Dr. Brooks– let’s pretend it’s Dr. Brooks– is always working late in his block. So a couple of things, as a nurse manager, to do here– first, let’s validate if the complaint is actually valid. Validate of the problem exists. So let’s go to block utilization, visualizer, and search for Dr. Brooks here. Brooks [INAUDIBLE] and click on this row. And here is the visualizer– visualization pattern for Dr. Brooks usage in the past quarter. So similarly to the room utilization visualizer, the green indicates the in-block correct usage. And the red– the yellow is actually overbook, meaning that Dr. Brooks is running across different rooms at the same time. And the red indicates the outside usage. From just a glance, we can see this– he’s actually running his block quite a lot. And hovering over it, we can see exactly 11 times out of 13 weeks, he is still operating at 5:00 PM. And moving along the hover, we can see that by 6:15 PM, he is still operating six times out of 13. And you can also show details to see on which day did he run over, by how much, and so on. But going back to the original question, we’re trying to validate if the complaint is real. From this graph, we’re seeing he is, indeed, running outside quite a bit. And maybe whenever he has blocks, I should plan to have the neuro nursing team to work longer so that they have the right expectations. So that is it about the examples I wanted to share, and I wanted to open up to questions. 

WOMAN: So we have a few questions coming in. And we’ve saved extra time, so if you have any questions you want to ask, feel free to use the Q&A widget at the bottom of your screen. We are more than happy to answer your questions. First one that came in says, great system. How does the data get entered into the system? Would we have to upload every month then the system calculates the stats? 

SOPHIE YING: Yeah, good question. So as I mentioned earlier, we have a whole team of data engineers to take care of the data pipeline. So how it works is that in the initial onboarding stage– as the customers sign the contract, we enter in this onboarding stage. And our data engineer will work with IT team to get a historical data down from the EHR. And then we actually set up this recurring daily feed that will automatically transfer all the newest data updates from the EHR to our iQueue platform so that there is no– we don’t need to update every month. Nothing like that. Once it’s been set up in the beginning, it will run smoothly afterwards.

WOMAN: How much can you customize the metric definitions for each customer? 

SOPHIE YING: So yeah, another great question. I may actually go to the screen share mode again. I just want to show you the metric definition on hover. So let’s say prime time utilization, right? We know each facility or each location can have different prime time hours. Some it’s 7:00 to 3:00, 7:00 to 5:00, 7:00 to 6:00, whatever. And we can customize that. We can work with the customer to set up the right definition and right expectation and configure that accordingly. And for example, first case on-time starts, it can be scheduled– it can be different criteria for the first cases, right? So for all those things, we– for every single measures, we have the flexibility to configure not only on a per customer level but even on a per location level. And yeah. So we’re pretty flexible on this point.

WOMAN: I see one more question. What additional metrics can you see on the surgeon-specific level? 

SOPHIE YING: So yeah, now that we’re on the demo, let me switch to the surgeon view, which we haven’t got a chance to talk about yet. So you can see a list of every single surgeon. And let’s use Dr. Howard as an example. We basically have every single metric that is available on the dashboard for the surgeons, specifically. And so you can see the case volume, case minutes, utilization, including his individual utilization and also any group or service line, block utilization that the provider is involved in, and all the operations metrics. On top of what’s being shown on the dashboard, we have an additional cases volume performed as non-panel 1 or non-primary. We know that providers sometimes can be second panel or assisting other surgeons, so this is one additional metric we show for providers, specifically. And we’re also working on a surgeon turnover metric in the future.

WOMAN: OK, well, I don’t see any other questions that have come in. So thanks again for joining us.