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Webinar Transcript: Achieving Optimal Patient Flow – What Really Affects Change, with Yale New Haven Health

At the LeanTaaS Transform Winter 2021 event, Robert Fogerty, MD, MPH, SFHM, Director, Bed Resources, Yale New Haven Hospital; and Pallabi Sanyal-Dey, MD, FHM, Director, Client Services, LeanTaaS, discussed how Yale New Haven Health adopted iQueue for Inpatient Beds to provide transparency throughout the system and manage the aspects of inpatient bed flow that could be controlled. To view the whole session, visit the Transform Inpatient Beds learning track here

Moderator: Hello and welcome to “Achieving Optimal Patient Flow Event Capacity: What Really Affects Change” at the Transform Better Healthcare Through Math Hospital Operations Virtual Summit, on behalf of Becker’s Healthcare and LeanTaaS. Thank you so much for joining us today. Now at this time it is my pleasure to begin today’s program and pass the floor over to today’s speakers Dr. Fogerty and Dr. Sanyal-Dey. 

Sanyal-Dey: Thank you so much. Welcome, everyone. Thank you so much for making time for us today. I wanted to take a moment and introduce Dr. Fogerty. He hails from Connecticut and went to med school at Northwestern, went on to do both his training and then become part of Yale faculty for a good few years now, as I understand. So Rob is our main speaker today and we will be conversing about capacity throughout. He currently holds the title of director of bed resources and oversees capacity management also across the network itself. So a heavy, heavy and exciting wonderful job, so we’re excited to learn and hear more about his experience so far. 

Briefly, I will also just tell you a little bit about myself. I also hail from the east coast from Massachusetts. Just like Rob, I’m a hospitalist and also an academic hospitalist at UCSF. I used to oversee patient flow over at our county hospitals, part of the UCSF system for the last several years. More recently, I’ve joined LeanTaaS in working on this wonderful capacity management software tool that we will be talking about. So that is who we are. Rob, I also understand some of your interests. So you are heavy into sort of quality of care, cost effectiveness. And of course that means patient flow itself. Anything else I should mention here that you’ve come across?  

Fogerty: So I think you’ve hit all the high points, two New Englanders to kick off a winter discussion. 

Sanyal-Dey: Oh, boy, you guys haven’t gotten snow yet – you did. See, this is a little secret as Rob is actually on the East Coast right now and I’m in California, but I will soon see the East Coast again. I’ll hand it over to you to take us through just what Yale New Haven system looks like and what the structure is.  

Fogerty: So, Yale New Haven Health System is an integrated health system along the coast of Connecticut. For those of you who don’t know New England, we are nestled in between New York, Rhode Island, and Massachusetts along the Long Island Sound. Yale New Haven Hospital is in the middle of our health system. But we do have acute care hospitals that stretch from essentially the New York border. Greenwich Hospital is all the way along the I-95 corridor and our furthest East facility is Westerly Hospital, which is just across the border in Rhode Island. 

Yale New Haven Hospital itself is located in New Haven, Connecticut. We have a total license of 1551 beds, we have an Integrated Children’s Hospital, Women’s and Children’s, and of course, an adult acute care hospital. We are the academic referral center for the region. We’re the largest hospital in the state by bed count, one of the largest in the United States. And we are the primary teaching affiliate for the Yale School of Medicine, which is conveniently located across the street. New Haven, if you haven’t been here, is beautiful. Please come in the fall. We get the great foliage. 

It is one of the more densely populated corridors in the United States and the reason I mentioned that is we’re the only hospital in New Haven. So we have to serve two roles. One is we are the Community Hospital for the community that we are privileged to be a part of. We’re also the academic referral center, both locally in Connecticut regionally, as well as nationally and internationally. 

Sanyal-Dey: I wanted to spend one moment on the fact that you actually have quite a large hospital, Yale New Haven Hospital, housing the 1500+ beds across two campuses. Can you give us a visual of what that looks like for you and kind of operationally what it means? 

Fogerty: 1541 beds is a little bit of a Monopoly number. and it can be difficult to to do that in efficient professional speak here. But we have a 900 bed campus on York Street. We refer to that as the York Street Campus. And then there was a smaller independent community hospital, which was a large hospital in its own right, with over 500 beds in there licensed to the hospital, St. Raphael, and I want to say about eight or 10 years ago now, the two institutions merged. So one, one license, one medical staff. We don’t duplicate all services across the two campuses. But that creates some logistical challenges in that we have no one to divert to, or I should say we have no one to which to divert. So we can’t really tell our fire departments and ambulance providers we’re overwhelmed, you have to go somewhere else because they would just go across town to the other hospital. Which is also us with the same medical staff.

So we’ve done some things to try to mitigate. We work with our community partners, and the sheer size of it being one hospital, it gets hard to know what’s happening in all four corners of the institution. And I think it will probably come up in the Q&A, is that the volume of things that are happening is immense. Having that transparency to it in a single place is incredibly valuable. 

The fact is we can’t really close our doors and can’t really go on diversion. It’s the natural ebb and flow of the day. This is a stylized graph, it isn’t actual data but we actually did this study in our hospital in 2010. When you look at how patients come in, depending on the service, there’s a little bit of a different time of day, but we don’t really discharge in the morning and we don’t really discharge intentionally in the middle of the night, so folks tend to leave towards the second half of the business day. But folks walk into the ER 24/7. They get in car accidents 24/7, go into labor 24/7, get chest pain when they get chest pain. So the intakes are always happening. 

But the supply demand mismatch, the temporal nature of that every day, is something that creates an amount of inefficiency and the hard part is when we’re living every day, 92, 96, 98% occupancy across the entire enterprise. You don’t have that facility, you’re not flexible enough to absorb both the new admissions and the patients waiting to leave for those few hours and it only exacerbates the bottlenecks that happen across most hospitals in the United States, I would say at this point. 

Sanyal-Dey: This graph is something I know you and I have lived and experienced day-to-day inside the hospital. One thing that keeps coming to mind is people always think about other industries, an obvious one that comes to mind is hotels, etc. Where you just think you have that specific checkout time and you know, people come and go in this very structured manner, that there’s so much more control and just a different arena.

The hospital’s such a different different landscape and, and to high level, here’s what you know, the struggle I remember I often experienced is that as a provider, whether you’re a physician, nurse, ancillary staff, whatnot, you’re trying to deliver care in a setting where there’s also these administrative financial pressures in which you know, yes, we’ve got to have some flow and we’ve got to have people out by this time in the morning, but as you can see on this graph, the first several hours in the morning, we are nowhere near to be near kind of that work of being able to safely allow people to go because they’re not ready, or there’s a multitude of different reasons and we’re rounding in the morning and etc. Those challenges would always be facing me, there’s not much wiggle room there. Rob, if you want to speak a little bit to that also from your angle. 

Fogerty: The mornings are becoming more compressed. I don’t know if anyone else had this experience, but when you say you want to go to medical school and become a doctor, you’re gonna have early mornings. That’s been true for generations. The morning can only can get so early. Now that patients are more complex, their care is more sophisticated, the support structure around those patients needs to be more integrated, let alone a lot of our structural failings in healthcare in general. They really get put into this crucible of the acute care hospital, between I’d say seven in the morning and two in the afternoon. Just like you said, it’s very challenging, because you’re trying to triage and provide care to the sickest patients, the most unstable patients, but you know that there’s someone just waiting for a goodbye handshake, or goodbye fist bump, these days before you can get out of the hospital. You just can’t get there because there’s someone in front of you that needs acute care right now. It is inefficiency that is only getting worse, and as we get better at managing complex disease with advanced therapeutics, and all of those biomedical success stories, it’s only going to get harder. 

Sanyal-Dey: Right, there’s more information, more stuff to incorporate. I will move on a little bit more now, transitioning into just further defining the problem of inpatient capacity. As you all know, this is of course, a complex issue. There’s different facets of it that we wanted to focus on today, and reflect a little about what our experiences have been at our individual institutions. So the first one being this whole lack of visibility, especially when it comes to forecasting or predicting pressure points or bottlenecks at the unit level. Rob, how has that been at Yale for you? 

Fogerty: It’s a challenge. Our clinical bed managers or nurses perform the bed management process 24/7/365 actually have these little crystal balls on their desk, and it is a morale thing. It’s very clever. We’re asked multiple times a day to see into the future and anticipate these bottlenecks. The problem is that there are some structural issues that we know about, right? We know when the PACUs tend to fill out, but then there are things that are totally unpredictable. We have, I want to say, about a year pre-COVID, so I guess we’re talking 2018-2019, we had a bus that was transporting prisoners to the courthouse here in town get into an accident. That happens all the time. That’s what we do. 

The unique factor of this is, they were all prisoners. So they came with their police guards. So it wasn’t 16 patients, but 16 patients and 16 police officers and figuring out how to do that in an already crowded emergency room that created an instant bottleneck at the front door to the institution. Looking at the other end, you have a DME provider that decides to get out of the business very quickly. You just had a new discharge barrier jump in the game at the last minute and then you can look in the middle. Do you have that right combination of the doctors doing the procedures and the particular procedures that they’re doing. and the anesthesiologist, coincidental comorbidities of the patients, and they all combine and all of a sudden you’ve got 30% of your cases flipping in? That’s a bottleneck that maybe you could have foreseen if you would have the data analytics to look at that, but hard to find those kinds of things that are that are easy to pop in a disk and let it go. So this is a real challenge. And even during flu season, we plan for flu season every year. We just don’t know when it’s gonna be January, February, March. Who knows? 

Sanyal-Dey: Right. I remember a couple of those in the smaller ones, like suddenly your MRI machine goes down or things like that. Immediate impact. The next thing ongoing – staffing challenges, a huge issue, and then with a pandemic. What has that been like for you over in New Haven?

Fogerty: Like for everybody, it’s been hard. Staffing is a challenge on a good day. And now, it may sound a little bit like a broken record, but I think it’s true, everybody’s sicker and you’ve got staff that are fatigued coming off the worst pandemic, that is still going on, of the past 100 years. Our COVID census has been going up steadily. And to put all that together, an unstaffed bed doesn’t do a patient who was boarding somewhere any good. So to figure out a way to match the patients and the bad and the new complexity to this is we’ve created so many lovely, great, life-saving, important interventions that require just a little bit of specialization for the team that is at the bedside. So now those beds become less interchangeable. 

We’ve talked before about how you can’t put a 92 year old with a hip fracture into a bassinet. Now there are some surgical procedures that can’t go to the overflow ICU because they don’t have the ability to care for that particular type of support apparatus that they came out of the OR with. So now you’ve got hospitals within a hospital and any time you silo off of a patient flow pattern, you’ve just created a new opportunity for a bottleneck, and then you put into that staffing and in the current environment. It’s really challenging and just in time means that you’re always on the brink.

Sanyal Dey: Just constantly, as we say, reactive, obviously falling behind. Related to that, you touched on this a little, there are unique staff skill sets and resources. You basically summarized this and it really does come down to things such as, you know, the level of care and the type of care the patient needs. So that brings it more focused on the nursing angle. Where you know, for example, on the oncology floor you have exactly the specified skill sets to take patients through their protocols that they need to go through etc and even on the surgical floors. We just have just wonderful staff that really know how to take care of your ortho patients and the list goes on. And then the complexity, like I said, we just reflected on the pandemic the needs for our COVID patients. The needs for all these specialized patients, that matching process, it’s challenging and I can’t speak to it more than we already have. Rob, feel free to add more. 

Fogerty: I think you just did, very eloquently. I think it’s a challenge that we need to accept. And we need to absorb the need and we need to solve, and these unique skills are so critically important, providing high quality care and advancing the delivery of health to our patients, if we accept this variable that we need to own. I think it’s only going to get more complicated and more complex which is why science needs to be the driving force around capacity management. It is no longer something you can do with a pencil and pad of legal paper. We need to recognize it has advanced past that and will never go back. 

Sanyal Dey: You know, one aspect of it that does remind me in the past used to be both static and no, like you were saying with science, we’ve had to really modify our approaches. This idea of geographic location being tied to the skill set one can provide that has been a huge challenge in this arena, especially during the pandemic, and that’s something we’ll speak to and reflect on even more as we move forward. The last thing we want to reflect on is demand and as I said, in other industries, demand is something one can relatively, with ease at times, not only understand, predict and identify, but they can also control. 

This is where, in medicine in healthcare, the control factor is huge, which is why we have it in caps, partial. Thoughts on this in particular? 

Fogerty: So many thoughts. We talked a little bit already about diversion. We get lots of folks who probably experienced this. We’re getting phone calls from all over the country looking for an ICU bed. And we lived that in April, I will never forget, the demand was everywhere, we had patients in ORs, PACUs – nature finds a way, to quote Jurassic Park, add a little levity here, but when someone gets sick they’re coming in. That’s that. If COVID has taught us anything it’s that we can believe we can control the demand curve, but we can only do so much. The parts that you can control, another lesson from COVID – oh sure, let’s shut down the ORs, but then those potentially curable diseases, resectable tumors, correctable vascular malformations – now they’re not. That doesn’t help anybody here. 

One of the sad truths, and this will come out, all-cause mortality’s going to go up during the pandemic. Part of that is because of the onslaught of uncontrolled demand, that we can’t just say “no” and lose the business to a competitor because they’re just as full. The patient is the one that ultimately suffers. That’s why when we say demand is only partly controllable, you can split it into services, there are some things you can safely delay. If I need to delay my cholesterol screening a year, that’s probably okay. If it’s an aortic root that’s growing, maybe not. 

When you look at things you think you can control, biology will control it whether we like it or not. That’s it, that is a big challenge. I’m sure it’s the same in California as it is in New England.  

Sanyal-Dey: Absolutely. The weather patterns may be different, so that brings different types of calamities, but the pandemic and what it brought to us, same types of things. You can’t  stressed more on the partial aspects of what you can truly control, and that is something we will actually reflect on even more using technology, what can be controlled. That is something of huge importance. Because that is where we’ll have an opportunity. We’ll delve into that as we move forward. 

Speaking of technology. I did want to take a moment to give a very high level summary of what technology we offer from LeanTaaS specifically, and then Rob will take you through a little bit of what their journey has been with us and implementing the software itself. 

So we’ve created a capacity tool, a software tool that really focuses on the three aspects you see above. Of course Access is huge. Accountability is a big thing and really knowing who is responsible for what, but you have to start from a place where everyone is seeing the same thing and it is transparent throughout, exactly what is going on through the hospital. So the way we decided to tackle it from a software angle is that the tool focuses on three areas. One being Capacity Huddles, in particular, that part of the tool starts off with a very high level view, as we say 10,000 foot view, of giving just a quick summary of what is going on in general areas of the hospital for incoming, internal, and then outgoing. 

Then we delve into more detail in the Unit page, which really gets into the granularity of how each unit is doing, what is the culture of that unit and how that impacts not only what is actively happening in real time, but also we then get into data science models that reflect based on historical data and what is going on in real time. What is going to be your incoming admissions demand and what the discharge flow will look like hours from now, so that one can operationally make smart decisions with regards to movements and efficiency to match the demands that will come through. The aspect of control, as I was mentioning, is really important. So one area of movement in the hospital where one can exert some control is the internal transfer activity that happens, especially when patients require no longer ICU level of care, they can go to the floor, etc. 

We’ve created the transfer tool that not only allows you the transparency of all the different types of internal transfer requests that are happening. It also takes it a step further, where there’s actually prescriptive actions that one can take as to which transfers you should make sure happen so that you can create more capacity in your system. 

The last part that I’ll just touch on briefly is that we cannot talk about capacity without the element of discharge, the outgoing flow. Part of our tool reflects on not only showing in real time, where this activity is occurring, but prioritizing how you should tackle your discharges based on our models and what will be happening in the future and what is going on right now to really truly surface that information. So that is a quick overview. There is more detail to this obviously, and more views and aspects of our tool that we will review probably offline and if there’s more information or even opportunity to get into that later on.

Rob, I’m going to hand it back, with regards to Yale’s experience so far. 

Fogerty: So iQueue, we’ve had it up for a little more than a month. Previous to this, we’re an Epic shop, we stood up a capacity management center. We did it on a really shoestring budget, we took all the folks we think are involved in the scaffolding of care, put them in a room together, helped helped to build a lot of real time visibility and transparency through Epic, our EHR, tried to improve our driving situational awareness across the institution. So one of the fundamental goals there was no that one would be allowed to claim ignorance anymore, “Butt it’s a bad day.” Back then we had good days and bad days, when there are bad days everybody knows. We actually every day 365 days a year, at eight o’clock in the morning our hospital has a teleconference, now it’s a Zoom, we talk about safety concerns, past 24 hours, upcoming 24 hours. First thing on the agenda item every day is a capacity report. How full are we, how are the ICUs, whatever the pertinent info is for the day, sometimes we’ll look at COVID numbers as well, if it’s trending in the wrong direction. 

The idea there was environmental services, patient transport, pharmacy, bed and care management, our nursing float pool, all those folks are key players in the scaffolding of care to deliver health to the patients in our beds. They were in different parts of of the organization, they made phone calls, but let’s put them in the same room, give them a single data set but make it transparent, so anyone with EHR access can log in, be it a cardiothoracic surgeon or resident a nurse, doesn’t matter, everyone can see where the red is. 

Then we have an analytics group that started to model some of that data. With that data we really started to understand how things evolved over the day with the level of detail we didn’t necessarily have before, across the house, using a single data dictionary. I think those are some of the real key lessons, transparency, everybody should be looking at one dataset. You have your own data set, that’s fine. Let’s use it to inform, discuss, have conversations, learn when it comes to making real time decisions we use the single Epic dataset which includes our bed management staff, is totally transparent. That’s what we use. 

The second part of that is the way that people in different parts of the organization digest that data has helped inform other people’s job processes. So we now know a lot about the link between, doctor puts in discharge order, nurse gives discharge instructions, transport these patients to door, PVS job gets triggered, bed is now clean, new patient assigned in to bed, we’ve processed mapped that, I don’t know, five or six times. Involving those stakeholders now that they’ve been in a room hearing about all of these other folks doing their jobs, that little bit of cross training, it’s not enough that so the transport can go and clean a room, but enough so that they understand the lingo and the challenges and the ebb and flow of the day, and raise the understanding across the organization to some extent about the complexity of managing capacity at an acute care hospital. We went from zero to 60 pretty quickly, a couple months, with that particular work. 

I think we have one more slide.

Sanyal-Dey: Yes. I really appreciate the transparency aspect after having had the chance to be part of some of your huddles. It’s really amazing. Throughout the day, the number of people that you’re able to get together is just incredible, that you all have established. And so to take it to that next step, as Rob was saying, iQueue has been around actively, it’s just been almost a month. And so we’re early in the process with Yale. Rob, if you want to speak to a little bit about how it’s been so far, and then I can take the audience through a little bit of what else we’ve been experiencing at other sites.

Fogerty: So we talked a little bit about the analytics, forecasting bottlenecks, it’s Bullet Point 3 there, being proactive rather than reactive, and knowing where we were, and then with the real time understanding. The next obvious step is to say, let’s not guess anymore, let’s use data to inform our anticipatory decisions. And I have a soft spot in my heart for pediatricians, I remember doing a pediatric rotation, and they hammered into my head the importance of doing anticipatory guidance at the well child visits, and it made so much sense. “This is what we’re going to discuss at our next visit, let’s talk about it now so there are no surprises, because there shouldn’t be any surprises.” That’s where we need to go, that’s where we need to focus on in capacity management, to know what’s coming, hours, days, maybe some day we’ll be able to forecast the whole year down to individual days, but for now we’ll focus on maybe a calendar day. 

I’ll go through the bullets line by line, but I do want to recognize that I started with number three, and I’m going to talk about it again because it’s so critically important to where we are now for all the things that we talked about earlier. Staff are tired, patients are sicker, care is more complex. And the care has arrived before we can put in large capital investments in things like buildings. So these are the levers that we really have to quickly adjust. 

So the operational intelligence for patient flow. Gone are the days of “doctor picks this bed for that patient and they stay there.” There are so many care teams involved and there’s so unique environments of care that need to be constructed and pass through in a patient’s hospitalization. To patients who come in with the same condition, the same risk factors and have two very different hospitalizations, mapping those flow patterns out, that kind of intelligent analysis of the way that patients move through a hospital is critically important to informing these decisions. It’s very, very difficult to do or enforce. 

About the large scale, we’re a big shop, but the denominator is the same, that’s the individual patient. Scaling up, you can’t just say 10 patients is 10x of what one patient is. So understanding that this is done on scale every day. But at the same time, we focus and we value transparency. We’re all in this canoe, we’re all Yale New Haven Hospital, we’re in it for the community that we serve, the patients that choose us to help them live healthier lives. And the way that we do that is we try to work together. So let’s not hide anything. It’s all up there for everybody to see. There it is. I’m having a real rough day in Unit X. That’s just how it is today. 

The proactive rather than reactive, this is something I know I’ve hammered on a lot. So critically important, because once the patient needs the bed, if the bed’s not ready, it’s already too late. And  there are not many conditions in clinical medicine that get better with intentional delay. That doesn’t really exist. That’s true in the ambulatory setting and inpatient setting. The literature is full of things like “door to needle time, door to balloon time, early sepsis intervention.” All of these things are time dependent.

So sitting in an ER hallway does nobody any good. There are mitigation strategies to manage that, but if we have the bed and we can make the bed available let’s get the patient in the bed. The way to do that is to anticipate so you’re ready, and the opportunity for improvement in just-in-time staffing, we do staffing about every four hours, we try to shift our float pool around every four hours. That’s very intensive and takes a lot of people a lot of time, a lot of effort, on the behalf of the unit base, charge nurses, the float nurses that have to get a new assignment, actually twice a shift. You can fix that and we can think about stretching that time horizon out just a little bit. We can reduce that reshuffling effort that helps morale, that helps efficiency helps the patient’s not having to get an extra staff member involved in their care for a short period of time. So it’s tough to be a pinch hitter in baseball. And it’s always tough to be a pinch hitter in nursing. So if we can reduce, we can improve these relationships over time with patients develop with their bedside staff. Not only is it better for the patient, it’s more efficient staffing model, it’s a win-win. Why are we doing it? Because we have to see into the future.

Sanyal-Dey: Wow, that was that was great, because what is nice to hear is that Yale, as you beautifully described, you all have a very sophisticated system to start with because of all the work you’ve done thus far with the command center and the transparency of the data and the dashboards that you have within the EHR. With iQueue, as I said early in the journey, these are some of the things we’re focusing on. What I mentioned before is in some of our other customer sites where the tool has been live for over a year now, almost getting close to two and whatnot, is some of these metrics that we have been able to move are time related, as Rob was just saying, as time is one of the most important things that we think about within healthcare, so whether it’s time to place from the moment a patient comes in through your ED to the moment they arrive in there said bed that they need to go to on that unit. Holding time for boarding times to EDI or PACU from discharge delays, or  even excess bed nights. We have seen great movement and improvements in these metrics with some of our other customers, which we’re excited to start looking at and observing at Yale itself, given where we are starting with them and the tools that we’re using. 

The aspect of iQueue that’s important to notice, you know, we really want to focus on operational decisions, and strictly that operational piece, not focusing on the clinical aspects but the operational flow piece, where a patient, like Rob was saying, sitting in the ED just waiting, there’s not much going on there. Can we do something there? So we have taken this approach both kind of using lean principles, of course, which has been helpful, but most importantly using technology and math, literally, to predict and understand flow at the highest level, and use that information to arm folks on the floors, who are not only trying to provide care but move patients efficiently through, so they can start making decisions in a standard format with regards to flow. Not just person dependent – often you have one person who is the walking tome of knowledge about what button to push to make this happen, but guess what, that person’s not here today.  So you know what, we’re gonna have a backup in this part of the hospital. That is what we’re trying to eliminate with this tool. That it is not person specific, and it is data driven, that it has the emphasis on forecasting, so that proactive aspect is really surfaced, and we tried to take it a step further by giving it prescriptive actions. 

Because this isn’t new to have the data, we’re not used to seeing forecasting information. We have gut sense of what’s going to happen. We don’t really know what to do with that. So that’s the other aspect that has been exciting and interesting to watch, what does one do with this data and what we’re trying to also embed is “okay, this is actually a step we can consider, move this patient here, do this in this fashion,” and we want to actually standardize that even more. So we take that cognitive load off of people who are trying to provide care, so they can actually do that and let these decisions happen in a sophisticated fashion using technology and math. 

Okay, let me move on to our next slide, the path forward. So Rob, what is the vision that you have? Now that iQueue is something that you will be utilizing and kind of the mission for Yale New Haven, what you have in mind and have thought about? 

Fogerty: So we’re really we’re really thinking from A to Z, about how we provide acute care in the walls of the hospital as part of a larger ecosystem, that includes our ambulatory practices, community practices, home care, our skilled nursing partners, all from soup to nuts, and the idea here is that there’s not going to be one solution to solve all that woes of health care, in southern Connecticut, let alone across the world.

But when we look at our management of where we provide the most complex care, arguably, which is the acute care hospital, how can we bring data to inform those decisions that you appropriately and accurately pointed out, there’s a lot of gut feeling, there’s a lot of experience, and if we’re going to be frank it’s time we moved past that as an industry. The tools are there, they exist, and I think sometimes in healthcare we want to be always right. We never want to be wrong. I think that, I’ll speak for myself, those misdiagnoses are the gut-wrenching fears the keep you up when you’re on service at night. What did I miss, what didn’t I get right today? 

We have 1541 potential patients, and that’s 1541 patients we need to get right every day. We need to start using data, because we need to recognize, and I think we do recognize, how they move through their hospitalization is part of that care plan, part of the way we deliver that care. If we can bring data to inform that critical part. Just as we practice evidence based medicine around antibiotics selection, around vasopressor sequences, ventilation volume measurement, preoperative ambulation, all based on evidence. This should be based on data and evidence, and that’s what I think is all we’ll want.

Sanyal-Dey: I couldn’t have said it better. It’s exactly that. It’s the reality. It just reminded me a little bit of my experience in one of the hospitals trying to get an app to data, and we have a lot of data now, everyone has a lot of data, and just someone taking the time to summarize that. Taking it and sending it out in email first thing in the morning or whether you create a dashboard, all of that takes time and work to keep up and to maintain. There is another aspect moving forward. It’s like we would love for technology advances to take that workload off of the shoulders of folks who are trying to make operations about hospitals and the care of a hospital be at top notch. So that’s something else that comes to mind for me. 

Moving forward, as we work through and work alongside Yale, understanding their system, there’s a ton of cool, exciting opportunities to understanding algorithms and how you make those decisions with regards to operational flow, a lot of that has even further surfaced in an exciting fashion, from the numbers from the data from the models that we’ve been working on. So I’m excited Rob to see what comes about that shared learning as we move through this process together. So some exciting things.

Fogerty: One of the things I’m particularly intrigued by is the concept of “Survivor Bias,” right? You can Google it. There’s the story about the bombers during World War Two that returned, they mapped out where the bullet holes were, and said that’s where we need to put the extra armor and someone very smart said no, those were the bombers who came back, we need to put the armor where the bullet holes aren’t, those were the bombers who didn’t come back.  That story has stuck with me for a long time. 

We’ve been managing capacity for some time now, since our capacity coordination center’s been set up, 4 or 5 years in. I think it’s time to take a fresh look. We get a new data analytics tool and we work on identifying where our blind spots are, where our survivor biases kicked in, and we need to improve our operational decisions. Part of that is identifying areas where we haven’t thought about for improvement, taking that fresh look, we need to be vulnerable about this. Just because we’ve done it a certain way for a while, it may not be the best way to do it. What better way to do that than taking a data-driven look at our performance in the past and the present how we respond to predictions in the near term. 

Sanyal-Dey: I think you beautifully just summarized all of these different bullets that we have here, as far as how we want to move forward and what we’re excited to look into. So actually with that, I’ll go ahead and with the remaining time we have, Nicole, I will pass it back to you. I believe we have some questions in line, and we’d love to get to those before the block is over. 

Moderator: Thank you both so much for this fantastic presentation. And as we mentioned, we would love to take some questions at this time. So if you do have any questions for either of our presenters, please chat them into the in Q&A box now, and I know we have a few here already. So Dr. Fogerty, how does this work with the command center you already have in place? 

Fogerty: The capacity coordination center we have was about building relationships between folks who perform tasks that are closely related, but maybe their organizational work structures did not have the same close relationship between the people. It’s gonna sound cliche, but we work in a people industry, we need to understand that, not just the patient but with each other. We work in an emotional industry with high stakes. We built that, they get along, they understand each other, we’ve reached a point where their ability to understand, digest, and act on data is far advanced far as where it was, it reached the level of understanding through peer learning and sharing, your earning and sharing. They have an operational understanding that is unmatched when it comes to nuts and bolts of how patients move through. 

Now we’re adding some forward looking information. Now that we’ve got these relationships, we understand the current situation and the current transparent data. Let’s nudge that forward and see if all of us work together and partner on what to do two hours from now, four hours, or six hours, eight to 12, and try to incrementally move that group forward. So it’s a natural progression, I think, of the evolution of our capacity coordination center.

Moderator: Another question for you is how has iQueue benefited your organization regarding transparency of capacity data? 

Fogerty: So a lot of the lessons that we learned when we went transparent with our real time data was similar to what I described. A lot of folks thought they understood it but didn’t. And it comes down to the beds not being terribly interchangeable is a great example of that. There was a learning curve for a lot of people, including some very skilled clinicians who are trained to do procedure X or treat condition Y. My job is to put you in a position where you can do that. My job is, I don’t want you to worry about what’s happening three pavilions over. But I’m doing the same thing for that team three pavilions over. And then we fill up those three pavilions, they get closer and closer, and they start to nudge, because share the same inputs and they share the same output streams. 

Now that we’re bringing in forward looking analytics, early warning systems for analytics, forecasting, whatever the appropriate term is. Now we can start to think about that subset of inputs that we do have some control over. Maybe that’s where we go next. We need to have that discussion. You need to bring your clinical expertise to the table. We need to bring the sterile data analytics tool and bring the reality of our environment. And let’s have a discussion. 

So the idea there is that now that we’re transparent, now that we’re comfortable looking at everybody else’s capacity problems, let’s bring in some advanced tools. We’ve gone through all that we understand each other to a certain Let’s see what new levers are out there. Using forward looking information, try to mitigate the bottlenecks that we can mitigate understanding we’ll never get to zero. 

Moderator: Wonderful. I think we have time for one more question today. How would you address change management with your staff and what are you doing to ensure adoption of the technology?

Fogerty: I’m glad that we left the change management questions to the end. people have actually written entire books and had whole careers on this. So I won’t put myself out there out there as any kind of expert, but we are fortunate in that our staff are some of the best people I know. This is sincerely the truth, I don’t remember who said it, but you don’t go to war with the army you want, you go with the army you have. And I am proud to have gone to war with COVID with my colleagues at Yale New Haven Health and Yale New Haven Medicine and the university, and having people like that around you makes change management about as easy as it could be. Because we all come to work every day to do the right thing and take care of patients. 

As much as it sounds like an advertisement, I’ll bet everyone watching this feels the same about their colleagues, because that’s the industry we work in. We go into this for a different reason. Having said that, there are folks who don’t like computers still. That’s okay. We’ll get you there. The way that we do it, not a carrot, not a stick, it’s I’m going to grab your hand you’re going to grab mine and we are going to fall forward. We’re going to get up and then we’re going to fall forward again and before we know it, we’re at the end of the block.  

And meet people where they are, and understand that there’s an investment here and it will be stressful for you and I appreciate that, but this is our goal, let’s be transparent about that. We want to make everyone’s tasks easier, so we provide a better level of health to the community. Once you get folks on board with that, a lot of the work kind of goes away. Having said that they need to trust you. I know our bed managers, I know about their kids running for office and “the twins”, I know EVS and transport folks, I heard about the Xbox from one of our CCC staff members, this is work, I’m not going to tell you what to do. I’m going to tell you what I think we should do, and I’m hoping you’ll help me and I”ll help you, and here we go. Let’s have this adventure together.  

Sanyal-Dey: Nicole, I wanted to add to that a little bit from the LeanTaaS perspective. about change management. So we really come from that philosophy strongly. Our customers, we have tremendous respect. I especially, I know in these health system, everyone has their own culture, their own understanding, your own trust. We have no expectation that we’re going to march on it and just say hey, guys, this is our tool, this is how you’re going to do capacity management, quite the opposite. We spend a lot of time in the beginning especially, even though it’s respectfully, time that we have asked for from Rob, maybe once or twice a week and spending time with their data team. For a few months where we really tried to establish a good understanding of our customers’ operational kind of truth, their reality of how they run things, making sure that our tool will reflect that. 

To view the whole session, visit the Transform Inpatient Beds learning track here

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