Sickbay Sessions | Episode 2: Patient Monitoring Data is Actionable Data: Saving Lives… Bit by Bit
Patient Monitoring Data is Actionable Data: Saving Lives… Bit by Bit
- Speaker: Raajen Patel, EVP of Innovation and Client Engagement Sudha Yellapantula, PhD, Senior Researcher
- Joellan Mullen, MSN, RN, CCRN, Clinical Engagement Manager
- Megan Sisson, BSN, RN Senior Clinical Engagement Specialist
- Carol Morris, MSN, RN Senior Clinical Engagement Specialist
Join our live webinar May 22nd
In today’s hospitals, information should move at the speed of life. But often, it doesn’t.In Episode 2 of our Sickbay Sessions webinar series, we’ll dive into how near real-time and retrospective patient monitoring data can drive better decisions, reduce risk, and—most importantly—save lives.Sickbay delivers data where and when you need it: programmatically, visually, in batches, or streaming—without friction and without delay. That means your teams, whether they’re clinicians, analysts, or administrators, can act faster and smarter using data that’s already there.What You’ll Learn:
- Why patient monitoring data isn’t just nice to have—it’s life-critical
- How Sickbay eliminates friction in data access, empowering care teams
- Real-world examples of data-driven decisions that make a difference
- Expect an insightful session that connects the dots between data and the work of saving lives.
Podcast Transcription: Sickbay Sessions 2
Jennifer: Thank you all for joining us today. I’m Jennifer Lazars, Director of Marketing for Medical Informatics Corps. We are thrilled to bring you the second episode of Sickbay Sessions, where we’re exploring how cutting-edge technology is revolutionizing bedside and remote patient monitoring, optimizing clinical workflows and closing the data gap in healthcare. Each session of Sickbay Sessions features industry experts and innovators to discuss the latest industry advancements and solutions throughout the session. Today, if you have a question, please feel free to enter into the Q and A section down at the bottom of your screen, there is also a chat function there where you can chat with other participants and our panelists. Here at Medical Informatics Corp, or as we call it, MIC, we’re proud to deliver next-generation technologies to unify patient monitoring workflows for Healthcare systems. Our focus is on every patient everywhere, monitored by people who care and powered by actionable data. Joining me today for our session is Raajen Patel, the Executive Vice President of Innovation and Client Engagement here at MIC. He and his team are going to be talking about Sickbay’s frictionless data delivery and how it supports clinical care and research efforts. So I’m going to hand it off to Raaj.
Raajen: Hi everybody. Thanks for joining us today. Last time you heard a little bit about why Sickbay was created, and today, I’d like to talk about delivering a data baseline to promote team-based conversation around care we’ve gotten used to frictionless transactions. I remember when I was a teenager, I had to be at the TV on Fridays at eight o’clock to watch X Files. But today, my kids can watch any episode of Bluey at any time on any screen. And this is true in a lot of you know, a lot of aspects of our life, shopping, paying bills, music. We expect this now, especially when it comes to data, and we get impatient when it’s not the case. Now in hospitals, impatience might be a virtue. You want information because lives are in the balance. You want to see the last six hours of heart rate before a patient goes into surgery, or see how fast medications they were given worked the last time they were in the ICU, or if their alarm limits were set at too narrow a range last night. And when we talk to our users, we hear a lot about the friction they feel in getting data they care about. So we at MIC built a product that is designed to get you that data as quickly and as easily as possible. It minimizes the distance between you and your data. Now this turns out to be a really hard problem, but it’s worth doing, because often you are effectively locked out of data. You have already paid for data generated by measuring your patients. It’s as if you wrote a song and now you have to pay to listen to it. You have to pay to put it in a playlist, to access it, to review it, to add it to your collection. So instead, Sickbay delivers data streaming in batches to one person to many people, on demand at no additional cost, like my kids deciding to watch different episodes of Bluey all at the same time. Frictionless data means you never have to wait to provide some real examples of Sickbay’s data delivery capability. I’d like to bring in some of my colleagues. They talk to our clinical and research users every day about how Sickbay helps them provide better care and insight. First, I’d like to introduce Joellan, a nurse who supports our pediatric clients.
Joellan: Hey, Raaj. Thanks for having me. Yeah, yeah, thanks. My name is Joellan Mullen. I am a nurse by trade, a pediatric nurse. I worked in the ICUs, as well as quality and safety, and leadership. And today I’m going to go over our core platform, which includes three applications. The first one I’m going to go over is PatMon. PatMon is viewing one patient in near real time. So, some use cases for this is looking at a high acuity patient. For example, I just have one patient, and I’m outside the room doing my documentation, and I can watch this patient a provider off the unit when they’re when the bedside staff calls them with concerns, they can pull this up wherever they are and look and see what’s going on with the patient. In addition, you can also look at the EKGs, so you can make your custom view that you want. So, if I am a cardiologist and I want to see an arrhythmia in real time, in near real time, I can look at this and adjust and provide the nurse with a. Different things that they can do. So, this is looking at one patient, which is great, but sometimes you may want to look at multiple patients. So, this is MultiMon. MultiMon is viewing multiple patients at one time. So, some use cases for this is watching all your patients when you’re in your assignment. You can actually minimize it down to the bottom and watch your patients as you’re doing your EMR documentation. So, this is great when you’re outside the room and need to get caught up on your charting, but still being able to keep an eye on those patients. You can also imagine this is a huge TV on the side of a wall where you can watch your entire unit, so a charge nurse can see what’s going on throughout their entire unit. A provider team, if you know, they only have a section of the unit. They can watch that section. You can watch patients for multiple units, not just the one unit. So, if I have watcher patients throughout the organization that I want to keep an eye on that I’m worried, you know, what’s going on with them, I can do that as well. And again, if I’m respiratory I can actually pull up different wave forms. So, I can pull up integrated devices and see what’s going on with all of my ventilated patients. So, these two options are great for near real time. But what if I want to see what happened in the past, or I want to take a closer look at something? This is our Patient Hx view, and what this is, is the view that I’m looking at right now is a trend, so it does incorporate all those devices. So, I have my respiratory rate for my ventilator on here, I actually have labs and meds that I can view. And so it’s a great trend to see what’s going on with the patient and also see the trajectory of what’s going on. So for example, I could use this when I’m getting handoff from the previous nurse. The nurse could tell me, as my blood pressure was trending up, I weaned down my more EPI, and I’m keeping an eye on my lactate, because my lactate seems to be climbing. So, if I’m a visual learner, this is great for me when I’m getting a handoff. The other thing you can use this for is event review. So, as you can see, this patient has a DSP right here, I could zoom in on this and actually see what’s going on and pull in other signals, such as my FiO2, etc, to see what’s going on. You also can look, not just at trends, but you can actually look at waveforms. So again, I’m a cardiologist, and I actually want to see what’s going on with different arrhythmias going on with my patient. I can pull this view up and see what’s going on. I do want to transfer now to my colleague, Carol. She’s going to discuss how to use this data to enhance documentation.
Carol: Thank you. Joellan. My name is Carol Morris. I am a Clinical Engagement Specialist. My background is nursing in the ED as well as having my Masters in Informatics. Now I want to talk about automation. Bedside staff continually to perform tasks that can be automated. Automation would free up time to focus more on the patient. An example of such is placing an EKG strip into the EHR. Automation has a large effect on the patient, the bedside staff, the care team and the hospital, as I’ll explain, as a new nurse, it was drilled into me, if your patient is on that monitor, they need to have a strip in the chart. This entails printing a strip at the nurses station, finding the paper with a little fixed stickers, or at minimum, a plain piece of paper, and then taping it off, the strips would then be put into the paper chart, which at some point would find its way into the patient’s EHR. Needless to say, this would take more than a few minutes for the process to be complete, and only those people that actually had direct access to the paper chart, would be able to see that strip until it was scanned into the EHR, which typically was done at discharge. This is the case for both standard strips into the chart, as well as those that were run during an event. With an event, you would hope, hope that you were able to find and print those sections of time when the event occurred so that strip could be reviewed. So, I would like to share something with you when using Sickbay drip export, it is a very, very simple process. Find what section you want to place in the chart. Complete the measurements using the calipers on the page. There’s a cup here and a caliper there. It is just a simple click and drag, and you get your measurements. You select export, export to EMR, you make your note, and you click Export. It’s as simple as that. The strip goes directly into the patient’s EHR as a PDF, which has the patient’s information as well as a live URL. This URL will take you to this page. So, who’s ever looking at this, we’ll be able to view exactly what you’re looking at, and then dive in deeper to get a better understanding of the patient’s situation, whether to evaluate a baseline rhythm or an event that occurred. In addition to exporting to the EHR, you do have the ability to share a URL by clicking Share HX link, copying the URL and sharing it over secure chat with the team for an event that occurred, a situation that is causing concern or something that you just want to look into later, you can save by finding the PHI and using it as a teaching. On a patient by patient basis, this saves time, freeing up the bedside staff. It improves communication among the care team and on a hospital level, the time saving is exponential, as well as the ability for the hospital to identify and support telemetry monitoring shortages, another way that you can see What is going on, the effectiveness of the strip export site that I work with identified capturing EKG strips was somewhere they needed a little extra help. As you can see, they have significantly improved. They’re monitoring roughly 380 patients weekly, per week, they’re exporting over 2500 scripts per week, and then since starting, they’ve exported about 200,000 strips, many with those annotations. Now here is my colleague, Megan Sisson, who will be discussing virtual care at scale.
Megan: Hey, thanks, Carol. So similar to Carol and Joellan, I also have a background in nursing. So Joellen mentioned how clinicians can use Sickbay at the individual level, and Carol introduced the power of having fixed workflows in disseminating this information into the EHR. I’ll go over the value of Sickbay to standardize and centralize virtual care. Sickbay supports virtual workflows for remote clinicians, including MDs, RNs, RTS and telemetry techs. These clinicians are getting all their patients physiologic data in near real time to process immediately. They can also use Sickbay to view historical information to dive into what led up to an event. Many sites are using Sickbay for virtual care in a many faceted way. There are health systems that are watching up to nine different satellite hospitals from a single central location. Another client is using Sickbay to watch patients from across state lines, while even another client is using Sickbay to watch patients via telemetry. And then we also have clients watching critical care patients. This makes Sickbay flexible to fit budgets, staffing and unique workflows for many different health systems. When a team of clinicians are watching a large amount of data and a lot of patients at once, it can get overwhelming to keep everything on track. These teams have a massive amount of data coming in, and they need to remember what’s baseline for one patient, what’s out of range for another patient, and which bedside nurses may have busy assignments and may need additional guidance. Processing all of this data at once and providing actionable insights is what Sickbay is designed to do in our virtual ops application. Hospitals use risk scores to act as a way to signal virtual staff when a patient is out of a desired range, whether that’s based on blood pressure, heart rate, lab values or even customized signals. These virtual centers have allowed hospitals to safely hold ICU patients in the ED while awaiting placement into the unit. It has decreased the amount of code blues that have occurred while these virtual nurses, MDS and RTS, assist the bedside team, and most importantly, it has helped patients by being another set of experienced eyes that can navigate events before they come, before they become adverse. So Sickbay is a way to power these teams, by not only delivering data, but by helping sort through a large amount of information in a seamless way. So I’ve talked about the data delivery at scale for clinicians. Now I’m going to transfer it over to my research colleague, Sudha, who will be talking about what data delivery at this scale means for researchers who are building the next generation of clinical insights. I’ll give it to you, Sudha.
Sudha: Thank you, Megan, and thank you for having me. So I will be talking about how is Sickbay leveraged for research. Now, as you’ve heard, Sickbay is recording the full resolution data from all the patients in all beds at all time. And as you can imagine, that is very powerful. Sickbay also provides a research infrastructure. You can access you can create cohorts, you can create studies. You can tag data. You can use a MATLAB package or a Python package to analyze your data. I’m going to illustrate this with an example. Stories are always better. There was a neuro intensivist at one large client side who wanted to study intracranial pressure data. Not only did he want to study intracranial pressure waveform, he wanted to particularly use this waveform from external ventricular drains. So in the neuro critical care ICUs people are there at ICP waveform is commonly measured to understand whether they are having a neurological crisis. If there’s too much pressure in the brain, and a fraction of the patients have full resolution or continuous ICP monitoring, it’s all the time, but most people have evds, which is therapeutic. They’re draining fluids or blood or extra pressure that builds up, they actually drain it for therapeutic reasons, but when the nurse or a doctor clamps the tube, that’s when the waveform becomes accurate. Now the doctor that the researcher wanted to study the waveform when it is clamped, and when the signal is more accurate, and down the line maybe to predict deterioration. This is an example where clinical data could normally be lost. So I’m going to show you an example. This is a three hours of ICP and EVT data, and you can see this is like one minute when the signal was clamped. So it’s like nobody in the in the room in the ICU was actually noting times down. This is when we clamped the data. This is when we clamp the waveform. No, but you can actually regain that information by analyzing the waveform, and that’s the power of research. And, you know, doing this at scale, one of the questions asked, well, you’re just clamping this one minute every hour, for example, how much data could you possibly have? And that’s when we started to we built a collaboration, and a whole big research study came out of it. Here are some examples of how the data looks when it is clamped, and we just want to extract data when it is clamped and ended up being machine learning algorithm was built. The MATLAB package was used to download data and just from five subjects, a model of was built using 33 bed days of continuous data out of which we once we had the analysis done. We figured out that 4.5 bed days was clamped ICP data and at scale, 200 subjects data were extracted for further analysis. The first level of the model was published in a paper. It was performing at a 99% accuracy. But this is an example of how Sickbay data can be leveraged from multiple patients by multiple users at scale to solve very specific problems in that field. And with this, I’m going to hand over back to Raajen.
Raajen: Thank you, Sudha, a heartbeat takes less than a second, and during that time, a clinician can see that heartbeats ECG from the bedside, from the hallway, from their office or at home. Every clinician on the team can see that ECG, that ABP, that vent setting, they can document them and share those trends with the care team. At the same time, virtual clinicians in their command centers can see all of this data across a unit, and researchers at that facility can. Use that data to build or bolster algorithms that provide specific clinical insight to the patient population their colleagues see every day. Now without Sickbay information becomes harder to get to as you revert to older methods. Think about when the EHR goes down. It’s an incredible disruption. How many hours does it take for you to recover from things that were supposed to be automatically recorded and without Sickbay? You have to be at the patient bed at the right time. You have to recognize that something is happening to that patient right then and then persuade the team to move in a new direction with a common data baseline that Sickbay provides, anyone can spark a conversation. Sickbay lets you talk about this meaningfully. If you only have point data, you have to infer what happened in between, and this increases risk patients fall into the cracks. By losing the data, you risk losing the patient, and you risk morale to the team. Everyone in the hospital is working in the service of saving lives. And the technological systems that generate data from any device, from any EHR, many camera should serve you, whether the system is flashy or plain, as long as it’s useful in helping you care for patients. And Sickbay helps everyone, clinicians, quality folks, but also it, researchers, data analysts, work in best service of why we work in healthcare. Sickbay solves a hard technological problem so that you can focus on solving the hard human problems in subsequent webinars, you’re going to hear more about clinical and administrative uses for this system, but for now, thank you to my team, and thank you for your attention. Back to Jen.
Jennifer: Thanks, Raajen, we appreciate it. I’m going to echo that. Thank you to everybody who spoke today on our webinar. For those of you who might have had questions throughout again, those were in the Q and A feature if for some reason, we didn’t get to answering your question, please know you can always find us at sickbay.com you can find us on LinkedIn and Facebook as well, and we’re happy to have a conversation with you anytime about those questions. I want you to pull out your calendars and mark a time for June 12, we have our third Sickbay Session: Pragmatic Improvements in Clinical Care, using Sickbay to feasibly improve outcomes. Scheduled for that date. Information for that will also be on our website and LinkedIn as well. So, thank you everyone for joining us today, and we hope to see you on June 12.
