I work for a company called Capsule Tech. In the brochure, it says Qualcomm Life. We’ve just had a quick change in company ownership and I won’t go into any of that, but basically we’re a company that’s been around for the last 20 years. And what we’re dedicated to is to be delivering intelligent care by integrating medical devices so that you can realise the data and be able to do miraculous things for all clinicians out there. So that’s a brief introduction about what we do, and I’ve been in health care for the last 12 years almost now, and I love it. And I love the challenges that it represents and I love the fact that we stand for what we do and how we help people.
So we know that in the last decade or two that the world has significantly changed, and what’s really changed is about our expectations, and our expectations is that we have data readily available to us instantly. Who here doesn’t do online shopping? In fact I probably should have said who does then you can all engage and you can all put your hands up yeah OK. So everybody here does online shopping. And when you access via your tablet or your phone or laptop, you expect the data to be there instantly for you. You expect to Google and, you know, no longer have we got the old dial up internet and we’ve got all the time lag, you know, we expect the data to be instantly there, available to us when we’re in our home personal environments. And we have a lot of connected devices, so the few that I just mentioned there, and within those we have an enormous amount of data. And that data is now driving more and more analytics to be able to change our behaviours, to be able to look at what we purchase and what we choose to do as well.
You know, if you think about social media and Facebook, how many times do you get a little popup to say ooh you must be interested in this or this group, this social group; when you’re buying and if you look on Amazon, when you’re looking to buy, that now turns around and will say to you, how about we recommend this for you. Because what’s happening out there is the analytics is looking at your behaviours, it’s looking at what you’re driving in terms of data on the internet, and it’s deciding for you and giving you options about what you might want to do. And for us we’re just accepting that as normality. At the moment I’d say not all of us are happy with the recommendations. We’re a bit like hmm that’s a little bit imposing. But we’re getting more and more used to it. And actually 30% of us will then click on the recommendations. So gradually we’re getting more used to data and technology informing us and telling us about perhaps the options that we need to therefore take.
So, how about in health care, well we realise that having access to data is probably our first step. And in health care do we have the access to data? Well it probably doesn’t surprise you, you probably know in your own hospitals that less than 0.1% of data that’s produced in a theatre or operating room, whatever you call it, or within the ICU, is actually recorded anywhere. So a patient is sat there generating data, 99.9% of it goes nowhere at all. And the data that does go somewhere, it’s like 0.1% of data, most of the time is being manually transcribed, and therefore 20% of the time it’s inaccurate information. So clinicians are making decisions on patients based on one in five is inaccurate information.
So we have an enormous amount of data that’s within the ICU, or anywhere within the hospital, but it’s not being used. And from a clinical perspective, it’s not their fault. We don’t have access to data at the moment. We don’t have necessarily the sensors or the tools to be able to allow them to deliver a more intelligent care. Like we expect to in our private, in our commercial worlds, we expect to be able to have instant access to data to help us aid decision making; unfortunately, at the moment, within healthcare, we’re a little bit behind and that isn’t happening. And what is happening is complete data overload. What you’ll commonly find is that a clinician, say, in the ICU area is expected to manually look at all of the devices, manually transcribe and interpret the data and preventative by actually trying to find out what’s wrong with the patient and analyse the data right in front of them. And it’s just not humanly possible to be able to do that.
We just can’t take in all of the data that surrounds, we’ve got thousands of algorithms that comes out of the patient to be able to understand what’s wrong with them, and we just can’t process that. We just don’t have the protocols within our brain or most of us anyway to be able to process that data and be able to predict what might happen to that patient and what we should be doing to care for them. So what’s happening with our clinicians is that they are overloaded with information, they’re getting frustrated. And it’s not just the fact that they don’t have data available to them so say in an EMR, it’s things like the alarms that go off, we have alarm fatigue, it’s quite a significant problem, and we don’t have the facility to say we’ve got all of these devices, they’re surrounding the patient, they’re from different device manufacturers because it shouldn’t matter, OK. You know, if they’ve got a high temperature, high blood pressure, SP02, or if they’ve got all of the problems, send me one alarm to be able to inform me that that patient does need care. So we have increased pressures on our clinicians and they just cannot deal with data in the way that we think that we can send it across. Because what we can’t do is just say to our clinicians here’s all the data and dump it in front of them. We need to provide it to them in a more contextual way that they can understand and comprehend.
So, if we’re going to deliver a more intelligent care, it starts with capturing and utilising the data. But in the first instance we need to capture the data. And data most importantly needs to be in near real time. There’s no point in looking at the data that’s a day or two old and then making a decision on the patient. It needs to be hundred percent accurate, you need hundred percent confidence in the data that you have. It needs to be in near real time and be accessible wherever you are with that patient, so at the bedside with their patient. The data also needs to be secure. Now I know you’ve just had a session on cybersecurity so hundred percent security, and the fact that the data that’s flowing through, there is no patient identifiable data that flows through, but you need to know that it is secure.
So what we need to look at for a clinician is really to simplify things, and it’s in three easy steps. Simplified documentation, so if there is a clinical system that’s being used, then the data needs to flow into that system automatically. We need to be able to reduce the charting time. We need to be able to completely eliminate transcription errors so that we’re hundred percent confident in the data. And the data needs to be recorded accurately and timely. We then need to simplify surveillance. So we need to be able to look at what’s around the patient and we need to minimise the noise that’s going on by all the alarms going off. So we need to be able to streamline those. And we also need to be looking at waveforms and having other types of data being available in a digital format for the clinician to be able to make decisions on the patient. And the third thing that we need to be looking at is about evidence-based interventions. And it’s about delivering the more personalised care. And it’s about having these protocols or algorithms that when you’ve got access to the data that you can use to then say OK this is what might be wrong with the patient. Present your clinician with some options about the care that they might want to deliver. This isn’t taking over saying this is what you have to do, you still need clinical input, but you need to have the data, the algorithms there, to be able to present to the clinicians so they can make a more informed decision about the type of care that that patient needs.
So if we have all of those factors, we have improved satisfaction from a clinical perspective. They are, I’m not then burnt out, we decrease turnover, and we’ve proven that, and we don’t get issues where we’ve got long lengths of stay, patients being readmitted, and we get better outcomes as well because we’ve got the data that is flowing through that is enabling our clinicians to be able to make an informed decision. It may surprise you to know that 6% of patients die because of inaccurate data. That’s quite a lot. We can avoid it. It’s really quite bad, but unfortunately it does happen. And it happens, I mean when I said 20% of data is inaccurate that goes into a clinical system where it’s put in via an iPad, it doesn’t matter. When we’re coming towards the end of a shift, it goes up to 45%. And even then it doesn’t even go in. Because, you know, it was something that was 10 hours ago: oh well, you know, it’s the end of the shift, it does happen, it really does happen.
But we can’t just say, OK, data from our medical devices. We need to be able to just put that into the EPR or into an analytic system or feed it into alarms, it doesn’t just happen. Even if it’s all in a HR7 format, and I’m not technical, you guys are the experts here, but it doesn’t just happen. And you need to be able to have a solution that’s agnostic. That can connect all of the medical devices, no matter what manufacturer has made them and what type of medical device it is, whether it’s a pump or it’s an ECG, and be able to contextualise, transform and transmit this data to all your different solutions that a hospital would need. Some of those solutions might be the EPR or the EMR; some of them might be for alarms as we’ve mentioned; or into some kind of analytic solution. Sometimes because the data is flowing, from a clinical engineering perspective what you actually might need is just a visualisation of where the device is, is it in use? And what’s the usage of it? Because, if we’re looking at purchasing, to be able to understand usage of your devices in a single platform layer is really quite critical, so to have a solution that does that and to really just bring it all together.
But if we look back at intervention, we, 18 months ago, and I’ve talked about this a few times, over in the US we got FDA clearance for a new type of medical device which is software as a medical device. And for six years we worked with the University of Massachusetts on developing this software, it’s called Alert Watch, and we’ve also been working for the last 18 months, since its release, and we’ve got about half a dozen in hospitals now in the US using, within the theatres, within the operating room, software that is about driving intelligence. It’s about having data flow through into the clinical, from any clinical system, from medical devices, from labs, from the flowsheet, into a presentation layer that can then inform the clinician about what’s going on with the patient in real time. And this isn’t, it’s not easy as anaesthetists say in the theatres to be able to look at all the devices and be able to quickly analyse and say OK this is what’s wrong with the patient or this is what I need to do. It’s why thousands of algorithms are run every second with this type of software and this is why we need data to be able to produce an analytic solution like this one. And this is just an example.
What I’m trying to represent here really is about connecting the data in the first place so that you can realise things like this and be able to help clinicians make more informed decisions about the care they give and give a more intelligent care delivery. And when we worked with the University of Massachusetts we actually reduced heart attack by almost 50%. And this is during and post-op and it was on 30,000 operating procedures over the six-year period, so this is not a small study. Kidney failure was reduced by almost 25% and deaths were reduced by almost one third. So the fact that we have data collected from the patient and then we visualise that data, however its visualised, we then save patients’ lives. And, you know, this is proven. So one in three people didn’t die just because we collected and we visualised the data.
So I have this slide next which is about the value of unlocking the data and integrating the data, but surely the real value is the fact that we can save lives. And it’s proven that we do. But it’s beneficial across all areas of the hospitals. You know, we talked a lot about the clinical benefits, having more access to data: 0% error in transmissions. So that means 100% of the data that you’re making decisions on as a clinician is accurate. We’re therefore reducing risk, improving patient safety and improving the quality of care delivery that we give. Along the way we save hours of nursing time. And as an IT and clinical engineering service that really is crossing over now, we’re able to make sure that you have a single integration endpoint. So you’re not having to do development work and working with each manufacturer of medical devices, but there is a single layer so that you can connect and visualise what’s going with those medical devices as well. And we’ve been working I think as I mentioned at the beginning in the UK. We’ve been a company globally for the last 20 years and for the last decade in the UK market here.
At this hospital statistics represents Cambridge Hospital that we work with, and we produced some ROI case study 2.6m equivalent in annual time saving costs. Now they didn’t get rid of anybody, so nobody was made redundant, but they absolutely foresaw in terms of how much staffing that they would need to increase if they kept with manual transcription across the whole hospital. So that’s a significant savings. And then if you look in terms of the theatre, they’ve actually increased productivity. So they were actually last year and I expect this year-on-year 235 more procedures that they were able to carry out because they saved three minutes per patient, directly because of medical device integration because the data was flowing in and flowing into the record automatically. And three minutes for a patient in a hospital bed, lying there waiting to have their procedure, is a long time. It’s a really long time. So to be able to again improve the patient experience is really quite important as an outcome as well.
So my message really is about being able to connect in the first place, to be able to have an agnostic connectivity layer that can connect your medical devices and be able to send it to any endpoint system that you need it to go to, to enable your clinicians then to make more informed decisions about their patients and be able to deliver a more intelligent healthcare. OK thank you very much.
Debbie Pope's presentation at the EBME Expo: Medical devices connectivity delivering better efficiency