Alcidion Group Limited (ALC) Earnings Call Transcript & Summary

I'm Jon [ Smart ] from Digital Health. It's Friday, which can only mean 1 thing of -- for those in our series of best practice webinars. Thanks for those of you who are joining us for what's an early start today rather than the lunchtime start, we're beginning at 10:00. And that's because our partners today are Alcidion who are based in Australia, and we're delighted to have Alcidion CEO, Kate Quirke, live on the webinar today. I'll be introducing Kate very shortly. And also her colleague Malcolm, also joining from Australia. Today's focus is to introduce everyone to the Miya Precision platform which is a new type of technology that is just being launched into the NHS that will help reenergize the health tech landscape. Miya Precision is the very first smart clinical asset for the NHS and it is already attracting the attention of health care organizations looking to accelerate their digital adoption, whether they're pursuing electronic patient record, best-of-breed visual program or an integrated care system joining up disparate systems Miya Precision offers an intelligent solution to unlock value from existing investments. And of course, that best-of-breed and complex kind of landscape is, of course, the reality for many, many trusts. Using its clinical decision support engine, Miya Precision provides the necessary automation to alleviate the cognitive burden faced by clinicians. Early communications about how smart clinical assets sparked significantly actually from Digital Health readers. And Alcidion is holding this webinar to share more detailed information. During the course of this webinar, you will be introduced to how and why the NHS needs a smart clinical asset as a new type of technology. You'll get detailed share of solution on how it works and you get an opportunity to ask a question of those bringing new product to market and supporters from the health care sectors. We've got a great panel team. We have Kate Quirke, CEO of Alcidion. Hi, Kate.

Hi.

And I'll be handing over to Kate very shortly. We have Malcolm Pradhan who's Director and Chief Medical Officer for Alcidion. Hi, Malcolm.

Hello.

We have Lynette Ousby, General Manager, U.K. for Alcidion.

Good morning, everybody?

Lynette's husband is keeping the kids and checking the background today and doing a grand job. And we also have Neil Perry, Director of Digital Transformation at Dartford and Gravesham NHS Trust, who are the reference side for Miya Precision in the NHS. As head of the format is roughly half an hour of presented material, and those presentations will be shared, as well, recording to follow. And then it's over to you for what I hope will be a really lively Q&A session. It's 10:00 in the morning, plenty of energy out there, so please don't be shy with those questions. Put your questions to the presenters using the Q&A chat tool, and don't be shy about using the up voting to kind of vote up the questions that you might care to kind of address, first of all. Okay. Hey, can I hand over to you and ask you to take it away.

Thanks very much, Jon, and thanks to everyone for attending the webinar today. As Jon said, I'm presenting from a lovely spring evening in Melbourne. To be honest, I was last in the U.K. on the last day of Digital Rewired and I flew back to Australia that evening. And I have to admit as it finished, I didn't have any real understanding about what the next 6 months might have had in store for us. I mean, we certainly knew COVID was an issue and hand sanitizer was at a high price tag at that conference. But the fact that I'm bookending that last trip with a virtual event, probably all I need to say about the global pandemic. In terms of a brief agenda, as Jon said, we're going to give -- I'm just going to give you an overview of Alcidion and our proposition to the U.K. -- to the NHS. Malcolm, our Chief Medical Officer is going to take you through the solution in more detail and give you a demonstration. And then Neil is joining us, which is fabulous to give you a perspective from a customer. I don't need to do introductions because Jon has kindly done those for us, so I'm going to get straight into it. As I said, I really am very excited to be able to officially launch our Miya, and that's -- we call it Miya. That's 2 countries separated by a common language. Between Miya and Alcidion, we like to put -- and make things difficult to pronounce. So Miya Precision, we're officially launching it into the U.K. at this point in time. But the NHS is, in fact, no stranger to Alcidion. And many of you will know us from 1 of our flagship products, Patientrack, which is an electronic observation and assessment solution. They've got a reasonable footprint in the U.K. Alcidion actually acquired Patientrack about 2 years ago because we could see the value and alignment between the data and workflow of Patientrack and the smart clinical asset that we developed here in Australian Miya Precision. So whilst we're officially launching Miya Precision platform now, we have, in fact, been working for some time with our first U.K. Miya Precision site, which is Dartford and Gravesham Trust. And I'm very pleased that Neil Perry has been able to join us on this webinar to talk about our partnership and why they selected Alcidion and in fact, our full product offering into the U.K. I don't like to steal his thunder by discussing it in any further detail. But it's important to note that we have a strong emerging reference site in Dartford for the Miya platform. And that's actually been part of our strategy of becoming ready to launch Miya Precision at this time because we felt it was important that we could demonstrate what we were doing into the NHS. So a little bit about who we are. We're an Australian health care informatics company with a unique proposition in healthcare technology. We've developed a platform approach to health care information. So that's what Miya Precision is. It's a platform based on FHIR, the Fast Healthcare Interoperability Resources, which is underpinned by leading-edge technology. And in fact, we fully -- the platform itself is really 2 to 3 years old in terms of the build of -- from a technology perspective. And we built it to support improved clinical workflows and decision-making in health care purely. Because we believe that data should be open, accessible and that, that data should be available always to support decision-making to improve patient safety and operational efficiency will follow. But really importantly, it needs to be done in a way that supports the caregiver and the day-to-day workflow of their decision-making processes, and that's where we're different. We've created an event-based platform that will ingest data from any source via any means, transfer and transform that data to FHIR, then enable access to that data through our own predefined capabilities such as clinical dashboards, decision support rules, a mobile EMR that makes all of the EPR data available direct to your fine. Command center capability, real-time analytics, and Malcolm will cover a lot of those in more detail, but it's important that what we're saying is that once you've got the platform and the data, you can then build on that with a number of different capabilities. As I said earlier, Alcidion has been a partner to NHS Trust for more than 12 years through our Patientrack solution where we've supported patient safety and the early detection of deteriorating patients in around 43 hospitals across the NHS. And we've got an established team in the U.K. headed up by Lynette. And that team has been implementing and supporting solutions into U.K. hospitals for many years. We're really very proud of the role that U.K. -- that Patientrack had in the U.K. And we see the NHS in the U.K. as 1 of our most important and strategic partners in terms of the opportunity to bring this new technology platform to a mature customer in terms of digitalization, but 1 that also supports a diverse range of suppliers and systems. So it's really very pleasing also to see that the U.K. government in their approach to modernizing the NHS has really been about using digital technology, but also that they're open to different ways of doing that and to doing that in an innovative way. So a little bit about the platform, which Malcolm is going to talk about in much more detail. But it's a platform that can act as an integrator of your best-of-breed solutions, bringing together those solutions by providing a unified look and feel across all of those. But it can also act as an orchestration layer for a regional view of data, which could be across hospitals in a trust or across trust in a region. But a third deployment is, in fact, as a platform of engagement on top of an existing EPR. Because what's important to us is that we really value the investment our customers have already made in health care IT. And we really want you to continue to realize value on that investment without having to rip and replace what you've already worked through. So we really want to work with other partners and suppliers to create an open ecosystem that supports the needs of the NHS. Today, what we will do is give you a more detailed look at Miya Precision and how we can support the digital health aspirations of the NHS. Of course, we're a bit limited in the time we have available, but we'll -- we'll delve really deeply, but we'll give you a really good overview. And of course, you're more than welcome to follow up with more depth later on. What our platform will allow you to do is bring together critical data real time at scale and then apply your own decision to put algorithms your own, rules across the top of that data. And then the results of that can be pushed out by your own mobile phone, via tablets and via dashboards to create a view of data across patients, across trusts, across regions. In Australia, we've actually deployed Miya Precision across all 3 of those scenarios that I've talked about. We have a site in -- just to get you some good Australian terms, Murrumbidgee. Local Health District, at Wagga Wagga Base Hospital, where we've overlaid the Miya Precision platform on top of the Cerner APR. And Miya Precision is providing a modern user interface via our mobile EMR capability or EPR capability throughout our Miya memory module. But also bringing all of that data into our platform real time has allowed us to create new workflows, clinical workflows in the emergency department, for example, identifying patients at risk. During that implementation, COVID came along. And what was great was we were able to really quickly integrate data from home devices, patients wearing devices, bring that into the platform so that we could support virtual monitoring of patients at home and support hospital in the home. So there's a number of scenarios that we can demonstrate in the Australian environment as well. We know we've got something there that will provide significant benefits to the NHS and where you are all placed at the moment, it can support both an EPR and a best-of-breed strategy in a trust. But it can also help consolidate across a region that's taken potentially both those approaches even within a region and use the platform to provide a single user interface across all of that data and effectively providing an orchestration layer for an ICS or an STP. So our approach also will support changing models of care because, I mean, they are evolving every day and look what's happened during COVID. So what you need is a platform that can easily change the clinical workflow unencumbered by, I think, the underlying legacy code that's in existence. Our approach is different. I'm going to hand over to Malcolm now to explain to you a little bit more detail why that is.

There was 1 button I was missing. Thank you. Look, thank you very much, and thank you very much for attending today. Our focus at Alcidion has been to improve patient safety, to improve clinical logistics and patient flow. And also to focus on improving clinical productivity because really, if we can't save time for clinicians, we can't get to the interesting conversations around how to implement best practice and implement decision support and evolve new models of care, which are really essential when dealing with generational changes in the population and things like pandemics. And at the same time, organizations have made big investments in health IT. And what we are really trying to do is to be able you to realize the benefits of those and maximize the value of those investments through -- in a way that's, I think, hopefully quite interesting to you. We are deeply committed to open standards and to interoperability. But let's face that no one's really solved all these complex problems in health care by just putting more data into a database, what we believe has been missing is a layer of smart infrastructure that looks at the data in real time and helps clinicians focus on emerging risks and really helping them in case they miss things in their busy daily practice. And so what Miya Precision has been really designed to be, an active, assistant in the clinical environment and not just a passive data store. So where are we at in health care really? In summary, the big investments in Health IT haven't really delivered the benefits we were expecting. And an example is after the big investments in EPRs in the U.S., preventable errors in health care is still the third leading cause of death. And while that -- we don't measure things in the same way in Australia and the U.K., traditionally, the safety data has been pretty similar. There's -- further to that, there's unexplained clinical variation everywhere in treatments, in management, in outcomes. And this is just 1 example, but it's all over the place because it's really hard to get evidence into practice. And the other side effect of health care IT has really been to actually add further workflow burdens on to clinicians. And for example, in New York City when COVID-19 infections were rising, Mayor Cuomo, 1 of his first acts was to indemnify clinicians against using the EPR because they acknowledged that it could slow clinicians down and have worse outcomes for patients. So the real message here is we're all here because we passionately believe that health care technology is a way to improve health care and to improve the health care of our communities. But the fact is that just buying the technology isn't enough, the benefits don't flow automatically. And in fact, in health care, you don't solve problems by accident. You have to design systems and processes from the ground up to solve complex problems. And that's what we've done with Miya Precision. So we got here through -- really through the popularity of the EMR adoption model, the MRAM model. And it's -- the dogma is that the further you're up the scale in the MRAM model tree, the better embedded benefits. And underlying this model actually is that increasing decision support will deliver those benefits and particularly at Level 7, upwards. And there was really good evidence for this model. So in the '90s and 2000s, there are many studies showing that with increasing use of decision support, you would get improvements in best practice compliance, in prevention and reduction in errors. Now what happened though is those studies were really from a few key institutions that have been developing their own EPRs over many years, really homegrown with integrated decision support and in close collaboration with clinicians for decades, sometimes. And places like Riggin Street and Harvard and Intermountain Health. And what we found since the acceleration of health care IT after the Affordable Care Act in the U.S. and accompanying high-tech PAC is that the systems that haven't been designed with CDS, clinical distribution point at their core haven't delivered those same kind of benefits that were published. But the underlying fact here is that -- the key idea is that decision support is key is to achieving benefits in health care. It's not a nice to have. It's really an essential piece. And we'll show you today how that can be achieved and how we can help you achieve high levels of MRAM maturity while reaping the benefits of your investments. And so we kind of operate in a couple of different ways. In Australia, a lot of our customers, as Kate mentioned, already have existing EPRs, and that's their system of record. So what we do is we take data out of EPRs and make it interoperable and it's really widely acknowledged that to get value out of EPRs, you need systems of insight and engagement. And these systems can help you deliver new models of care and do all the decisions support and all the kind of interesting stuff that we really would like to do. In particular -- particularly, that's really important because as we know with generation changes over the coming years, we're facing increasing patient complexity and increasing needs to stay out of hospital and new ways of doing business in health care. So we have advanced interoperability. We can also write back to EPR. So it maintains -- it's still the source of truth, but you now have this powerful toolkit with which to innovate. As Jon mentioned, in the U.K., we realize there's many best-of-breed installations, and there's been a lot of work in doing all that integration. But it's still complex to get an idea of what's going on through all that data and orchestrate new workflows between different products. So what we do there is we take data from those different systems, convert it into a FHIR EPR and really allow you to then have a consistent view of all that data. We can write back that data to various systems as you require. But if you have gaps in your product suite to get up the level scale, we can provide certain features to fill those gaps. And we have partners, for example, in areas that we don't do, for example, better with medication management. We currently don't have a PaaS component, but we so we work with all PaaS vendors out there. But we are talking to a number of organizations on building a best-in-class through bookings and PaaS capability. The other thing I'll just point out around the hospital aspect is that data doesn't have to be just from your best through, but coming from other places. And as Kate mentioned, we've been integrating device data. And because Miya Precision keeps data provenance. Very clear, you can run different algorithms on data from different sources, from general practice or from patients and clinical devices. So one of the big challenges, even if you do have a decision support and smarts in the system, it's really how do you engage clinicians and integrate to clinical workflows. And we have some key principles at Alcidion. And the first is that every click or tap with a clinician is pushing a friendship. And ultimately, while we all want to stay lives, people want to get home on time. And so fast access to data is really important. And another key insight is that each specialty is really a different business. You keep buying hospital systems, but actually different specialties have different concerns. And the other point, as I mentioned, is having something that's monitoring risk and identifying and highlighting risk, so you don't get clinician time to do all that work and getting logging in and checking all that raw -- essentially raw data to synthesize what's going on. The other principle is that we want to make the right thing to do the easiest thing to do. Health care is 1 of the industries where it really requires personal heroics of the staff, nurses and doctors and allied health just to get the job done. And that's not scalable. And before COVID, we were really concerned about clinician burnout and that's a real issue. So automating workflows and reducing cognitive load is really important. So what I'll do is maybe I'll just jump into a demo of the mobile app, which will give you an idea of how we surface this information, and then I'll talk a little bit about Miya Precision in a bit more detail. So what I'm going to do is jump over to my phone, which is just running Miya Memory. And for example, when you first launch it, it lands on a page, which is really about clinical risk. So here we've got -- what's changed since you've last logged in or if you've been first time in the day or been in theater or something. So what's changed? Over here, you can see critical results or abnormal, anything red or orange is risk and down the bottom is any vital science, and you can customize those in terms of the criteria by which -- as are regarded as risk. So if I tap on all patients, I get a view that immediately shows me where I need to look, what's -- which patients have got high-risk clinical results. For example, the Tom Abbot there has an INR, which is not looking too good. If I tap on that, within seconds, I can flick through the app and get to key critical results. I can acknowledge that just by clicking the acknowledge button, and we can write that back to different systems. So you have, for example, like EPRs, just so you have a unified view of what results have been acknowledged or viewed by clinicians. We have different views. If I go in there and just click assays. We have a variety of degrees. I won't go into all the different views in the app because we don't have too much time. I go to patients, there are many flexible ways of getting a list of patients, which are in the ED, it's a different tool, for example, how awards are structured or services or specialty units and consults can be viewed very quickly. So if I go to all patients, again, red or orange's risk. So off the top there, you can see there are some notifications, some have normal labs and observations for poor Tom. So we'll go in there and have a look. And immediately, we've got full access to the electronic health record. And again, red or orange is highlighting clinical risk. And if I go through, you can click on view chart and get into all sorts of details, and it's very fast. And we've seen us operating with high-volume monitor data, for example, and the clinicians really appreciate that speed. Monitors is interesting because it allows specialties configure using patient context and specialty context, what they want to see without having to dig through all that information. So for example, postop, what would you want to know as you're walking around? What's a glanceable view that provides you with that value rather than having to hunt through data? Because if you don't have that kind of filtered and smart view, really, we're just showing large volumes of data on a more inconvenient screen. So that filtering and decision support is really critical to giving clinicians that fast access to the data they want, and sometimes that they should have. So results I showed you, documents, medications and tasks, et cetera. We'll jump into documents for a moment. If I click there, we can quickly see what's been written in the clinical notes. And you'll notice, say a tab of the top right, which says concepts. And that's using our natural language processing to identify key concepts within clinical notes. So if I -- instead of having to read through 5 years of discharge summary is I can quickly say, "Well, what are the key concepts that have been talked about in those discharge summaries. And if I click on repeat fibrillation, I'll take you to the note and I can click on the top right there to add that to my problem list, for example." And there are lots of other clinical workflows and shortcut that you can implement that you can do in the app, which I won't have time to go into, but happy to talk about further later. Notifications are really a key part of how we surface decision support. So for example, here's someone with an INR that's an all but not super bad. But the fact they've got a procedure coming up means that they're at clinical risk. So we have a knowledge graph that helps to pull back relevant data. So again, we try to bring relevant data forward to the clinician. And down the bottom there is where people -- organizations can configure what are the immediate actions that people should take to mitigate this risk. If I click actions, then I can quickly automate certain practice elements. So again, making the right thing to do, the easiest thing to do. So that's a really quick -- we'll show you 1 more here, actually. This is another aspect of decision support using timers, which are really -- turn out to be quite a key part of monitoring health care. So someone starts in aminoglycoside in 2 or 3 days, we set a time to say, just to check if we've had best practice compliance, have had their kidney function checked, how they had their aminoglycoside level check. And so we can monitor not just the initial alert or decision support but have people complying with best practice. So that's a very brief preview of Miya Memory. And I'll just go now a bit of detail of how -- what's going on behind to make that all work. So as case set, we take data from all sorts of different sources, including APRs, PaaS community devices. From this point on, this is -- behind this box is Miya Precision. And so we convert everything to FHIR and we use a product called OntoServer to clean up all the data elements or as many as we can into standard terminologies and code sets. And we do that on the way in, not later, not -- it's not a warehouse that we just throw data, and we want to clean it up because we're operating on that data in real time. We spend a lot of effort analyzing what's going on in that data because you get very noisy data, you get repeated events, you get batch data. And what we're doing here is work out what has changed, and we generate some meaningful clinical events that allow decision support to act. We catch that data in smart ways. So there's no extra burden on those host systems that were not really designed for decision support. And we have a clinical decision support engine that listens -- that looks at every bit of data and say, for example, cracking value comes out and they'll say, well, actually, what have I got registered with me that is interested in that crediting value. And we have a business clinical rules engine, which is specifically designed around building and managing safe clinical rules and auditing and all that sort of stuff. We have various algorithms. We have -- for -- usually around flow, we don't use deep learning algorithms for patient care directly because we don't really trust them that much. We would prefer to -- we do emphasize explainable AI and rules, for example. But you can plug in what about algorithms you like, we're not going to be the source of algorithms, and we've done that in Australia with other organizations. Those outputs like auditors and risks and automations come back and put back into the platform and then sent out to devices and dashboards and to mobile. And so what's key here is that the event bus is actually the core of the system, not a centralized transactional database. And that really helps us operate on data in real time at scale. And I won't go into it, but we have all sorts of mechanisms to manage algorithm quality, data quality and audit and safety around scaling up decision support because 1 of the big problems is how do you manage all that clinical content. Another way of presenting that data back to clinicians is via web-based dashboards. And we've got a lot of different configurations and organizations can kind of drag and drop, configure these. And this is all receiving via FHIR events. And what's really interesting about this is it also helps to make explicit models of care, which are often implicit in different contexts. So for example, there are some studies showing that up to 50% -- 40%, 50% of patients with fractured neck and femurs don't get pain relief -- a documented relief for the 4 first -- say, 24 hours. And so we can put up displays that show pain scores to signal and send out alerts if people haven't been paying attention to that process views. And this is integrating chronic disease management, so it patient surveys as well as other data. And what I'll do is I'll just jump into some examples to show what that looks like. So is this a flow dashboard. So again, all these columns are configurable. We have a couple of hundred different configurations, and we're growing them. But it's not just display. The key thing is how do you get fast access to key workflows. So if I click on referrals, for example, it's like mini app that pops up some 1 click away from accessing the details. And if I want to add a new referral, it's 2 clicks. And then I can add a referral and so forth, which will be monitored. If I want to create a task, for example, I can look at adding a task within a couple of clicks. And these are just -- and these lists are all generated through value sets on the OntoServer so that you can code them and the system understand what's going on. It's not just all free text. And we can configure these columns in pretty flexible ways. When you go into the details, you can have a look at -- configure out a full view of the patient record. For example, and this is all very configurable. So you have access to all sorts of overviews, like activity for the patient, what's been going on in observations. For example, if I view to got risk-rated labs, for example, anything red or orange is risk as we talked about, you can graph those and you can narrow down views and sort of search through large volumes of data really quickly, with the view that you're identifying areas of clinical risk. Another area that we're working very closely with Neil at Dartford Gravesham is, for example, in the eNoting area -- sorry, I've just got to move this out. The way it zooms is blocking my button. So this is an example of our real-time natural range processing and really another good example of workflow automation. So for example, here if I just type a 67-year-old female with COPD, presents with cough and fever, no chest pain, for example. So within a couple of seconds, it's gone and done natural language processing. And you can see this is a pretty basic template that I'm using for capturing the data. But we picked up key concepts and they've categorized based on their oncology. We also pick up negation. We spent a lot of work on negation detection. So if I say, "Okay. Yes, this past medical history is, I confirm it." What it does then is it then adapts the form to say, okay, well, you might want to get respiratory assessment going here with your preconfigured form. You might want to pull back data to look at their baseline SpO2 over time. And because we understand the context of these different areas of I say, clearly, chest infection. And I if I start confirming these conditions, it will start to then automate best practice and plans, for example. I don't want go into the plans in detail. They're just examples. But this is a really great -- good example of how do we bring forward the -- or the decision to decision support in real time to assist clinicians with complying with best practice. And we do -- and I'll show you just another dashboard over here, which is, again, different configuration for a different purpose. But we've got a very -- you can go through quickly and see concepts like we did on mobile. But we also have a coding sheet here, for example, that highlights key conditions, and then you can track back to see which are the key documents that have -- from which they've come from. So it's a very powerful platform. We're trying to maximize the value of this in real time. We have a lot of examples out of the box, these key problems that occur, so you can have a toolkit to really start thinking about how to implement some of the safety and risk monitoring examples. We also have a warehouse capability. And as Kate mentioned, we don't have time to go in here, but hospital operation centers and command centers, so you can quickly look at those across the organization. And finally, really, just to summarize, we really talk about this as a smart clinical asset that takes into -- that can use all the clinical context in the data that you've got and operationalize that into real-time PaaS clinical interfaces to improve automation and improve patient safety and clinical workflow. So thank you very much. I think Neil, I'll hand over to you.

Thank you, Malcolm. And Jon, I've got to apologize beforehand. My PC NHS grade laptop is struggling a bit. So I just had to drop out and rejoin. So I've just sent my slide to Lynette, and Kate, if I do end up my screen crashing, one, I'll get a laptop as soon as I can next week. And two, I'll ask them to present the slides and I'll to talk through it. But I'll go as quick as I can to make it doesn't crash.

Okay. Well, we can hear you loud and clear, Malcolm. So in your own time.

Or Neil.

Neil. Sorry, sorry.

That's okay. Hold on then. Let me try and present then. Let me get -- seems I've switched over, sorry for that. And I've turned off my video. So just to get my PC that extra bit of compute power.

A bit of extra oomph then. If I just kind of briefly kind of fill whilst you're kind of getting that sorted. Malcolm, really, really interesting presentation. I may be kind of a geek here on a Friday morning, but if I wanted to kind of get a fuller presentation, presumably, that's something that you can happily arrange and if people are going to contact you directly to follow up, that's something you can put together, yes?.

Yes. Yes. we're certainly happy to go into key scenarios or key issues and into more detail. Definitely.

Great. We have Neil, not Malcolm. My apologies, Neil. Plus this elephant now on screen. So take us away, Neil.

Excellent. That's a mammoth from Mammoth Mountain, 1 of my favorite places. So hi, everyone. I'm Neil Perry. I'm the Director of Digital Transformation for Dartford Gravesham NHS Trust. It's really great to see so many familiar names on the -- tuning in today from both suppliers, NHS colleagues as well. Which am I presenting? Am I presenting the full slides or my notes? Just let me check.

Full slides.

Excellent. That's good. So yes, that's me. I've got around 22 years' worth of experience within the NHS focused on acute clinical systems. Are my roots are in systems development and systems integration, but I've worked with the majority of PaaS systems and EPRs and implemented them and continue to developing things over my growing years in NHS. So about Dartford Gravesham NHS Trust. Dartford Gravesham NHS Trust is located in North Kemp, Southeast of England right by the Queen Elizabeth II Bridge and Bluewater Shopping Center. So it's nicely located. We're a normal NHS Trust. Were about 550 beds across 2 sites spanning Dartford and Southeast London, and we provide a range of normal acute services. I say normal because I think it's really important as a normal NHS Trust, I believe, we represent around 80% of the NHS. We're not a global digital examplar. We don't get huge, great sums of money. We're not a teaching hospital, we are just a normal hospital. What I think does set us apart, though, is that we reset our clinical ICT strategy back in 2017. And we made a conscious decision to be an early adopter of innovative technologies. So we've implemented things like artificial intelligence for radiology chest X-rays and people might have heard me talk about that. And we've implemented real-time monitoring of remote patients with health sensors. And I think Jon's Digital Health has published articles on that and things as well. And we've got an ambition to be a self-made digital examplar. I don't think anyone's going to give us any big handouts and so we've got to do what we can with what we've got but we aspire to have a leapfrog strategy and meet all the trend setters in digital health and those are progressing really well, not where they are now, but where they plan to be in 5 years' time. So that's naturally our strategy. So we also a look to our North Camp there. So our digital strategy reset, I think a bit of background is worthwhile going into. So we kicked off our digital strategy reset in 2017 with an on-site tech show. We had over 25 suppliers come together in 1 place in our education center. And it was a range of modern technologies, from virtual reality, artificial intelligence and talks around artificial intelligence and patient portals and eNoting solutions and mobile, print tech and as I said, virtual reality and lots of different technology. We -- the day was attended by over 300-plus clinicians and managers, which is quite impressive, that's about 10% of our organization came together in the same place on the same day and sifted through different technologies, some of which were competing technologies. And they really got a good feel for the art of the possible and where they would like to go. And they became very vocal and got engaged and tell us what was wrong with IP as well and why it should be more like Google and Apple and Amazon and all those great technologies. After the tech show, we interviewed our execs and managers and our clinicians to get that kind of feel for what the problems was, what they felt they wanted to progress. And we looked at measuring our digital maturity gaps. So 2017 was the last national digital maturity assessment. I've got our scores on there that we self-created. And it showed that we were lacking in noting record assessment plans, meds optimization and decision support, and that correlated to all the stuff that we're seeing from our clinicians as well. We then went on and started to review the digital exemplars in England. We did a tour of the North where the majority seem to be. But we also look -- visited some in the south. And then when you looked further a field to Estonia and places in Europe that were doing quite innovative things, and we looked at America and Australia and New Zealand as well and sort of put together the key themes that they were doing. And they're very similar, but actually, they seem to be more advanced than us on the clinical side of things. So out of that strategy review, there's an 80-page document that -- with lots and lots of words, and it's really nice and pretty pictures and things. There's 1 bit that we go back to constantly, and that's a functional blueprint which we call Bob the Onion because it looks like a onion. But this functional blueprint is that coloring by numbers blueprint that we can work out, what we've got, what we haven't got, what's coming up for renewals, getting old and tired and things. And I'll come back to this further with showing -- I think it shows quite nicely the Alcidion suites, which parts of this, this really can cover for us. So as part of all of that, we assessed the market and said, "We started to view what are we going to do, we can do big EPR, mega-suite EPR Or are we going to do a best-of-breed solution?" We had a good strong PaaS already. But should we replace it and go for FNL strapped into the 1 system or not. Now mega suite EPR versus best-of-breed is an age-old topic that predates my time in the NHS and will probably outstretch my career as well. And I've still got quite a career ahead of me, I'm sure it will. But ultimately, we decided that replacing our PaaS was too high risk. It was a good PaaS, it works, and it would have been 2 time consuming. And we felt it would lose our clinical engagement. And we found the places that have done their EPR strategies through national program or post the national program, a lot of clinicians have kind of disengaged because they felt that the focus has been around the administrative functions first and not giving them the clinical tools they wanted. And there were lots of commentaries that EPRs are for bean counters. And actually, the feeling was that all clinical systems we have bean counters. And we wanted to try and break that and give them the tools that they would find useful. And if they find useful tools, they'd use them rather than us chasing them to use things. So we decided that best-of-breed will allow us -- allow our trust to build on the existing investments that we had, leaving the powers and admin systems in place and allow us to focus on delivering the clinical functionality faster. It's -- if also done correctly, we can decide that by putting together different suppliers that had their niche skills, it would build a good collaboration of suppliers and partnerships to work with and enhance our ability and knowledge as well. And it's definitely done that. So in 2018, '19, we tendered for the key parts that we felt were most important, our eNoting and mobile eNoting, quite importantly solution and then a ePrescribing solution. And as history has it, we chose Alcidion for both actually. So Alcidion for the Miya products and also Alcidion partnered with Marand -- Better By Marand for the open ePrescribing. And that's kind of going to feel a lot about jigsaw. But why did we choose Alcidion? I think this is key. I'm doing this talk kind of just made me stop and reflect. It was the clinician's choice, hands down, through a competitive process. It was a clinician's choice. We'll set them -- they were able to demonstrate a rich suite of clinical tools or assets. They were able to demonstrate the local customization and very agile as well, so in our demonstrations, things like clinicians would always come with a new challenge. And so actually, we want an eFORM that does this at clerk and needs to work. This week, we changed it this way. And we had to go to the rest and we're like "Sorry, can you show us what the clinicians use now?" And Alcidion stood out as being able to come back within a few days and say, "Right, here's the clerk and pro format to how you want it with natural language processing and with machine learning and with." And that really impressed the clinicians. Alcidion demonstrated knowledge beyond our knowledge in the use of data for real-time decision support with algorithms that were already premade from partnerships with hospitals all over the world, predominantly U.K., Australia and New Zealand, but the fact that it's stretched the globe was quite impressive. The global referenceability of Alcidion, that stood out for our clinicians. I was surprised at this one, but they were very keen to be able to go and talk virtually. We didn't get to go to Australia, but we Zoom called a few sites in Australia and phone called a few others. But also the fact that there were hospitals just down the road across the river in Essex, we went to pass them, saw them spend a lot of time. So thank you for that ham and tea. And I think the clinicians really got wowed by the benefit of being able to do free text noting with the natural language decision to help them drive decision support. I think that was the wow factor for them. So hands down, it was the clinician's choice across nurses, doctors -- junior doctors to consultants as well and wider AHPs. It was the techies' choice. My infrastructure team were very impressed with the technology stack, both the operating systems capability and Alcidion's capability to make it highly available. These systems support teams are very impressed with the machine learning capabilities. And the -- if that, then this, rules engine capabilities that Malcolm was talking about earlier, being able to put in data from everywhere and to set up the rules and things that the clinicians would want and be able to do that ourselves as well. And we were very impressed with the system integration capabilities. So the knowledge that Alcidion had and they would sort of help piece us together some of the really quite complex things of putting things like allergies and alerts and pushing it into ePrescribing system then pulling data back to be able to present back to our clinicians to make those decisions if something is wrong with the prescriber, like phosphate levels have gone too high, stop the prescriber from being able to administer those drugs and flag to the clinician through the mobile app that they need to get a clinical review done quite quickly on those. And it was the management choice as well. So the company size, Alcidion is not a one-man band. I think that's quite key to us. It's over 100 members of staff and a large development team. Alcidion's partnering ethos really sat well with us as to we didn't want to throw stuff away. We want to use what we've got, and we wanted to pull in lots of new innovative technologies like our smart wearables, like the AI and start to use that and patient portals and things as well in partnering with companies to do that. I don't know if I had drop out, as I said and reboot my PC, but Alcidion's been built by several different companies and 1 of those companies was a quite large and competent management consultancy. So they've got those skills with them as well, which are fantastic to be able to pull on. The global customer satisfaction and 0 attrition, customers not leaving the Patientrack family and now we are Alcidion family. But being able to sort of call on people in Australia and New Zealand, and I pronounce it wrong, but we spoke with [ White Marta ] and New South Wales, and we're all on very similar journeys. But although it's slightly different, but there is so much there to be able to share and that was fantastic pull on. And we also saw it as not a 3- to 5-year product we're going to buy and replace, but actually has potential to be a long-term investment and a company that we felt we could really partner with to start building that decent product ecosystem. So that's why -- that's why we chose them. They have been very impressive going through that. So in reality, what are we doing? So central to our project and that strategy is tight integration with our existing and future systems, which needs to be seamless to the clinicians. They don't need to know about the boxes, why not we talk about the H7 and the APIs and that kind of stuff, they just want to be able to use a product that works. And that's what we felt Alcidion presented to us. So the key today, single sign-on and patient contextual launch of all the other systems, but even more than that is using the atomic level of data from all our other systems to be able to push and pull data around and use it in these clinical decisions pools -- clinical decision rules engines' full decision support. The key components highlighted in my slide are the Smartpage products, which is the mobile product that we were resizing. And that's going to be our Bleep replacement. It's also the products that we see as being able to handle data from any point of other systems and be able to put it in and aggregate it and make those notifications available in the hands of our clinicians. So as I say, phosphate level has gone too high, they need to change the IV fluids and things that are going into the patient, they can get that notification really quickly and drive faster workforce task management and rules and things. The well-known Patientrack solution that's used in U.K., Australia and New Zealand, for e-observations and clinical risk assessments. We see that as the foundation of everything that we do and our clinicians to see that as they are the basics of what we need to be doing, and that drives a lot of other things. So we've got that, and we're pleased to say we're live across adult medicines with that now. And then there's the Miya product. Miya Precision is the highly modern new and fresh solution, which we saw, as Malcolm said, as an eNoting solution for doctors clerking action plans, preoperative assessments and integration back to our patient portal for being able to kick off patient questionnaires that they can complete in their own home and then send the data back into the Miya Precision platform. Post operative notes, our electronic discharge notifications, which become really, really keen, driving wider health care system flows. And our patient flow, both the ward dashboards have seen what's going on with the patients and then our site teams dashboards for the patient journey, to be able to give them lights on effectively across the trust as to what's going on. Then the partnership is set with OPENeP from our Marand. I think there are slides on the call. Hello. We're implementing that system, we should be live in the next year across 80% of our trust is our target. And those 2 products together are a fantastic combination. We've seen OPENeP in some of the trusts down in the south. And we've seen the management consulting capabilities of Alcidion and the 2 companies working scaler fantastically. And then all of this wrapped with this rules engine. There's actually 2 parts of the rules engine, the Patientrack 1 and the Miya one. But this is where we get to bring in all these artificial intelligence and machine learning concepts and capabilities and pool them together to really make our clinical data into an asset. So I think that's really key. So but going back to my functional blueprint, which has now gone blue. All the green parts on this slide, of the pieces of our puzzle that Alcidion will be able to provide for us. And I like these things -- you're close to do all of this stuff. So we started off and we're working on advanced inpatient module at the moment. But actually, the capabilities can go across outpatients under A&E. So once we finished with our own patients, we'll be looking to go back and get an eNoting and all these capabilities going across those, but the ePrescribing the clinical rules engine, bringing data in from medical devices and integrating it, machine learning and natural language processing and our Bleep replacement actually feels a large part of our functional blueprint, the jigsaw puzzle. So I think we're going to -- although we said we wanted to achieve this by 2023, I think we're on track to overachieve and hopefully get that done well before that, which is great. So some of the stuff that we're really excited about. We're really excited about the solutions advanced Predict and Prevent So AKI Predict and Prevent was developed with Western Sussex just down the road in South England, so full credit to them. This algorithm takes age and past medical history, clinical assessments and pathology data to predict and prevent acute kidney injuries in hospital and has been shown to reduce those along with cardiac arrests. So we're implementing that, and we're really pleased to be doing that. Also we're excited about the natural language processing that we've spoken about and taking the free text data, codifying it. We believe that free text clinical noting is necessary, and the natural language process and capabilities make that free text a clinical asset, pushing logic through rules engines and suggesting those appropriate actions and pathways to automate order sets and prescribing decisions based on the percentage of symptoms and clinical narrative is really key. Along with all those machine learning capabilities, help automate more administrative tasks and actions like estimate the length of stay based on how results have come back and preloading those order sets. And we see this as being able to help reduce the cognitive and administrative burdens of clinicians and the back-office staff as well. We're also really excited, and this is 1 of my favorite things about the ability to be able to pull in that medical device data and integrate the medical devices. So this is where the real clinical benefit comes in, putting data in from all the medical devices using the data together for algorithms and using machine learning logic to aggregate and correlate things like pathology and vital science and ECG and then spot trends like TCAS and PCOS and and hydration intake alerts. It's this level of capability, I think every NHS Trust needs to aspire to get to, and we're really excited to be able to have the ability through our wider clinical ICT strategy and now the Alcidion tools to be able to deliver on this. And lastly, quite a busy slide, but we're really excited about reducing the clinical burden. And I think this slide when I talk to it fully, shows that. But effectively, we're excited about the -- being able to integrate better with the wider health system such as putting past medical history and allergies and alerts from the GP system at the point of doctor's clerking so that part on there just change the slides to try and grab that. We're also excited about automating the clerking processes and preloading pathway admission bloods and radiology requests and other pieces, which is this orange part, hopefully come on screen, not sure how well it depicts it. We're excited about the sentinel monitoring capabilities and being able to use the data from all the different systems to help drive rules around ePrescribing and things like that and being able to push alerts into our patients when they get home and have those patients feedback as well for our partnership with Patients Know Best. We're excited about closing the loop on the data. So once the patient actually gets through the journey, we've intervened on ePrescribing things and stopped certain prescript drugs being administered and things if need be. We're excited about the discharge point. The fact that we'll be able to push the data back to not just the GP, but we've got ways to be able to push data back to the community pharmacies so that the prescription is ready to pick up. So the GP pharmacists are able to intervene, review what drugs have been changed through the inpatient's stay and make sure their records are up to date as well. And then we're excited about the partnerships that we've got with people like Current Health to be able to monitor patients at home and have Patients Know Best partnership that Alcidion are working with to be able to push and pull data and have really tight integration between those systems so that we can either intervene while the patients at home better or when the patient comes into our outpatient clinic for their follow-up, we've got all the data and much more data wider and all the rules and things in there and logic to be able to really help drive the pathway forward. So they're the things that we're really excited about. And I believe it's looking very exciting and very achievable, and we're glad to be on the journey with Alcidion and our joint wider community suppliers. So I think I probably have to run. But thank you. And I'm going to hand back to Jon.

That's great. Thanks, Neil.

And my PC didn't crash thankfully.

And you did well. I think part of your digital strategy should involve you getting your new laptop, Neil.

Monday morning on top of the list.

Great presentations. We're overrunning. Unfortunately, we're not in a hard stop in terms of the presenters. So we're going to add a little bit of extra time, 10, 15 minutes on to take Q&A. And I'm going to take them in the order that not -- in the order -- in the kind of order voted for questions. I'm delighted to -- say, that Tom your turn, the CSO of East Lake hospitals has shot to the top of the leaderboard with highest voter question we've ever had, 24 votes. Tom's question is in our region, we have 2 EPR systems and 2 best-of-breed approaches in the acute hospitals and different systems, in mental health trust, eNotes for our primary care, both Rego and eNotes for community, can Miya ingest info from any system in any care setting. Could you provide a single front end set on top of these systems, particularly in the acute hospitals to facilitate the flow of care of patients between hospitals across care boundaries. Well, a few questions in there. Kate, I'm going to hand that 1 straight to you.

And I'm going to hand that to Lynette, who's going to speak to us from a U.K. perspective.

And morning, Tom, thank you for the question. It's a great question. So the simple answer is yes, it can. So as Kate, Malcolm and Neil explained earlier in the presentations, we can take information and ingest it from a multitude of areas. And that's not just limited to care settings. We can take about from social care or local authority. We can consolidate all of that information together in a region regardless of the incumbent systems and present that in a uniformed way to the user. So hopefully, that answered all of your questions there.

That's great. The next 1 on the leaderboard is Craig Wood, Craig says hi there. Thanks for today's presentation. I was particularly interested to hear the overlaying of Alcidion platform over Cerner whose attitude is typically we can do every day, exclamation marks. Who drove that discussion? Was it the customer or a combination of the customer and Alcidion? Kate, I'm going to put that to you first.

Yes, I can answer that one. Obviously, in Australia, New South Wales, where this is actually deployed over at the top of Cerner, have been a long-standing user of the solution and it's actually across the entire state, and they have a very deep involvement in the Cerner stack. So they actually went to market to look for an innovation partner to be very focused on how they could mobilize the data out of there, what we call an EMR in Australia, and you refer to as an EPR. And how they could use that in an innovative way to support new clinical care models, and we were selected to do that. And so it was very much driven by the customer. And it's been very successful. And I think that solution and many of them that we've got other flavors like many of the other big U.S. companies deployed in Australia as well are billion still systems of engagement. But as Malcolm has shown you, I mean, billion still systems of record. But as Malcolm has shown you, the system engagement needs to be something that really speaks to the clinicians. And what Neil was referring to is exactly that. The clinicians need to be engaged in the use of this technology.

That's great. This next 1 I'm going to put to Neil, first of all. This is from our good old friend, Norm, who says, "Hi, coming from a site whose pairs will be coming up for renewal over the next few years I would be interested to hear more about Malcolm's comment around your past newer developments. Sorry, we'll take Neil, first of all, that would be great.