Author:Julie Coope

September 8, 2020

Painting the Picture with Data in Healthcare with Julie Coope

003 - GAT Podcast: Force Multiplier

29 min read

003 - GAT Podcast: Force Multiplier - Michael Jelen & Julie Coope Discuss Painting the Picture with Data in Healthcare

MICHAEL 00:00 Hi, everyone. This is MICHAEL Jelen, and welcome to the BRG Global Applied Technology podcast. The GAT team, as we call ourselves, are a globally distributed team of software engineers, data scientists, graphic designers, and industry experts who serve clients through our BRG DRIVE analytics platform. We're helping some of the world's largest and most innovative companies and governments transform data into actionable insights. Today I'll be speaking with JULIE Coope from our BRG GAT team. JULIE is both a registered general nurse in the UK and a registered nurse in the USA, with more than 30 years of commercial and clinical experience. We'll be discussing how JULIE applies her clinical experience to help healthcare professionals achieve better outcomes by painting the picture with data. As always, if you have any questions or comments, please email us at And with that, please join me and JULIE Coope. Hi, Julie.

JULIE 00:52 Hi. How are you doing?

MICHAEL 00:53 Great. How about you?

JULIE 00:55 Good. Thank you very much.

MICHAEL 00:57 Well, thank you so much for joining me today. I'm really excited. We have a lot of great stuff to talk about. And before we jump right into it, I was wondering if you could give a little background and introduction of who you are and how you came to be working in this team.

JULIE 01:11 Yeah. Sure. My background is a long one. I'm probably one of the older members of the team. I started some time ago nursing in the UK. Born in Salford, a good Manchester girl. Manchester as in the football teams, which will be known to many. I kind of grew up in very working-class background, pushed into looking for a caring-type role, which was typical of where my family had gone to. Nursing, I suppose, in those days, was seen as the carer's role, although certainly in the UK at that point in time, university nursing, which I know is more the norm in the States, was finding its own, and I was fortunate enough to train alongside university nurses as a trial. Grew up, did my nursing through Manchester, and then, basically, kind of as a bit of an accident, I ended up in America.

JULIE 02:21 Funny story, I suppose, really, because my friend who was desperate to go and nurse in America didn't want to travel by herself to sit the exam. We had to sit the American exam where obviously, we-- the English exam wasn't accepted by the US at that point. I don't think it still is now, actually. But yeah, my friend had decided to go over and spend two weeks revising for this exam. I said, "Well, I'll come along with you. I won't do the revision," because the US exam was very different to ours, "which means I will fail. You'll pass. And everybody's happy. I get a trip to America for a couple of weeks. All is good." Well, guess what? My friend failed and I passed. So I had the option then of spending a few thousand dollars to pay them back for all the investment in the study, or I just kind of thought, "Well, let's go and do it." So went off to America. I think it was quite a shock to the system because I think the relevance of what we're going to talk about today is more about how I as a nurse have learned to use data more to the benefit of the patients and the system. And I think the system that I left in the UK at that point was very much in the realms of we look after patients. NHS cares for patients. No real relevance of the cost of caring for those patients. We just looked after them and got them back out into society.

JULIE 04:00 At the point that I left the UK, actually, the NHS was introducing, very controversially, managers into the system to try to manage it more as a business. In fact, I was part of strikes and took place opposing management, the management being brought into the NHS. So it's peculiar the route that I've taken through life, and pretty much I've ended up working with those managers to try to improve the system. But yeah, I suppose long story short, I went to America and saw a very different system in place and a system where I think the facts and the figures had more of a meaning. Spent a short time in America nursing and then brought my skills of both the caring side and the provision of quality care and an understanding of the system and efficiencies of the system that I think I got to know a little bit more of in the States. I brought them back, and I went into private healthcare in the UK which was, again, a little bit behind I think the American system, but basically then gave me the ability to bring all together. So a bit more of the use of the stats and the numbers and the KPIs that I've learned but also gave me the ability to give good quality care because I had the system and the services and the funding behind the care that we were providing.

JULIE 05:48 From there, I think I was a little bit frustrated with being in one place trying to make good and actually heard about an organization that was starting to publish data around hospitals and the quality and the efficiency of their provision. And that took me over to Dr Foster. Dr Foster really bred my thirst for the use of data and I think has really given me a good grounding for how data can be used, not only to improve efficiency and productivity but quality of care which I think is somewhat lacking.

MICHAEL 06:38 That's great. And I'm very, very grateful for all of the tolerance you've given on our usage of Zs. And now it all makes sense as to why you've adapted so well to all the American slang that we use in the team. I do want to start by digging into a little bit around what you had said with your first exposure to data in the US system. So at that point, was that predominantly operational, or was that actually outcome-based as well?

JULIE 07:09 At that point, which, as I say, Jelen, was many years ago - remember that I am the oldest member of this team - it was very much operational. So patients came in. They had a length of stay given to them, and they were discharged on that point in time. We measured our length of stay by hours. We measured our productivity by the numbers of patients that we got in and out of the door, which, as I say, was a real novel concept to me having come from the NHS where patients just laid in bed really until you had a safe place to send them. So yeah, more operational at that point.

MICHAEL 07:45 Interesting. And how was that coming at it from a practitioner's perspective where, forever, the patient was number one and that was really what the focus was to a system that is a little bit more based on monetization and ensuring that hospitals are a business rather than simply providing care to patients? Did you see a big disparity there?

JULIE 08:05 Yeah. It was quite alien and I suppose a little bit uncomfortable. It did seem very production line-focused. And almost I had difficulty in bringing the emotion together with the business because I still wanted to give that care, but I suppose I had to separate the emotion in order to deliver the care efficiently but still have the quality hat on. But I think it's a very different system to walk into certainly from the NHS that I left.

MICHAEL 08:41 And how have you seen the reliance on data change, I guess, both in the US and the UK over time? If we start at maybe with operational data that the US was paying a bit more attention to back in the day, now we have things like wearable technology. We have far more sensors and far more data points that are available, I guess, throughout everyone's lifetime that are measuring our health. How have you seen that [crosstalk] evolve, and how have you been able to, I guess, dovetail your practical experience with providing care with some of the information?

JULIE 09:16 I think the journey that we have taken certainly in healthcare and generally with technology over the years is such a-- it's been such a fast one. Almost too fast for us oldies to keep up. And I think we've almost gone too fast. So we've gone from the point of the nurse being at the side of the bed measuring the pulse with three fingers on the wrist to it being totally machine-driven, which is producing so many data points, or as I like to say it, is giving us so much color potentially to the picture that we can see, not only of the patient and their health but of the organization performance. There's tons and tons of data presented in such ways that aren't always that attractive or usable or accessible by the people on the ground. And I have seen us go from the points of having very basic figures such as length of stay to details on how many times a patient will get out of bed, how many times they go walking down the stairs, what length of time it takes them to get down the corridor to get to theater, the respiration rate at that point.

JULIE 10:44 And I think one of the difficulties I have with all of these data points is how we bring it together to make sense of not only the patient journey but the whole operational performance. And I think we are at risk of-- at this point in time having too much data, we're overflowing with data, and we're having difficulty in drawing the dots together to paint a meaningful picture.

MICHAEL 11:19 I think that's absolutely right, and that's a trend I think we're seeing across all industries, not just in healthcare, although it is very prominent there. I guess I'm curious, how would you recommend that we take this information and we try to turn it into something that's usable and practical in our everyday lives or especially providing services to patients?

JULIE 11:42 Yeah. So I am very passionate about this and which is one of the reasons I came over to BRG, to be honest. As I alluded to at the beginning of our chat earlier, I spent a lot of time with a company called Dr Foster who takes data from the NHS and benchmarks. And they were one of the innovators of doing this and produce lots of different siloed KPIs. And I think what I'm passionate about doing, particularly for my own profession, is giving the ability to paint the picture, to tell the story of where there is potential variation but also how we can influence that variation. Which are the trigger points? Which are the pulse points for us to focus on? And I have been involved in quite an interesting project with one of our colleagues, Chris Donker, who's on our team, to look at how different organizations are at a different level of maturity of using their data.

JULIE 12:57 And it's particularly interesting when we talk to different groups of people across the healthcare system, some of whom are ultra wanting to focus on machine learning, artificial intelligence, and almost are so excited about doing that. We forget about how we're going to use the output of what the machines find and go back to the basics of the caregivers, the clinicians, the doctors, the nurses, the physiotherapists, just ones who understand how a patient moves through the pathway and how sometimes we get it wrong. Patients are not machines. We can't program them like an aircraft to travel at a certain distance over a certain period of time. We've got an awful lot of data, but we've got to learn how we pull it together and visualize it, not necessarily at the level of readmission rates and length of stay rates but a patient pathway. Let's understand the journey of the patients. And I think what we can do with the data is help to visualize it in such a way through collaborating with the people that use it to make it useful and meaningful to them on the ground.

MICHAEL 14:16 I think that's a critical point because one of the focuses on artificial intelligence or machine learning is always to achieve that human-level performance as quickly as possible, and we see that in certain areas like interpreting X-rays or potentially other radiology-type scans that could identify cancer or other issues in a picture. And while we're able to often arrive at a position where, for a one discrete task, a computer might be very good at that, when we take a step back, I think that what you had mentioned earlier about one of the reasons you came to BRG, this fusion of expertise and information is really the place where we all need to focus right now. As the amount of information increases, computing power is getting better, but we still need to tap into the brains of the people who have been on the ground day to day providing services to know actually when we put these two things together in the right hands of an expert, we come out with better outcomes than either a person or a machine individually. And I know you've been involved in training and teaching and working with nurses, especially in the capacity now at BRG, providing lots of client service attention. How have you been able to bring that knowledge and that training to other people to be able to empower them to paint the picture with numbers?

JULIE 15:41 I think you make some really interesting points there, Jelen. The machines are way ahead of us, and we're almost forgetting the people that are going to put what the machines produce into practice, and we've got to speed up and catch up, or we've got to understand that what the machines produce has got to be user friendly to those individuals on the ground. And I put myself back into the position of a nurse in the NHS XX years ago, where we were just fed a number and expected to do something with it. And numbers, often with nurses, we're blind to. Nurses didn't go into nursing to start reading data statistics, understanding SPC charts. But what I think is very easy to do is to start to review that data and understand where it falls in a patient journey and paint the pictures. Just paint the picture of the patient journey. And often, you get so many individuals in the room going, "Oh my God. That's right. We never thought about that."

JULIE 16:57 For instance, I was involved in a pathway review that was focusing on pneumonia patients. It was actually high mortality in pneumonia patients. And because, very often, organizations are producing data at a very high level, the key performance indicator level, they're not being tied together, but what we could do by looking at the whole of the patient pathway was understand that these pneumonia patients in a particular area of the hospital were, one, not getting the focus they required because they were in a remote part of the hospital, but two, when we actually started to distill the data, the patients were coming from a certain area within Manchester. That area within Manchester doesn't have a good provision of social care. So we couldn't actually get the patients discharged from hospital. All the organization was seeing though was that these patients had a long length of stay and that they had a high mortality rate.

JULIE 18:06 There's one thing certain in life is death. So if you stay in one place long enough, you're going to die. It wasn't an issue of poor provision of care, which is unfortunately what this organization were being labeled for. It was an issue across the whole of the system. But because the data had been so siloed and disparate in the way it'd been viewed, nobody could see that. So we couldn't see the wood for the trees. So all we had to do was basically pull out the data and start to paint the picture and add more color to the picture. So the more data we had, the more we could layer it. And actually bringing in the individuals who worked in that area in the community, again, it was jaw-dropping. You could see it straight away. You could see the picture of the type of patients that went in, the type of patients that we couldn't get out the other side. But unfortunately, it wasn't being viewed from that holistic point of view.

MICHAEL 19:10 That's fascinating. It's incredible how data can expose certain things that we may have missed on the surface just by simply looking at more and more instances of it. And I know as a result of that and the work that we've been doing collectively as a team, we've been developing a bunch of additional tools. You mentioned Chris's assessment of information maturity to understand where on that life cycle or where in the journey a given hospital or health system may sit. And I was wondering if you could talk a little bit about some of the other tools that you've been involved in. Because when I hear something like the story that you just said of looking at a specific situation with high mortality in pneumonia, how exactly would I be able to get to the bottom of that rapidly? And I think you've been working with some of the team members to build tools that help us speed up the process of identifying interesting information amidst the pile of huge sets of data.

JULIE 20:07 Yeah. From somebody who came from a background of absolutely hating figures - maths was my worst subject in school - now I just love to dig into it and love to kind of investigate and pull out the nitty-gritty pieces. But I think, as I mentioned earlier, it's really important to start reviewing all of the data that relates to that patient pathway. And I think there's sadly a focus on just the very basics of that inpatient stay. But we've looked recently at pulling out some imaging data that basically is focusing on X-ray reporting, variation in the use of X-rays which, only from a safety point of view, will have an impact on patient excessive X-rays. But it also relates back to clinical teaching and standardizing of pathways. But what we're trying to do is look at the whole of the patient journey and who interacts along that journey. So again, going back to that analogy of painting the picture. So patients coming into hospital will interact with histology, pathology, theater, imaging, many different services that are kept very separate and siloed. I think what we're trying to do is bring all those data sets together and start to understand, from a very high level, where there is variation, but drill it down right through to the individual's patient pathway and then roll it back up.

JULIE 21:44 So what are the influences of this patient's longer journey or shorter journey or readmission or infection? We've done the same for a number of areas. So not only pathology and imaging, but we've now got a big focus on theaters or our operating room, as you would refer to it. It becomes very, very interesting to start to look at the journey of a patient who is potentially sat in a hospital bed and then goes off to theater and might actually be longer on the theater table than would be the norm when you look across a whole data set. I think there's a great focus on theater utilization because of the business aspect of it. We need to get more patients through the theater system because that's the earner. That's where the income comes from. What we don't tend to focus on is how long those patients are in theater or how many times they're going back to theater or how long they spend in recovery and how that then potentially impacts their length of stay or the complications that come out of that.

JULIE 23:00 So we're kind of drilling deeper than those overall typical key performance indicators because the drilling deeper into an understanding of the patient's experience, the patient's overall journey eventually rolls up because patient experience equals patient efficiency and operational efficiency which then obviously leads to the income for the organization. So I think we're kind of at risk of not ignoring the patient, which we can get to through the data and roll up that data to paint a very, very colorful picture which will inform the operational team as to where to target. Because low-cost care is often better care because efficiency means a better outcome normally for a patient.

MICHAEL 23:56 It's so interesting that having the perspective that you do, seeing what things were like prior to the usage of data at all and moving into a world where now it's part of your daily life and it's very important, it seems critical that all that information is captured at the most granular level so that you are able to, as you said, both roll it up and drill down into whichever level of detail, whatever the trends are would emerge at that point. I was wondering if you've seen any gaps or where you think in the next, let's call it maybe, five years you would expect there to be some changes in the way that we use data, capture data, interpret data, and provide healthcare services to people. Some of these could be trends that you know are coming or perhaps things that you really want to happen and you're very, very excited about.

JULIE 24:49 Well, speaking personally and from a nursing point of view, one of the huge gaps is the use of the data that the clinical staff capture. There are masses and masses of data points. Nurses are constantly documenting about their patients, clinicians too, but the nursing record is often not ignored, but I don't think it's been tapped into as much as it could be. And I think the opportunity is to start drilling down into-- we measure individual doctors and their performance. The outcomes and the efficiency of a patient journey is often directly related to nursing staff. And I think it's almost remiss that we haven't focused on that level and have treated this profession as almost the forgotten profession. And this group of people can have a massive impact on the efficiency of an organization. And I think if there's anywhere we need to focus, it's on this mass of people that basically service our hospitals. They are a huge part of the role of the hospital performance. And if we're going to focus anywhere, it's on the data that they are constantly capturing about their patients.

MICHAEL 26:30 That's so interesting. And I think it fits in very, very well with some of the trends that we're seeing elsewhere in wearable technology. I know for instance that a handful of our team members wear different devices to track their heart rate, their sleep, their readiness, and things like that. And if you are very well-prepared for the day, you would expect to get more accomplished, do a better job, have higher quality. I can only imagine that that would pour it over in a much more impactful way to healthcare professionals. So if someone is working many back-to-back shifts or overnight shifts and they're tired, you would expect to see some sort of performance deterioration, I imagine. It would be amazing if we could actually make sure that we're tracking the healthcare professionals in addition to the patients to make sure that we're putting difficult procedures at times when people are ready and alert and prepared to tackle those. Have you seen things like that as well in the research you've been doing?

JULIE 27:29 So it's funny because a few years ago, we were talking, Chris and I, actually, about the-- we likened the journey of a patient through theaters to potentially an aircraft flight. And do you know when you're a pilot who is about to fly an aircraft, your health and your-- your mental health, your physical health are absolutely a focus point before you take off on that runway. But we don't seem to give the same importance to the health, whether that be mental or physical, of the clinician who is about to cut into you, who is about to operate on you. The situation is just as important when we have a patient on the operating table, but we don't take it seriously. And I think there has to come a time when we implement that, that we care for our individuals who are caring for us because our lives are at risk at their hands. And we obviously can do so much better if we're dealing with people who are at the peak of their health, whether that be mentally or physically. And I think if the current situation with COVID has brought anything to the fore, it's a focus on how we can remotely monitor our patients, our staff, that there seems to be much more of an awareness now into how we perform and a focus on mental health as much as physical health, and we've got to care for the carers.

MICHAEL 29:17 That's extremely relevant. And actually, I'd love to spend a little bit more time talking about some of those changes that have happened super rapidly as COVID has spread across the world. What are some areas that you think should get a little bit more attention, or how do we handle some of the remote telemedicine components in order to provide good care while still keeping everyone very safe?

JULIE 29:40 So it's funny because I recently had a telephone conversation with my GP because they're not seeing patients who are not urgent - they don't have an urgent need right now - and I found I got a much better consultation with him. He appeared to be more interested, almost more scripted. It felt as if he had to follow a more complete set of questions to make sure that he had covered all angles. I think we've got a long way to go from a public acceptance of this becoming the norm, but from an efficiency point of view, we've managed to clear accident and emergency rooms. We've managed to clear GP waiting rooms because people are-- perhaps we've gone a little bit too far in the-- unfortunately, some people are not attending A&E when they should do, but people are now assessing whether it's appropriate, whether they are actually in need of a clinician assessment. So we are rationalizing ourselves and triaging our conditions more. I think the more that people have got access to IT, Zoom conversations, it's changed the world. So definitely some good has come out of this very poor situation. And I think that that's been the same in all-- not just in healthcare, but many people's work situation has changed. Obviously, in our team, as you know, we're all based from home and are quite used to these conversations over conference call. But actually, it focuses attention. So I think from a healthcare point of view, the more we can do remotely, the more efficient the system will become, and hopefully, more appropriate consultations are going to take place in the future.

MICHAEL 31:47 I'd agree with that as well. And one other piece of information or I guess a large swath of information that I could see playing a role in that transformation would be the interpretation of someone's personal body signs that are available through many of the wearable techs that we have today. Because I know when I go to a doctor now, it's very different than probably 50 years ago where if I went to a doctor back then, I would differ to them entirely on what's going on in my body because I really have no insight or access to that. I wouldn't be able to test myself. I'm not able to see any of my vitals. But many people today are able to tell you what their regular resting heart rate is, what their regular high temperature is in days when they're sleeping well or sleeping poorly. They have already so much information about themselves that they can help to provide. And I think that can be, in many cases, both a blessing and a curse because we can often think as patients that we know what's going on or we know more perhaps than a medical professional, but that is very, very rarely the case, I found. I was just curious how you've seen this change over time and how you may see it change in the future when a patient comes in and says, "Oh well, I know what's going on here. I have this data, and you need to use it." How do we bring the data from the customer or the patient back into the full ecosystem of the system that they're getting services in, and how would you interpret that?

JULIE 33:13 Yeah. It's a strange situation because we have evolved so quickly from leaving that place where you went to sit in the doctor's office or the GP surgery, as we would say, and the pinstripe-suited man sat on the opposite side of the desk, and you kind of were beholden to what he said and what he found. Whereas now, you're almost-- I think in many cases, you're going presenting your symptoms. So you're almost kind of second-guessing potential diagnoses. And I think it's putting a triage in place, but it's also the education that we now have and the ability to monitor as you point out. I know you wear your ring. Many of our teams have got that ring to tell us what's going on. It's helping us to be healthy. And those negative points that you're collecting about yourself, hopefully, are going to help us with preventative medicine in the future. Because what we can start to do is predict areas of focus for you as an individual that hopefully will help to keep you healthier but will prevent the hospital admissions that we're all trying to avoid for the efficiency of the system and the health of the nation.

MICHAEL 34:42 Yeah. And I think you just touched on a topic that we'll probably have to discuss a different day. But the idea of personalizing and tailoring medicine to an individual is something that only now are we starting to capture the volume of data required to be able to do that. Because, while broadly the same, we are all a little bit different, and it's important that we get the services that are best for us as individuals. So I think that's a very interesting point as well. Well, Julie, I want to be mindful of time. I was just wondering if you wanted to let everyone know where they can find you in case they have any follow-up questions or other things that they want to talk to you about.

JULIE 35:21 Yeah. Sure. I mean, I'm an oldie, sir, but you'll find me on LinkedIn usually or via BRG, and I am really passionate about the use of data. So please, if you want to reach out and happy to discuss anything related to helping to improve healthcare through data. I think if there's one point that I want to leave this with is please don't focus on those high-level indicators. Let's understand how we can start to visualize the patients and their journey and how that actually links those KPIs. What we want to do is start to understand the variation and how that variation, when we view it, cannot color to the overall picture. And I think really paint that picture for the organization of what that KPI actually means to their patients and the system, and therefore, how they can improve it. Focus on the lower level. Engage those people at the lower level who are delivering care with the data. Paint the picture as they understand it, and hopefully, it will be a win-win for all.

MICHAEL 36:47 Well, thank you so much, Julie. This has been so enjoyable. I've learned so much.

JULIE 36:51 Thank you.

MICHAEL 36:52 And I really am looking forward to the next time we chat. So have a great--

JULIE 36:57 I'll get off from my soapbox. [laughter] Thank you, Jelen.

MICHAEL 36:59 Absolutely not. Thank you so much. Have a great rest of your day.

JULIE 37:01 Enjoy.

MICHAEL 37:02 And I'll speak to you soon.

JULIE 37:02 And you.

MICHAEL 37:03 Thank you.

JULIE 37:03 Take care. Bye.

MICHAEL 37:04 Bye-bye. The views and opinions expressed in this podcast are those of the participants and do not necessarily reflect the opinions, position, or policy of Berkeley Research Group or its other employees and affiliates.

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