The Healthcare Quality Podcast: Big Data in Healthcare

By:  Vivienne Kneale (@supposeIam) & David Kashmer (@DavidKashmer)


Listen to the podcast here.


Hi, and welcome to DDD, which is Data Driven Decision Radio, episode four, if you didn’t know. My name is Vivienne Neale and I’m delighted to be back with you. For those who have asked, my background is in education, training, broadcasting and social media. Of course, I am also a sometime patient, curious to know what decisions are being taken in my name that might just affect me and thousands of people just like me. So, once again I am joined by David Kashmer, Chair of Surgery at Signature Healthcare. David is an expert in statistical process control, including Lean and Six Sigma. He has a special interest in new tools to improve healthcare, like gamification. David also edits and writes for a blog called So, hi David and welcome back.


Vivienne, again it’s great to be here with you and all the listeners today.


Yes. We are all looking forward to seeing what you have for us, but before you start, I have been scrabbling about in the news and I’ve come across something I think you’ll find quite interesting. It was an article about a designer who has managed to create a 3D printed elbow and upper arm prosthetic. This actually has hand actuation and it was made for a boy without an elbow. I know that you have a real interest in 3D printing. So, what do you think about that?


Vivienne, this fascinating phenomenon has been seen throughout social media. I am connected via Twitter to many of the 3D printing hubs and we have seen interesting cases like this over the last 4-5 years along with the rise of 3D printing. The particular one you reference comes to us from and they run through a nice example of manufacture and design of a useful novel prosthetic for a child, just as you mentioned, and one that is able to grip and flex. It was about three to four years ago that I first saw an example of a 3D printed prosthetic for a child and this one really shows the nice advances that have come between now and then, but Vivienne, again what we are seeing is this rise of personalised medicine where a sharable stereolithographic, or STL file, can be posted on the web, transmitted easily across the internet and can be used to size, design, redesign and tweak a prosthetic limb for a child or an adult. It’s really a fascinating time that we’ve come to.


I agree. It’s like science fiction, but its science fact and its science now. Do you know very much about what Ninja Flex is?


Not the Ninja Flex product in particular, but my understanding from the article is that there are several types of implement that will relieve pressure from the plastic prosthetic against the skin. My understanding from a brief review of the article is that one of these products is a Ninja Flex inner liner. It allows a comfortable fit for the prosthetic while relieving pressure. If you’ve ever seen the show Dolphin Tale where there is a prosthetic limb manufactured for a dolphin. That tail requires pressure relief against the skin, and similarly, but distinct, for humans, we require some type of pressure relief to both fit the prosthetic properly and make sure we don’t have pressure sores develop at the sight of the limb interface. In this case, my understanding is that Ninja Flex is one of those products, but pardon me if I misspeak, I am not familiar with that particular one.


Well, it’s certainly exciting and I’m looking forward to a full body transplant. I want to go somewhere and have a look at all the bodies on offer on hangers and say, “yeah, it’s okay. I’ll have that one”.


I think there may have been several science fiction books written in a similar vein.


There has been, and there is a new film out as well. So, yeah, I am all for it. So, anyway, talking about things that are changing the way we might possibly live our lives, I have been exploring a little bit more about data this week. We both know that data points, absolutely billions of them, are being generated every day. I think, and you may be in this position, some healthcare providers may well feel totally overwhelmed by what’s being generated. You can actually end up wondering just what to do with big data. For some people, even knowing what big data is, is a hurdle. So, let’s start right at the very beginning. I mean, big data, as far as I can see, is at the heart of the smart revolution and the basic idea behind it is that everything we do is increasingly leaving a digital trace. I quite like the phrase ‘digital exhaust’ myself and this trace can be used by us or others to analyse what is happening and making decision making much more smart. The driving forces in this brave new world are access to ever increasing volumes of data and also our ever increasing technological capability to actually mine that data for maybe commercial or organisational insights. So, you can actually end up thinking, well, I’ve collected all of this stuff, what am I going to do with it? We actually know that data in itself can be difficult to manage, but that’s not an excuse not to do something about it. It’s impossible to ignore. So, what happens if you feel your department’s information isn’t being represented or you’re wondering where to go next? Do you have any experience of that kind of situation David?


Vivienne, I do. Not just in my professional life, but in every day of our lives. Let’s review. Most of the data that exists has been generated in the last ten or even five years. What I mean by that is, information scientists teach us that the data we’ve generated lately, relatively lately in human history, vastly outstrips all of the information that we had produced here before. What is interesting about that is we live in a very different world, some say that our minds were originally designed for. It was evolved to be hunters and gatherers eventually and now we live in a sea of information, which is very different and much more fast paced, some say, than what we are designed for. Now, to step back from that philosophically into more of, I guess, a concrete example, I’ll tell you that in our department of surgery one of the things we wrestle with is how to extract meaning from the data we have. As you said, big data implies the use of these massive data sets that exist to come to meaningful conclusions with the power to change our mind and make us act differently, even sometimes in counter intuitive ways. Two examples, one from a surgical department and one not. Let’s talk about the one that’s not first. About two or three years ago, a very famous pop news article came out about how Target was able to identify, the store Target, was able to identify women who were pregnant via their web browsing and associated web browser history before the women themselves knew they were pregnant. They would receive targeted advertisements and other data that was related to their pregnancy before they knew they were pregnant. It was just based upon their web search history and the big data that Target had used, extracted, culled and made a predictive model of that could tell us which customers were pregnant. Well, it was so good that again it would identify women before they knew themselves that they were pregnant and such is the value of big data. To come to these seemingly impossible, but very valid conclusions from enormous data sets. Now, let me tell you just in the hospital setting, we are now evolving away from the very consistent historically utilised process of process improvement that we always had. We had a quality improvement process that focused really on specific people. Us as surgeons, individuals, who went wrong, how did they go astray strategy. We are learning now with more complex data, statistically robust tools and the valid use of data this different lesson. The lesson of systems and how to evolve even predictive models for what we are about in our surgical department. For example, we put together a model based on our data over a long stretch of time that would predict when our hospital would have to divert patients, when we would be unable to render the care we really focus on for sick and injured patients and that process is called diversion. We built a model with a large data set that would tell us ahead of time when we would be going on diversion, and then we leveraged that to patch up our system so that we would know how to avoid going on diversion and how to take care of patients without having to turn anyone away. So, those are two example, Vivienne, of how we leverage big data both in healthcare and how I’ve seen it used in industry.


I can see that as being exactly the point in the UK with the National Health Service. We have some extraordinary periods of overload and I think that data will obviously help tremendously in predicting that actually almost down to the wire to make sure that the whole process works smoothly. Certainly, I think the health profession in general has been able to reveal new insights and opportunities and even excavate unknown or recurring problems. I think that will help both in efficiencies and also economic efficiencies as well, which is just as important. When you overspend, you don’t have the money to do what you have to do.


Vivienne, that’s just one of the ways, as you said, one of the important ways that big data can really serve us in healthcare. I think that there are several insights we can get from big data and then there are some insights that we can’t even imagine. what is so fascinating about the big data process is we can consider it more of a way in which we have become sophisticated enough with data to process big data along certain lines, large data sets, to draw from them meaningful conclusions and its more of a way to focus on what insights… rather how to get insights from data instead of the specific nature of what they are going to be, but just as you said, they fall in line with resource allocation, whether its diversion or some other aspect of quality fairly routinely.


Yeah. in fact, Bernard Marr, I don’t know if you are aware of his book, it’s called Big Data, discussed starting with a smart model and what he suggests is you start with strategy, you measure metrics and data, then you apply analytics, report the results and then you transform the business of caring. Is that how it works in your organization or are you doing something different?


No, it’s fascinating that although I have not used the smart model, many models point to the same pattern. For example, the Six Sigma model is called DMAIC, which is where you define what you are looking at, you measure it rigorously according to statistically valid sampling patterns, you analyse that data and then you improve, make changes. Then, an additional aspect that the smart model does not include, you control the project over time, meaning once you’ve made changes you have a plan to look in on it again to see how you’re doing. So, the DMAIC model incorporates some things the smart model does not and yet the smart model starts us off with, I think, a key element, which is strategy. The DMAIC model does not include really whether you should be doing a particular quality project, it just starts you off with definitions, making sure you have the correct or a reliable clear definition that can be used consistently. What I like about the smart model is the focus on strategy upfront.


Yeah. Well, it’s quite interesting. I like the controlling the project afterwards. So, we need a new one that combines both, I think, because it’s so easy, isn’t it, to actually have a huge exploration of something and then brush off the dust and say, “excellent. That’s done”, and it has to be ongoing.


I agree completely and I would just say that what’s done in Six Sigma, as far as the strategy portion goes, is not again the project pattern or the DMAIC pattern that you’ll use to improve a process to find, measure, analyse, improve, control. That’s once you get into a project, but Six Sigma practitioners, especially what are called master black belts, that’s where the strategy aspect comes in. There are other tools that we use outside the DMAIC project to say, “Hey, look. In our set of possible projects, which ones should we be doing? Which ones will have the most bang for the buck?” We use tools such as FMEA or failure mode effects analysis, FMEA, to decide which projects have the most bang for the buck, where we should go first, and that’s the strategy component that compliments the DMAIC pathway. So, it does get there, just in a different way.


I don’t want to put you on the spot, but I will [laughter]. Can you tell me what observations and insights have really given you the most bang for your buck in your experience? Is there something that really stands out for you?


There is. There are several projects, which across centres, give bang for the buck. What is useful about the FMEA tool is it steers you towards projects that maybe you wouldn’t have thought of, for example, it focuses on the ability to detect a problem. Meaning if you have a problem that is very challenging to figure out because you don’t have a good mode of surveillance or you don’t have a good way to catch it, that’s a more important problem. Also, the incidence of the problem, how often it happens, the severity of the problem. When it does happen, how bad is it? These and other factors are used by the FMEA tool to come up with a composite number to prioritise projects. Across centres, you see similar projects come up. Some are disaster planning at certain centres. What would happen if the building collapsed? Is that going to happen often? No, but it’s so severe and it’s so immediate that often a FMEA will find that. So, for surgery departments there are similar projects. That’s how it usually goes.


Well, certainly thinking about the profound explosion that happened in China that would have been an example where a disaster model would have to have been put into practice immediately.


Yeah, absolutely.


So, do you think therefore that what we are saying is data makes healthcare or practice more predictable, if that’s every possible?


Well, I do think it is possible, I would say. I think one of the typical replies to quality interventions is, “well, every patient is so different”. I think that’s true. I do think patient to patient there are opportunities to personalise medicine, and we should, and that’s coming both with the personalised dosages and 3D printed pills that we talked about last week, to genomic medicine. There are really so many ways to personalise healthcare. That said, we can consider each system, and not just individual patients passing through it, but each system of patients, each population of patients who come through the ED, the patients who… we can consider them as a population. There will be a bell curve, not always a normal distribution, but some curve associated with, for example, how long patients stay in the emergency department, and yes, from both personal experience and quality education we can do things to improve that populations, for example, time in the emergency department. We can decrease the variation, the width of the curve with certain interventions to improve our care. I think it can be done, I’ve done it, it’s what I focus on every day. It is very different than how we are typically educated in healthcare and that’s what makes big data valuable, but often more challenging to implement in different systems.


Of course, that does bring up some moral and philosophical questions, in terms of if you keep so much data on your patients or a group of patients, you could possibly ultimately deny treatment or you could say, “well look, look at the stats. Look at how you’ve looked after yourself. Do you really think that I want to use my time, my skill, my money, my department’s money in giving you another stint when you haven’t given up smoking or you’ve eaten too much cholesterol or what have you?” I mean, I know this is trivializing it in one sense, but there has got to be… I’m sure in the wrong hands we might end up giving data freely and it’s then used, not against us, but not to help us.


Vivienne, there are two large ideas that you reference in your comment. I think it’s important to talk about them. One is data security. You indirectly say we give up so much data, and we do. In the United States, we’ve had in the veteran’s association even one laptop being stolen that wasn’t secure, exposed the data of many, many patients. That is a challenge in the age of big data. How do we keep our data secure against cyber-attacks? How does my personal health information stay secure? There are lots of answers to that. I think it’s going to be a little beyond the scope of what we talk about today, but it’s no less important. I think its key. Then the second related thought you had as well, we may have all of these aggregated… all of this data, but the challenge is then what if it is used for a purpose that’s, let’s just say nefarious or maybe not what we would want it to be, or maybe to deny treatment to an individual? I would say, yeah, that is a real possibility. I have seen large data sets and ones that aren’t ‘big data’, just different studies used to say we should or should not treat populations. So, I think that risk exists whether we talk about big data or just staff who are driven to say there are certain things we should not treat. That’s a tough balance. I don’t look to big data to make individual treatment decisions and we teach, at least when I teach data and data use, I focus on the fact that there is a real difficulty in applying population level data, bigger data to an individual patient, to any one individual. It’s very tough to do for a lot of different reasons. You have to watch it. So, where there is a down side, like you said, where maybe somebody will use this not to treat, there are incredible upsides. Maybe we will use this to treat you better, which is my focus. Maybe we will use this to get you through the emergency department faster because we know that actually makes you do better in the hospital stay, for one example. I am not saying that’s true across hospitals. I am just using this as an example. ED time and length of stay at some places may correlate to outcome, maybe at some it doesn’t, but the issue I reference is I would say, and I’ve heard exactly what you bring up before, maybe they will use the data for the wrong thing. Yes, but I would say maybe they’ll use the data to treat me and my family much better than we could otherwise get. That opportunity, that side of things is what I tend to focus on, and I think your concern is well said.


Yeah. Well, I think it’s really interesting. We could take a whole podcast to talk about the pros and cons in a wider field and I’ve got so many ideas buzzing in my head. I will keep you right on the straight and narrow [laughter]. So, data does show important trends and what can improve quality of care, but that actually is often an individual experience and I suppose data becomes more valuable to you when it’s used to further customise and personalise a client’s experience. Certainly in the marketing context, customer experience is all. So, the question for me now, my last question is, how far does personalisation actually go without actually being unattractively intrusive in the medical profession, or should we ask, does that actually matter when more hospital procedures are intrusive anyway? What do you think?


I would say, for me personally, I believe in personalised medicine as a window on better healthcare. I think as we become more advanced there is a real opportunity, not just for satisfaction in my experience of care when I’m a patient, but also to get a better outcome because we are using medications that will work for me, a prosthetic that will fit for me, or whatever my needs are. So, I think it’s very useful, but I would also say, while we manage things down that road it’s important to manage and have the ability to interpret and gather larger data sets that say, “this is our performance for the group of patients in the emergency department as a whole, or referrals to our outpatients surgery practice as a whole”. When we manage things that way also and use our data set and slice it up in different ways to get… those two complimentary paths give a much better experience. We can ask ourselves, okay, how well are we doing when it comes to getting a patient into our practice and using the tools, for example, of personalised medicine for that patient. You can use big data to ask questions like that. So, I really think there are two different ways at getting at excellent care, neither one of which is alone enough. Those are my thoughts on it.


Thank you very much. Well, certainly you’ve given us much to think about this week and thank you very much David. So, I hope you’ve enjoyed todays episode, and if you want to keep up to date with David Kashmer’s approach to quality and statistical process control, business model innovation and critical practice, do join us for the next programme. In fact, we are very interested to hear what innovative practices are being undertaken in your health provision. If you would like to appear on the show, contact us through our website. We are looking forward to hearing from you. Meanwhile, if you have enjoyed the show, do leave us a rating on iTunes. It is one way we can ensure the word is spread. We look forward to being with you next time. So, bye for now.