84 — Rapid Response: White Swan's Data Driven Mission for Better Patient Care with Miranda Mapleton

Lenny: 00:00:00

This episode is brought to you by our friends at Dig Insights. Using decision science, Dig Insights helps researchers at the world’s most well-loved brands drive growth in crowded categories. Their work is supported by proprietary technology, including Upsiide, the only ResTech platform exclusively built to test and optimize innovation. Learn more at diginsights.com.

Karen: 00:00:36

Hello, everybody. Welcome to another episode of the Greenbook Podcast. I’m Karen Lynch, happy to be hosting today and happy that you are all choosing to spend some time with us. Thank you for that. It’s a pleasure to know that our listeners are listening and taking in the stories that we’re sharing with them. Today, we do have a spoiler alert. We do have a story to share. I’m really excited about this episode, actually, for some personal reasons which I’ll talk about momentarily with our guest. First, let me tell you who we’re talking to today. Today, we’re talking to Miranda Mapleton. If you don’t know Miranda, she’s the chief executive officer of White Swan, and we’ll get into a bit about what White Swan is all about. Know that she joins us currently as a founder of a charity organization but also with a vast background in marketing positions at large companies like PepsiCo and Mars and grown her data skills in those places and her marketing skills. Now she’s applying that expertise, the decades’ worth of expertise into a non-profit that is benefiting society through data, which is, really, very exciting. I’m going to let her fully introduce herself, but, first, I just want to say, Miranda, welcome to the Greenbook Podcast.

Miranda: 00:01:56

Thank you very much.

Karen: 00:01:58

I’m so glad you’re here. Why don’t I turn the mic over to you and let you share a little bit more about yourself with our audience so they can understand who you are before we start talking about White Swan and the great work you’re doing.

Miranda: 00:02:12

Sure. Thank you. I started my career in FMCG marketing, as you mentioned. I still have a love marketing. I started as a graduate in a biscuit company over here in the UK, and then I grew up through the ranks in marketing and became a director with PepsiCo running a big one billion pound [unintelligible], which was fun. Then I moved onto to Mars and to Petcare, so I changed categories. I have a love of animals. I have a Labrador and always have loved animals. Then, from there, I moved into eCom, and that’s really where I got more into the data world, to be honest, because I was working for a big business in the UK, which was [unintelligible]. I was running all of their marketing, and that led me to understand more about how data can be used for good decision-making. What I found after some time in that business is that I felt I wanted to do more good in the world. I had always had a healthy interest in health and wellness. A very good friend of mine, Steve King, had been founding a business called Black Swan Data, and he used his technology to diagnose this sister with a rare form of Parkinson’s. He wanted someone to use the technology of Black Swan for good, and that’s when he asked me if I’d found the charity. That’s my path to leading White Swan, and I have seven years since founding this and running the organization. Yeah. The rest is history, as they say.

Karen: 00:03:36

There are so many personal connections I’m making already, and I imagine, if I’m having those personal connections, our audience is too. First of all, we share a love of animals. My golden retriever just had to leave the room, because she gets all up in my business. Yeah. We share that affinity. I have also been quite interested in health and wellness. I’m pretty open about the fact that I’m a two-time cancer survivor myself. One of the reasons why I’m anxious for you to share the story of Julie King with our audience and how White Swan came to be is because, like many of us, we have people in our worlds that are dealing with illnesses. Some of them are hard to diagnose and hard to detect and, certainly, hard to treat. It becomes a complicated puzzle. I applaud the efforts that you and, certainly, and Steve and then White Swan continues doing to help people through data. Why don’t we take that step back and tell us a little more about the start?

Miranda: 00:04:38

Julie is a wonderful lady. She’s the sister to Steve King. Steve is the CO-founder of a company called Black Swan Data. Now, Steve is her big brother, and Julie had unfortunately been suffering for about eight years undiagnosed. She was very, very poorly. She had two children herself, and she had become wheelchair-bound and over that time just got worse and worse, and the doctor said to Steve, “You have to accept you’re probably going to lose your sister.” He couldn’t accept that prognosis. He used Black Swan’s technology to try and find out what was wrong with her. What that involved was searching millions of social conversations in public forums but capturing that data and using NLP techniques to understand the common themes and the sorts of conditions that were being raised disproportionately to the rarity of that disease. Super rare for being—coming up several times. In conjunction with that, this process took a year or so. She kept a diary of everything she did and how she felt, so they were looking for patterns. It’s all about pattern recognition and what made her feel better and worse. Steve posted a video of what Julie was like through the day and asked lots of forums what they thought and had thousands of responses. Then used the same NLP techniques with Black Swan to see what was coming up within those responses that they haven’t considered before. There were two conditions that came up through that which hadn’t been—she had not been tested for. One of them was Parkinson’s dystonia, which is what she’s got. They took those results to her consultant, and he agreed to check her for them. She was diagnosed very quickly then on the right location, and then her life was transformed. This was a very, very sick girl, and now she is competing triathlons. She’s actually started taking part in CrossFit European Championships now, and she’s amazing. It’s also enabled her to be a brilliant mum to her children. As a mum myself, I can only imagine what it’d feel like to not be able to part of your children’s lives in the way she wanted to be. That story inspired the people of Black Swan who worked for Steve at the time to want to found a charity, but, like everything, it’s really to do these things on the side of your day job, so he asked me if I would found an organization for him initially with just volunteers. I had volunteers joining me, and over time, from me and 20 volunteers, we’ve grown into an organization with 5 staff, 3 trustees, clinical advisory board, and 130 volunteers across the world. That’s happened over the last seven years. We’re registered as an independent charity, and we’re all about using technology and data for good. We’ve seen data have the power to save lives and transform lives in Julie’s case. As you mentioned, unfortunately, Julie’s not alone. There’s 500 million people across the world suffering long-term undiagnosed like she is, so we’re trying to help those people using our technology and analytics.

Karen: 00:07:41

I love that story so very much. There’s so many directions we can go in with this conversation. We can certainly talk to you about how to manage the growth of a non-profit, how to manage a non-profit let alone how to manage the growth of a non-profit, but we’re also interested in the data angle here, and I think that one of the—one of the aspects of this story that I found really interesting, and I don’t it’s been what you shared. It’s in what I—when I went onto your White Swan website, and I went to the navbar where it said, “Our Story,” there was this great, really well-done—by the way, for our audience, go check it out—graphic facilitation video explaining the story as well. The aspect of it—again, let me come back to the question at hand—is how was the information that was gleaned then shared with medical professionals who made their decision? Because I imagine many medical professionals could be cynical. They could be skeptical of, “Wait. What’s this work coming out of the insights industry that’s”—tell me a little bit about that process. How did you get convict people? How did Steve and Julie convict people that this data was actually going to inform their decision making?

Miranda: 00:08:54

That’s a good point, and I think we should never underestimate the power of our medical colleagues and their—with their training and expertise too. We very much look to work alongside clinicians and always do in our work, so we don’t do anything in isolation of clinicians. In Julie’s case, I think what Steve found is that they’d hit a brick wall with solutions as a clinical team. By showing them the depth of conversations that they looked at—and we’re talking about millions of conversations here—and very sophisticated NLP techniques and to keep coming back to these two conditions that kept coming up with the description of her symptoms, which were quite complex because Julie’d get worse through the day. She’d start up in the morning being able to do most activities, by lunch time would be in a wheelchair, and by the evening couldn’t even lift a knife and fork. That progression every single day was quite outstanding, really, in terms of—and in an awful way—how it would change. The clinicians themselves, I think, felt the—both—there was a lot of robust analysis that had gone into this, and you couldn’t deny these two that had came up. When she said [unintelligible] what? She went, “That’s a good point. Maybe it could be that,” and hence looked into it. A lot of doctors, the challenges to them is that there’s so many rare conditions out there. There’s thousands of rare conditions. It’s very hard to them to always be able to correctly identify which one am I immediately. I think they understood it was coming from a very good place, it had robust analysis behind it, and could see that it was a credible diagnosis. We’ve been testing our techniques to look at other people as we get approached by people who are looking to get themselves self-diagnosed. We no longer take on individual cases, because there’s too many of them with the build in our technology to help people like Julie who can help them scale. When you talk to those people, they have the same experience of going through a diagnosis process. They go down lots of paths. They get to lots of dead ends, and it can be very hard to express to a doctor all the things you’ve been facing over time, because you don’t necessarily know what is and isn’t relevant to them. One of the things that was important within Julie’s diagnosis, ultimately, was that she started the process with toe-curling. At the time, she had no idea that that was actually relevant until she got her final diagnosis. They said, “Did you ever experience toe-curling?” She said, “Well, I did.” “Did you tell your doctor?” “No, I didn’t.” “Why?” “Because I didn’t even realize it was a symptom at the time. I just thought I had a cramp.” I think what it allows us to do is look for all the different signals, I guess, within that great, big noise of data.

Karen: 00:11:31

Yeah. I love this so much. Another story I’ll share personally is that my father has Parkinson’s—the more traditional form of Parkinson’s—and he’s also had a pacemaker added. He’s in his 80s and has had—seen his share of doctors. Recently, very recently, he had a situation where he would become non-responsive, and the medical professionals at the emergency room and all that kept thinking he was having these mini strokes called TIAs, I think. Transient something attacks. I’m not a medical professional. Anyway, my family, we kept calling them episodes because, afterwards, there was never any sign that he had had a stroke. We kept calling them episodes. Somewhere, my father found within the few weeks an article that he had clipped out, so a paper article that talked about something about syncope, which is like fainting spells. Then they called their researcher daughter and say, “What can you find out?” kind of thing, so I start doing some research, and syncope is actually a side-effect of many Parkinson’s patients. It is exactly what his—he has been experiencing, because it’s also linked to low blood pressure or drops in blood pressure. I’m like, “Why am I the one and why is my family—why are we the ones trying to connect dots?” Because no medical professionals have been telling us that. He has been dealing with this for well over a year, but we have to be advocates for ourselves. When I heard the story of White Swan, I was thinking, “How wonderful that there is an organization.” Because, once I started doing the research, more and more results were coming into me. As you’re talking just now, I’m thinking it would be amazing if I could mine the internet for stories of other Parkinson’s patients who were having these fainting spells tied to their blood pressure, which drops because of their medication. Wouldn’t that help his neurologist prescribe the right medication? Anyway, I share that because I think it’s genius, and I’m really excited that you’re doing this work. To your point of not really working with individuals, who are you working with then? Who is the beneficiary of some of this data work that you are doing right now?

Miranda: 00:13:44

We know that social data is hugely underutilized currently within the health and wellness area, and that’s what we’re trying to work on. How do you use these giant data sets? They are huge. Billions and billions of social conversations around health every year, and it’s global, so it’s multi-language. That’s what we’re trying to utilize. We work currently with a broad variety of organizations in public sector and private sector and charitable sector. Essentially, the likes of British Heart Foundation, Great Ormond Street Hospital, Royal Marsden Hospital, all in UK. We’ve worked with some American organizations as well, [unintelligible] like [unintelligible] as an example. We work with a lot of NHS trusts as well. We work with those organizations, because they all want to utilize this data but don’t know how or don’t have the capabilities, technology, [unintelligible] techniques to make sense of data like that at scale. It’s unstructured. It’s messy. It’s noisy. You’ve got to be able to structure it and cleanse it and make sure you’ve got right breadth of data set in the first place, so you don’t miss things. Because, often, you’re looking for things that—you don’t always know what you’re looking for until you find it. Questions can change over time. Like traditional research techniques, it’s unbiased by design, because, instead of asking set questions, you’re going out and seeing what the conversation is around these broad areas and then narrowing it down. We’ve built, over the last five years, a technology platform called Million Minds. That allows us to structure this data and interrogate the data on any topic in the health area, and it’s category-agnostic, so we can look at symptoms or lifestyle factors or treatments, for example. All the different ways can look at it. For those types of organizations, [unintelligible] and therefore, answers a very broad range of questions. Some of these organizations are looking at how to improve patient experiences and patient care. Some of those organizations want to accelerate diagnosis, for example. How do we get patients faster from initial symptom to an accurate diagnosis? We help in that area. We also help improve things like clinical trial design. How does the voice of the patient represent his—is represented in that trial design to make sure the most important symptoms and lifestyle impacts of the condition are taken into account in [unintelligible] about then? We also help to prioritize research areas for organizations when they’ve got lots of different things they can look into. For other charities like Alzheimer’s, we might help to inform where they put their resources for helping dementia patients and their families. We can look at it in a huge variety of ways.

Karen: 00:16:26

Yeah, yeah. No. Again, it’s no doubt this is great work that you’re doing. When I think about the types of organizations you’re working with—and there’s a lot of foundations and trusts and societies and things of that nature—and what strikes me is that the work that you’re doing is very much like a research provider who is a specialist in this field. Right? That’s how you sound. You sound like you are a research specialist catering to this field, yet there’s this charity aspect. Tell me more about the charity aspect and how this works if one of these associations who—sometimes they have money in their budget for research. How does it work? What are you actually doing for them that would be different from a full-service supplier?

Miranda: 00:17:11

Everything we do as a charity has to be in line with our charitable mission. For that, that’s a—that is about the improvement of health using data and analytics to accelerate diagnosis and prove the effectiveness of treatment or prevent illness. We will only do work if it does that, because we’re guided by charitable objects. When we work with those organizations, we do it two ways. We either work in a paid capacity, so we still do that for far reduced funds than it would be a traditional commission organization, but they pay for that piece of work. It is a research project. We can do that. To answer key questions, we help those organizations form those questions. We can be very agile in approach, because we often find that people start with such, discover things, and then we pivot as we go through a project, or we do pro bono work. For other organizations, we use our volunteer set to do completely free of charge preventive projects. We typically do that for the charitable sector. We sometimes we do it in academia as well. Those projects come, sometimes, from passions of volunteers. For example, we did one in cardio in a hypertrophic cardiomyopathy. Someone who lost a relative in that area, and, so, we did a big project voluntarily in that area on to accelerate diagnosis of that condition. Other projects come to us because they are—they hear about what we can do. We haven’t yet found any organization globally that does what we do and certainly none that do it in a charitable capacity. If we make any surface funds, those all go into helping other patients. Because what we’re ultimately doing as we build our data sets and our Million Minds platform, is we want that to be patient-facing, ultimately. What that’ll mean is other people like Julie can access our data set and our capabilities but do it in a way of helping themselves and their—and their clinicians to get them to an answer.

Lenny: 00:19:02

We’re going to take a quick pause to highlight our podcast partner, Dig Insights. Have you listened to _Dig In_? It’s the podcast brought to you by Dig Insights, designed for brand professionals that crave innovation inspiration. Each week, Dig invites a business leader onto the podcast to spill the beans on the story behind some of the coolest innovations on the market. Search ‘Dig in’ wherever you get your podcasts.

Karen: 00:19:28

I love your work, and I think that there is something very powerful about a strong mission that guides an organization, whether that is in the non-profit sector or whether that is in the for-profit sector. I think, when you’re guided by a vision, that makes everything a little bit clearer. The next question I have, which is related to that, is actually about your volunteers then. You mentioned, I think, you have over 130 volunteers across the globe. I’m curious. Okay. Tell me about those volunteers. I know that, again, back to your website, which I’ve spent some time on, there’s an area that says you can—you can be involved. You can be a part in the organization. Obviously, there’s some funds that you can collect. Tell me about the volunteering. I find that interesting. That’s a lot of people.

Miranda: 00:20:11

It is a lot of people. Actually, to begin with, as I said, it was me and all the volunteers. I got very good at volunteer management, which is a new skill for me, because I come from a business world. One of the things I recognized early in my leadership was the how to inspire other people to work with me and follow my vision without getting paid for it. What that was about was them buying into a mission and feeling the same way about helping these people who are living long-term undiagnosed to find an answer. It’s awful. If you don’t know what’s wrong with you, and you don’t know what to do about it or how to prevent it, you really feel in a terrible place. Everybody that’s joined us as a volunteer has bought into that mission of helping those people. The other thing I learned within this is that they need to feel hugely valued in what they did. Anything they did with us we’re very grateful for, and their skills were valued. What we found over the course of that is that the skills that first came to us were very much data, science, and technology-led. Then, over time, we attracted volunteers who brought very different skills. We’ve got doctors and medics who volunteer for us and will help with the data construction sometimes. Because every time we print a new taxonomy—a [unintelligible] taxonomy for a new disease type, we clinically check that. We’re very close to how to use a combination of patient data and complexities. We also found that we brought in creative people, so we’ve had volunteers doing our website for us, for example, or others that might help with finding new prospects for us to do work with. There’s also [unintelligible] funds to keep the charity going. All of the volunteers bring different skill sets, and all of them are hugely valued. We also recognize that people can give up different amounts of time at different times in their life. It ebbs and flows. Recognizing that and being flexible to that. The only thing I ask of people is if you commit to do something, please do it, because we’re a very professional organization. When we commit to things, we always deliver them. They seem to have very good—we’ve had several volunteers with us since the very start, so seven years, and others have [obviously] joined us on our journey.

Karen: 00:22:16

Yeah. It’s great. Again, there’s another area that we could talk about for a really long time, probably, which is volunteer management. I know that there’s a lot of associations in the research industry that are volunteer-driven and volunteer-led, and it’s always on people’s minds how to keep a volunteer motivated. If you had a very short statement—yes, appreciation, because I think a lot of people intuit that you have to appreciate your volunteers and what they’re giving. Is there any other way that you help them—help them to help you?

Miranda: 00:22:49

I think clarity of requirements and what you need from them is very important so that they can decide if they commit to it or not, and they don’t feel their time is wasted. Yeah. Feeling valued, as I mentioned, and then buying into our mission and seeing us delivering against it. They feel their work is purposeful. They see the delivery of it. They understand what it’s being used for. It’s very visible.

Karen: 00:23:14

Yeah. That’s great. It’s great, and I think that it is human nature to hit a certain point in your life if you weren’t necessarily raised that way or if you didn’t start at a young age doing that, you certainly hit a point where you want—you want your work to have meaning. I can imagine that this is a great solution for people who are in either the insights data, science space, medical space. It seems to be lots of people can buy into the mission of let’s impact people and really change lives for the better. Really, kudos to you on everything that you’re doing. Let’s talk about further out. You have seven years behind your belt. As the guiding light for the organization, I’m sure you have a vision for the future. How are you going to keep on keeping on? Do you have a five-year plan from this point forward? Are you still in the works there to create one? What’s your vision looking forward?

Miranda: 00:24:10

We continue to be guided by this desire to—we believe no one should suffer unnecessarily, not knowing what’s wrong with them, what to do about how to prevent it. That’s guiding us all the way through. We know to ultimately deliver against that, Million Minds needs to be patient-facing. It needs to be something that’s available worldwide for everyone to use in a self-serve way so that they can take that information to their clinician, like Julie did, but an automated version of what Julie did to help them on that journey. That some way off yet, because it’s—we’ve got a fantastic platform that we’re developing, that we’re using every day for all of our work. To be patient-facing, we need to have a—develop more data sets against it, so we keep adding disease ontologies to it. We need to make sure that we’ve got the right funding to allow for that long-term plan. We need to work out, over the course of that, how that fits with other things they’re using in their daily life. Because, obviously, throughout all of this journey, technology is changing the healthcare space, and people are trying to offer different solutions. LLMs is another example of that. I’d say we’re always on a journey towards that ultimate goal. We have had some [incredibly] encouraging early signs as to how good we are at trying to map these mass data sets and come up with a credible list of things that could be wrong, but we don’t want to be used isolation. Part of that journey involves involving clinicians in that journey so they feel that it’s a credible solution. We did a win a place a few years ago on [unintelligible] accelerator in—with the NHS in London, and that helped us to understand more of those challenges that had what things we need to do to make that a reality. We also find as we go along this journey there are other opportunities to help big communities that make a difference in this space. For example, we know that academics struggle to manage this data and access this data, and yet there are millions of academics across the world spending huge amount of research funds and aren’t using social data as well as they could because they don’t—they don’t know how to manage it without the technology to manage it. Right now, for example, one of the interim steps [tools] that ultimate vision we’re looking at is could we build a platform for academics to use to access our data set and, in doing so, hugely improve the efficiency of their works, make it much more actable access? A data set which could be robustly looked at time and again if they have a research [unintelligible] and want to go back to years later and see if it get the same results from social. Because social’s a great predictor of trends as well. What’s going to be the next thing that we need to consider? We are seeing some of these steps along the way as we identify opportunities to utilize our skills for good.

Karen: 00:27:01

Yeah. That’s great. One of the things that comes along with the work that you’re doing are some of these issues. You talk about data, and you can’t help but go into data quality. Right? We know that there must be information out there that is misinformation. This is a series of questions that I’d love for you to expand upon. How are you dealing with the fact that you don’t want poor data or bad data or inaccurate data to factor in? That’s one bucket—right—the data quality issue. Then there’s also the privacy issues, especially when you come to healthcare. You mentioned how people are tracking their own health data. In my mind, I know wearables are a thing and apps for tracking health. Individual data, there’s privacy concerns. How are you venturing into those two big buckets of data quality and data privacy and facing those challenges that the insights industry faces but is tightened or heightened in the healthcare sector?

Miranda: 00:27:59

Sure. Maybe I’ll take privacy first, and we’ll talk about quality second, because they interrelate. From a privacy perspective, all of the data we deal with is being shared already in a public forum, so this is publicly shared data because it’s social data. It’s data from blogs, forums, Twitter, et cetera. It is very different from the type of data like a medical record, which is shared in a private context with your consultant. That’s one point I’ll make. First, it would be publicly shared. Obviously, you’ve given permission within your Ts and Cs to share to—for that to be shared. Also, everything we work with is anonymized, so we don’t work with any PPI data. We only work with anonymized data, because, actually, for us, the benefit is in scale and volume, not in looking at individual bits of information. That really comes back to data quality piece, which is when you deal with big volumes. Within that, it helps us to strip out trends and look for anomalies in that, so you have less things that will take you off to the—a different space. We’ll also look at, for example, whether there’s spikes and what’s led to that spike in conversation types. Is it all coming from one source? Is it all coming from one handle, for example, that’s on Twitter? You can take out some of those anomalies in the data. We do a lot with cleansing and looking at relevancy using our data modeling, so relevancy is—is it something that truly is relevant or if you picked up a term that it is not, and it’s being used in a completely different context. That’s where the NLP comes in as well. We’ve spent a lot of time building our taxonomy in getting that checked in different clinical areas as well. What we find is that the terminology used by patients can be very different to what’s used by doctors. That’s where it’s been really important to make sure we’re led by the patient voice but we have a check to make sure that something is a relevant thing to be looking at, not just causal.

Karen: 00:30:03

Great explanations. Thank you very much. Also, one of the things that I love about that—and when I say, “That was a great explanation,” it’s very logical and easy for me to follow. I’m not a data scientist.

Miranda: 00:30:13

Neither am I, by the way. I think one of the benefits of running an organization is something you didn’t originally start as a data scientist, as it were, in this area. I understand that there’s lots of people that need to understand what we do who don’t come from that technical background, because we’ve talked to and have a very broad range of partners and stakeholders and volunteers.

Karen: 00:30:37

Yeah, yeah, yeah. No. It’s fabulous work you’re doing. The way you’re doing it inspires a lot of trust, because you’re also doing due diligence. I imagine that is largely informed by some of your roots. Is Black Swan still involved in the work that you’re doing? Are you really self-operating at this point?

Miranda: 00:30:57

We are an independent organization. We are very grateful for them kindly donating the use of their technology to us. We’re run completely independently, but they do donate that technology usage. That means that we can benefit from their data sources, their data processing power, without costs and overhead that would go with that. The investment they’ve made in their technology we obviously hugely benefit from all of the work we do, and we do independently.

Karen: 00:31:24

Yeah. Speaking of benefiting, I don’t want to get off this particular recording until we’ve talked about some of—some other stories. You have on your site some wonderful case studies. I think that I could spend hours reading about some of the impact of your work. Are any of those case studies really poignant to you? Does anything stand out to you as—whether it’s there or whether it’s in your data bank—as the founder? Are any other case studies that you are particularly proud of and excited about?

Miranda: 00:31:54

One of the early ones I could particularly love is a project we did in hypertrophic cardiomyopathy. This is a really rare cardiac condition. It often strikes young people. It can be fatal. Most people do not know they have it until they have a heart attack and, unfortunately, lose their life. It’s tragic for anyone to lose a life. It’s very tragic when it’s a young person with a family. We did a piece of work to accelerate diagnosis in this area. One of the things we found, which was a completely new insight for them, is that the people who we should look to educate about this condition was schools’ teachers, because they were often the ones that saw the earliest signs of this condition in young people before necessarily their parents or their doctors. Because they saw them relative to other children and how they reacted under any type of sporting condition. That was a new insight, which was then going to lead to greater education in that group to spot these symptoms and signs. I still am really proud of that, because I feel like it was something that’d not been sourced—that we haven’t looked at social data for that. Another one we looked at, which I still love the organization, the Royal Marsden Hospital in London. It’s one of our world-famous cancer hospitals. They have an amazing team there and incredibly dedicated to cancer care and improving the outcomes of cancer patients. [Unintelligible] asked to look at how they can improve their care further. They did that piece of work with us, and one of the things we found was that the understanding of access to clinical trials and new types of—and treatments that they could get on was [a key area] they could improve on [was a result of] that [they’ve] improved that education around that area to improve access. We’ve done lots of different projects over time. To be honest, I could probably find many more examples. Those are two of my personal favorites. We’ve done some brilliant work with the Alzheimer’s Society looking at dementia care and where people reach a tipping for needing help. That helps inform their priorities for the care that—and the new support they give other dementia sufferers and their families. Even the work that we’ve done in clinical trial design, I think, is great. Because, ultimately, there’s a lot of pharmaceuticals that are looking to try and improve outcomes for patients. One thing they really need is great insight at the start of that as to what matters for patients. What’s the impact of having a condition and, therefore, what should the new treatment give them? We’ve done some great work for there in that area, which I feel really proud of, actually, because I feel it’s made a significant difference in the outcomes of the patients.

Karen: 00:34:34

It’s right on mission for you. It’s right on mission for you. I think that’s great. I think that—with my insights hat back on again—I think that is what we are looking for. We are looking for in all of the work that we do as insights professionals, that one spark, that one thing that we have discovered that is a truth that we can act upon to make a difference, and that is really what the insights field is all about. You are working to deliver those insights to people that can give the level of care that we need to have a healthier society. I love your work, Miranda. I think it’s fantastic. I want to get to some specifics here about what can people do. How do they get involved? How can they support you further in some of this work that you’re doing?

Miranda: 00:35:23

If anyone would like to volunteer for us, they should contact us at hello@whiteswan.org.uk. If you’d like to work with us and partner with us, we would love to hear from you. Same contact: hello@whiteswan.org.uk. We’d love to have some more organizations to work with [unintelligible]. We’re always looking for new partners that we can do some of those projects with. We’ve got lots of use cases right across the spectrum from new therapies, discovery, to research priorities, to clinical trial design, improving care, accelerating diagnosing. We can work across any health area across the whole spectrum. If you’ve got a problem or something you’d like to know about from a patient insight perspective, please give us a chat, because we’d love to work with you.

Karen: 00:36:07

Sounds great. I know that we are going to be bringing you to the stage at our health event in the spring, so thank you. Spoiler alert: we’ll be able to share some of the great work that you’re doing in that forum. If you haven’t yet signed up for IIEX Health, which is now going to be a virtual event in February, please do so. We can include that link in the show links as well. What else is on the horizon for you in 2024, which is really right around the corner?

Miranda: 00:36:35

We’ve got quite a pipeline of different areas we’re working on project-wise that are going to take us through, I think, the next few months. This one I mentioned earlier about academics’ access to this type of data, we’re very busy exploring how we could fund—find funding to make that happen, because we see a very strong need amongst the research community for better access to this data. Then, forward from that, we continue to build out and mainly how we can use LLMs in our work to improve efficiency without diluting the accuracy of the work we do now. It’s one thing we do find at LLMs is they can sometimes make up stories.

Karen: 00:37:11

We’ve heard. We’ve heard a thing or two about that.

Miranda: 00:37:13

Yeah. We are looking at them as a valuable source of time saving on some of our analysis work, so we continue to look at how we can do what we do better for our partners.

Karen: 00:37:24

Yeah. Hopefully you have some AI experts in the mix volunteering to help you as well, I’m imagining.

Miranda: 00:37:30

We do, yes. We certainly do. We certainly do. We’re very lucky. We’ve got an excellent mix of volunteers and advisors.

Karen: 00:37:36

That’s so fantastic. Miranda, is there anything that I didn’t ask you that you wish I had gotten to during the course of this discussion?

Miranda: 00:37:45

I don’t think so. I think we’ve covered a lot. Thank you so much for having me on. It’s been a real pleasure.

Karen: 00:37:50

Oh, it is my pleasure as well to have hosted you. Thank you for joining us today. For everybody listening, once again I thank you for tuning in. It’s great to know that you are there and that we are sharing some content with you that hopefully can make a difference in your life as an insights professional or marketer or human being, for that matter. I want to shout out to our producer, Natalie Pusch. Thank you so much, Natalie, for everything that you do to make us a success. To our editor, who we’ve been calling Jamie and James but I also want to start shouting out that he is with Big Bad Audio. Thank you so much for the work that you do. To our episode sponsor, Dig Insights, thank you for making this episode possible. That’s all we have for today, friends. We will see you next time on the Greenbook Podcast. Bye-bye.