In this episode, we had the pleasure of speaking with Dr. Irene Dankwa-Mullan, Chief Health Equity Officer, Deputy Chief Health Officer, Merative (formerly known as IBM Watson Health).

Dr. Dankwa-Mullan shares her background and expertise in health data analytics and the importance of health equity. Learn more at: https://www.ibm.com/watson-health

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April Moreno 0:04

Welcome to the public health network or the official podcast of the public health Podcast Network. I'm your host Dr. April Marino. Join us as we speak to public health professionals around the country and around the world, in Global Community and Environmental Health Topics. Join us also as we speak to podcasters in this field of public health. To learn more about us visit public health podcasters.com. And in the meantime, enjoy the episode. I'm your host, Dr. April Moreno and today we're speaking with Dr. Irene Dunkwa. Mullen. She is a nationally recognized industry physician and scientist, health equity thought leader, scholar and author with over 20 years of diverse local, regional, national and global leadership experience in primary care, health care systems, businesses and the community. She is the Chief Health Equity officer and Deputy Chief Health Officer at IBM Watson Health Cloud and cognitive software at IBM Corporation. She's also a member of the IBM industry Academy, a selected community of preeminent leaders to drive innovation and engage in cutting edge work for the industry. Welcome, Dr. Dunkwa. Well, then,

Irene Dankwa-Mullan 1:19

thank you so much. Thank you for having me here. It's I'm delighted. It's a pleasure to be with you.

April Moreno 1:26

Thank you so much. So, you know, we've talked a little bit right now about as we introduced you a little bit about your role at IBM. Could you tell us maybe a little bit more about the work that you do? It sounds really exciting and health equity, health equity?

Dr. Dankwa-Mullan 1:41

Absolutely, I am happy to do that. And I'm often asked about my role, my journey from being a physician, public health professional in and how I got into the technology industry doing what I do, because I did not start out. Knowing that I'll end up at this intersection of health care, public health, AI and machine learning technology. So I'm trained in internal medicine. And as you don't get the public health degree as part of the five year MD MPH program. My journey to technology and currently as Chief Health Equity officer was, after spending eight years in public health prevention, working in communities helping to strengthen the safety net services for primary care and then another eight years at the NIH, where I was at the Institute on Minority Health and Health Disparities, working on translational research and population health and health disparities. So I joined IBM Watson Health about six years ago as part of a team to promote science evidence, clinical subject matter, expertise in with the AI machine learning to some technologies that we were building. So in my role, I provide leadership for the scientific evidence value of our tools and services and products. As well as that strategic leadership for health equity, right, making sure that we have inclusive technologies. We have ethical, AI and machine learning. We've promoted in working with the healthcare industry, because we work across the health care ecosystem with stakeholders to learn different ecosystems, leveraging our technologies, helping them to drive improvements in health, not only cost and quality, but also most important innovation and an equitable outcomes. So, you know, we, as such, health, IBM tells Watson Health Solutions portfolio is diverse. And in terms of health equity, our efforts include partnering and engaging with communities to build health solutions, equitable technology, solutions, technology for social good. So like we work with, I work with large employers who are also purchase of practices of healthcare for the employees. We work with health plans to help them with that, you know, we have data we've been a trusted partner for various types of claims, data and EHR, built a data architecture, with the aggregated data sets until we're able to work with them to conduct a range of research evaluation studies, as well as helping them to monitor their efforts or goals towards health equity. And, you know, something, we work with them on dashboards, health equity dashboards to help them really, you know, see, follow or track their progress in how they do with their health care interventions. So I would say, you know, my role is varied, I think about health equity, a lot to think about how we can help Harkness clients, you know, collaboratively address challenges, healthcare problems, and health care issues. But I also spent some of my time on, on sites, how we can lead with science, how we can promote technologies with scientific evidence based studies. And I spend another quarter over my time on different efforts, helping to serve on committees collaborative. And also, you know, working on promoting AI, algorithmic fairness and attention, trustworthy AI, really needed to build and cultivate trust with communities, including the public.

April Moreno 6:27

Thank you. And I think that's another conversation I would love to have with you in the future about AI and health equity. So that's definitely something I'll reach out again, to do. But today we're talking about big data, kind of an introduction to, you know, kind of the work that, you know, IBM does, and Watson and kind of big data analytics today in this episode. And so what have been some of the most interesting features, in your opinion of big data analytics that you've seen in public health?

Dr. Dankwa-Mullan 7:02

That's a great question. When I think about it, big data and analytics, especially for those that are employed in public health, they range from, you know, big data analytics made from less complex, advanced analytics to such as sort of descriptive analytics that do not necessarily involve artificial intelligence, to more complex analytic methods that involved artificial intelligence, machine learning, deep learning methods. And all these analytics, so sort of one or two alive, these analytics require huge data sets from either retrospective, the pieces or perspective pieces to achieve a higher level of accuracy, and precision. And so some of the interesting features of big data, and how analytics are leveraged, right, include the various types of questions that can be answered with it. Because we know for example, AI and machine learning, these computational modeling approaches are an extension of traditional statistical methods that we learned in school. And the statistical modeling approaches, currently are able to provide much greater specificity. So for example, we can, it can provide like, like not only a change in x is associated with a change in y. But given the magnitude of that change, and the timing of that chain, so it allows for a greater understanding of the complex and dynamic systems that influence health outcomes. So for descriptive analytics, for example, it can inform questions around what happened to similar patients, what are the trends? And in similarity analytics, it can inform questions around how it happened to, you know, how a certain feature or disease progression happened, so that you can identify some promising intervention. So you could predict earlier, but we also have predictive analytics that informs questions about what will happen, how should it happen? And so it's, those are some of the interesting features and we know that you can improve the accuracy and insights and results of these big data analytics, especially if you have company data, especially if you have comprehensive data that includes social factors that include patient generated or, you know, generated data that we that we all know, influence happens that health outcomes outside of clinical care. There are lots of examples that have some of these interests in the big data analytics that have been formed public health, right, and especially during the height of the epidemic, the COVID 19 pandemic, how it informs lack targeting of vaccine distribution and informed various public health measures. One of, you know, an example that I could share was the, in the region, especially at the height of the debate, you know, we could create visual analytics platforms, with anonymous data from multiple sources, you know, the pandemic cases, the

mortality rates, the hospital rates, and we were able to with in collaboration with various groups public health, we're able to really create data driven dashboards, right to help the public understand their risk COVID, one dashboard that was created, for example, in the province of Halton, the South Africa, could look at, you know, the policy makers used it, you can identify current hotspots of infection, you could look at predictions for the spread of the virus, you could use it to look at risk factors that make separate communities more susceptible than others. And even going to the extent to look and understand questions, such as how many active cases are in my neighborhood? When is the predictive people, you know, of cases, and it was really informative that it helped public health officials to put in like, you know, a stronger system subbands. Outreach, really to help mitigate what was indeed a pandemic that we have never experienced before. It's really interested in that way. Oh, really informative, to help inform policies to help surface best practices, intervene, pandemics, for example.

Thank you. Yeah. So that's very relevant still, to this current moment. So there's so much potential. And there's so much need out there worldwide right now as it comes to big data analytics and predictive measures and active dashboards for public health during this time. So I have another question about kind of the implications. And I'm going to put this together in terms of public health and health Equity, what have been some of the Public Health and Health Equity implications of big data analytics and the needs? So for example, we have these tools, but how have they been serving public health communities? How have they been serving policy and diverse communities?

That's a great question. That they still, you know, there's some progress made, I would say and provide some of the implications. But we still have a lot to do, because I don't think the leveraging all the potential, the full potential of these big data in public health, and it's perhaps you know, more needs to be done and maybe more training and awareness but big data analytics, especially for addressing health disparities and for what it health equity, can help improve our understanding of disease prevalence in communities and counties and states. It can help provide new insights and trends into disease epidemiology. How maybe more specificity like I said, right and the magnitude, it can help researchers sort of also advanced in practice practitioners and the stair. The role of environmental and social determinants on disease offsets its progression, that development It can also help, you know, it used to help understand patient behaviors. And identify interventions that have been successful in different patient cohorts. So public manage population knows, we use it to facilitate, especially in clinical trials, so pragmatic trials, which may, you know, may be relevant for some public health interventions, prevention studies, and improve decision making as well as guide tailored interventions. And that's basically the, you know, I think one of the strongest assets, really guided tailored interventions in real time and practices to build diversity disparity populations that see.

Yeah, I agree with you, there seems to be well, there definitely is a need for continued application of big data analytics, especially during this time with the pandemic, for example, for predictive analytics, and also for in interventions, as you mentioned. So I'm really excited about the potential and the different things that we can do at this time. And so the next question I have for you as basically is, what can people, what do they need to know right now as it relates to big data, public health equity, and the future? What What can people kind of begin to focus on what in your opinion is important as we look ahead to maybe be more proactive for future pandemics? Or also for health equity, conversations and interventions? What can we look forward to in terms of big data?

Yeah, I think the, you know, what's important to know is that big data and analytics consistently generate insights from health disparities research, health equity, public health equity. But it also requires an understanding of the interrelated diversity, complexity of our data. And the fact that we are in a data rich environment, but we're not using the full data that's available to its potential. We got patient generated data, environmental data, mobile belt technologies, and the variety can provide us and leveraging differently, big data can provide us with a clearer and more richer understanding of the potential determinants so that we can we can really get to the end know, also, knowing about the data, the data volume increases the likelihood that these data will provide accurate insights. But one of the things that I really talk about it, I think it's important is data. Empathy is what I call it, referring to how much empathy how much patient values and preferences, there are reported outcomes, they're integrated into the care so you can have an AI data, chemo, advanced analytics, on big data surface insights, but we also need to have the data empathy to coach to around the context of the data, what's the experience of all the people, and that's a form of bias that the if we don't acknowledge that data, empathy, right, it would also really limit our ability to optimize our decision making proper process from from data so that, you know, I think knowing that yesterday's volley, the richness, we need variety, but data variety, data, velocity, all of it this, this fifth point, empathy, the centering the patient or centering populations.

I agree, and I heard you give this conversation at the Aimia conference last year. Maybe it was this year, I can't even remember. It's all not too long ago. And you talked about data empathy. And I remember that very clearly that it was you know, it was a great discussion isn't really important, especially as we think about, you know, health disparities and diverse communities, that component of it Data empathy is so important. Can you tell us a little bit more about how to apply it for health equity and diverse communities?

I think it's hard to have an understanding that different populations have different live experiences, it's important to understand that appreciate or acknowledge the lived experiences of marginalized communities or communities that have been experienced barriers to quality health care, and our own biases that we may have. And so not totally relying on AI data, because we know it's, you know, it's not just the AI decision, but it's that that it complements our it's basically to add to our thoughts. But we do also need to integrate the patient's values, the patient's preference, and understand it AI generated data, patient generated data bodies. So in a way, I think, even when we are working with AI, technologies for public health, I think it's important not to be not to generalize, because when you use a population that will if there's not enough variation, it does not work well. individual patient level population, public health and public health systems. You know, different populations have had variation. We were working to clean our you know, the science is always progressing, innovation is always advancing. And that's that's the goal to in order to really equity, thinking about all aspects,

right, yeah, and there are so many, there's so many needs in terms of health equity, and data. And if we're looking at matching patient data in public health with clinical data and research, there's going to be definite gaps in population representation, because of the fact that clinical data, clinical trials often faces a lack of diversity, right? They they have a lot of challenges in bringing in diverse participation and research, clinical trials, things like that, translating that information, or combining that information and triangulating that information into public health population level, may or may not, you know, it looks like a huge challenge right now. And I don't see how that would match, especially with the diversity of our communities,

making it more important to also train the pipeline and ensure that there is minority researches. Having access to big data science, training, it's really critical.

Definitely. Thank you. And so I guess, you know, one last question I had for you today was about your role in health equity. What makes this a role that has been of interest to you? Why are you passionate about this topic of big data and health equity?

Well, I think it's the right thing to do. I always say I'm passionate about it, but then I turn around and say, it's not only a passion, this is this is the way it should be, you know, be health disparities is because of, you know, it stems from systemic inequalities and inequities. And so, my passion is to see sort of a health care transformation where every person, every individual has the potential to achieve their maximum health. And given those resources and opportunities and the power and agency to achieve that full health potential because I've seen, you know, in my family and friends and communities,

You know, I'm optimistic. You know, there's so much awareness around inequities. And I think that there's a lot of goodness, right? People are wanting to see a fair and just society. And so my passion is around, because it's a moral imperative because it's ethically what we need to do. And it is a lot of community and stakeholders that are helping to drive this forward.

Thank you. Yes, we're here. We're all going to continue to do our part towards health equity. Thank you. So how can we connect with you how can we learn more about IBM and Health Equity at the company?

Yes, I will say to connect with me you could reach our Twitter or our den for Ira Twitter account or LinkedIn. The there's a lot of insider information, also on idea of acceptance help out websites. So and also, Twitter, I wax accounts will have something. Certainly, if you go to my LinkedIn website. Thank you.

Thank you so much for joining us today. Dr. Dr. Mullen.