WEBVTT - Smart Talks With IBM: Addressing health disparities during COVID-19 0:00:00.680 --> 0:00:05.279 This is a very real problem that directly affects millions 0:00:05.320 --> 0:00:08.640 of people around the world. If we expand that to 0:00:08.720 --> 0:00:12.479 look at the indirect effects, such as how disparity creates 0:00:12.600 --> 0:00:17.280 enormous stress on communities around the world, we're talking about 0:00:17.480 --> 0:00:21.400 billions of people who are impacted by this issue. And 0:00:21.480 --> 0:00:25.840 for me at least, this was a really big problem 0:00:26.040 --> 0:00:30.800 where using technology wasn't an apparent path. If you were 0:00:30.840 --> 0:00:34.240 to ask me how technology could help address an issue 0:00:34.440 --> 0:00:37.640 like the disparity among different populations when it comes to 0:00:37.720 --> 0:00:41.320 health care access, I would be at a loss. But 0:00:41.400 --> 0:00:45.240 as it turns out, technology can play an important role 0:00:45.479 --> 0:00:49.720 in that effort. As we'll learn, tech is really one 0:00:49.720 --> 0:00:54.320 piece of it. Real solutions to eliminating disparity will require 0:00:54.480 --> 0:00:58.720 much more than technology. I spoke with Dr q Ree, 0:00:59.160 --> 0:01:02.400 Chief Health off Sir at IBM and Dr Irene don 0:01:02.480 --> 0:01:06.880 Qua Mullen, Deputy Chief Health Officer IBM Watson Health about 0:01:06.920 --> 0:01:10.800 this issue. Both doctors have dedicated an enormous amount of 0:01:10.840 --> 0:01:14.840 time and effort as positions and data experts to create 0:01:14.840 --> 0:01:18.960 a more equitable access to health services across all communities. 0:01:19.440 --> 0:01:22.440 They helped me get a deeper understanding of the challenges 0:01:22.520 --> 0:01:26.640 we face, how they impact millions of people, and the 0:01:26.680 --> 0:01:31.280 way technology plays a part in addressing the problem. Thank 0:01:31.280 --> 0:01:33.600 you both for joining me today. We have a lot 0:01:33.640 --> 0:01:36.800 of ground to cover and a big topic to talk about, 0:01:36.840 --> 0:01:39.640 but before we really dive into that, I wanted to 0:01:39.720 --> 0:01:42.720 hear a little bit about your personal story, about your 0:01:42.840 --> 0:01:46.319 journey and how you got to where you are today 0:01:46.400 --> 0:01:52.400 and your personal reflections upon the really big challenge of 0:01:52.640 --> 0:01:56.640 disparities and access to health and health care services. Dr Rey, 0:01:57.000 --> 0:01:59.600 would you care to share a little bit of your 0:01:59.640 --> 0:02:03.840 background owned and your personal experience. Sure, now, I appreciate that. 0:02:04.080 --> 0:02:08.120 So I currently serve as the chief Health Officer for 0:02:08.200 --> 0:02:13.960 IBM and Watson Health. I'm a physician by training and 0:02:14.200 --> 0:02:19.200 also UM have some background in in health policy. But 0:02:19.880 --> 0:02:23.800 if I were to reflect on how my journey in 0:02:23.960 --> 0:02:27.600 health and healthcare started, in some ways, it might have 0:02:27.639 --> 0:02:32.040 started since I was born. UM. I was born in Soul, Korea. 0:02:32.960 --> 0:02:39.480 My I was the eldest child of of my generation 0:02:40.360 --> 0:02:44.519 and UM I got very sick at a very young age. 0:02:45.360 --> 0:02:48.760 When I was several months old, I was having UM 0:02:49.160 --> 0:02:52.239 what was known as failure to thrive and I wasn't 0:02:52.280 --> 0:02:57.120 able to gain weight and the the health care system 0:02:57.160 --> 0:03:01.519 at the time, UM wasn't able to find a solution, 0:03:01.600 --> 0:03:08.160 and my my parents, UM we're told as go go home, 0:03:08.200 --> 0:03:12.079 and and likely that I wasn't going to UM make it, 0:03:12.639 --> 0:03:17.200 and so UM, in a fascinating way, things things changed. 0:03:17.720 --> 0:03:21.919 I was able to gain weight, and my mother, who 0:03:21.960 --> 0:03:26.919 was a nurse, UM, you know uh uh supported my 0:03:26.919 --> 0:03:31.920 my growth. And then we immigrated to the US, a 0:03:32.000 --> 0:03:36.640 country of extraordinary opportunity to to start a new life 0:03:36.720 --> 0:03:39.440 with my dad who's an economist in the World Bank, 0:03:39.480 --> 0:03:43.040 and my mom is a nurse. And UM was fortunate 0:03:43.160 --> 0:03:48.120 enough as an immigrant to to really have a supportive family, 0:03:48.200 --> 0:03:53.200 plus an extraordinary education that helped me recognize, you know, 0:03:53.240 --> 0:03:55.200 the value of health and and the role that we 0:03:55.240 --> 0:03:59.760 could play UM in advancing and improving the health of populations. 0:04:00.720 --> 0:04:04.400 I trained as a physician in internal medicine and pediatrics 0:04:05.080 --> 0:04:08.680 to take care of families. I had the the good 0:04:08.720 --> 0:04:11.480 fortune of taking care of a lot of families, many 0:04:11.480 --> 0:04:16.800 of whom were immigrant families UH in d c. And Baltimore, 0:04:17.640 --> 0:04:23.240 and many from communities of color and poverty. And then 0:04:23.480 --> 0:04:27.960 UM had the good fortune to work in the federal 0:04:28.000 --> 0:04:32.719 government as a health policymaker and look at health disparities 0:04:33.240 --> 0:04:38.159 and the challenges of health and healthcare across the country domestically, 0:04:39.279 --> 0:04:43.400 and even play a small role in the health policy 0:04:43.440 --> 0:04:46.280 around the Affordable Care Act, which played a very significant 0:04:46.360 --> 0:04:51.080 role in expanding care for underserved populations. And now have 0:04:51.320 --> 0:04:54.680 the good fortune nearly a decade working for IBM and 0:04:54.760 --> 0:04:57.160 looking at global public health and and looking at ways 0:04:57.160 --> 0:05:00.559 in which data, analytics and AI can support the health 0:05:00.600 --> 0:05:03.840 of the populations of the clients and partners we serve. 0:05:04.279 --> 0:05:07.240 Fascinating a doctor don Qua Mullin, can you tell us 0:05:07.240 --> 0:05:11.400 a little bit about your background and your journey. Yes, absolutely, 0:05:11.839 --> 0:05:16.600 um so I also a Service Deputy Chief Health Officer 0:05:17.320 --> 0:05:21.560 UM at IBM Watson Health and Chief Health Equity Officer, 0:05:21.680 --> 0:05:26.919 and I have primary responsibility for science, data and evidence 0:05:27.800 --> 0:05:32.279 research and evaluation. And I also as Chief Health Equity 0:05:32.360 --> 0:05:40.640 Officer UM basically helps to ensure AH equity, health, health equity, 0:05:40.680 --> 0:05:48.599 diversity and inclusion UM working with Q and the brilliant 0:05:48.600 --> 0:05:52.360 team at IBM Watson Health. In terms of my journey, 0:05:53.560 --> 0:05:59.160 am I grew up in Ghana. I knew a woman 0:05:59.480 --> 0:06:02.680 at our heart who was also called Irene Um. She 0:06:02.839 --> 0:06:06.280 was a dentist UH and I actually called an anti 0:06:06.360 --> 0:06:08.880 Irene even though there were no relation because she was 0:06:09.920 --> 0:06:13.279 At that time, there were very few women doctors in 0:06:13.279 --> 0:06:16.480 Ghana that I knew, and I was really inspired by 0:06:16.560 --> 0:06:22.800 hair so Um. To be honest, I was also motivated 0:06:22.880 --> 0:06:28.040 by the idea of being thought as someone smart, intelligent, caring, 0:06:28.080 --> 0:06:32.440 and a dedicated physician who was also a woman. So 0:06:32.560 --> 0:06:35.159 I really really wanted to have those qualities that I 0:06:35.200 --> 0:06:39.800 saw in in anti Irene. Um. In addition to growing 0:06:39.839 --> 0:06:43.960 up in Ghana, which was which is quite different right 0:06:44.160 --> 0:06:47.479 from the US, there was a lot of illness that 0:06:47.600 --> 0:06:52.200 I saw that were chronic um and diseases that were 0:06:52.240 --> 0:06:58.360 easily preventable with either vaccination or it's cleaning and early detection. UM. 0:06:58.400 --> 0:07:02.760 I actually remember getting moms. I remember, UM. I don't 0:07:02.800 --> 0:07:05.400 remember getting measles, but I was told I had measles 0:07:05.480 --> 0:07:09.000 as a child. Um. You know, a health care system 0:07:09.080 --> 0:07:13.080 was was overburdened, health care was rationed and I and 0:07:13.120 --> 0:07:16.120 I experienced and I saw all of this. But I 0:07:16.160 --> 0:07:21.080 was also drawn by this opportunity to pursue a deeper 0:07:21.400 --> 0:07:25.160 um scientific understanding of the human body, right the physiology 0:07:25.160 --> 0:07:29.760 of the disease, why it occurs, UM and UM I 0:07:29.800 --> 0:07:32.680 sort of really knew that there was disease outside of 0:07:32.760 --> 0:07:36.840 just UM clinical care because of what I saw with 0:07:37.440 --> 0:07:43.000 you know, lack of nutrition and hydration UM in in 0:07:43.720 --> 0:07:46.800 growing up, and so as I entered I came here 0:07:47.280 --> 0:07:51.200 UM actually for college after I finished high school UM I, 0:07:51.520 --> 0:07:56.760 and when I entered medical school and residency and learn 0:07:56.800 --> 0:07:59.240 more about determinants of health, it was sort of on 0:07:59.320 --> 0:08:04.400 aha moment and UM in medical school, I went into 0:08:04.440 --> 0:08:07.640 public health as well, so I did a double public 0:08:07.680 --> 0:08:12.840 health medicine degree and UM MY and becoming a primary 0:08:12.880 --> 0:08:16.520 care physician UM was what I wanted to do. And 0:08:16.560 --> 0:08:20.120 I realized really, yes, I wanted to care for vulnerable 0:08:20.200 --> 0:08:24.920 and socially disadvantage populations. I wanted to be more compassionate, 0:08:25.440 --> 0:08:30.880 you know, listen and understanding and value UM their culture 0:08:31.040 --> 0:08:35.800 and beliefs. UM. But but I also had that motivation 0:08:35.840 --> 0:08:39.120 about changing the way medicine was always focused on clinical 0:08:39.160 --> 0:08:42.440 care in the US, you know, seeing the sick chronically ill, 0:08:43.160 --> 0:08:48.720 to focus on also addressing determinants and disparities and and 0:08:48.800 --> 0:08:55.040 supporting interventions around what we know as social determinants, and 0:08:55.480 --> 0:08:59.760 you know, both need to work together. So I had 0:09:00.120 --> 0:09:04.480 do you just like UM Dr v i UM worked 0:09:04.520 --> 0:09:08.440 in public health and also had the opportunity to work 0:09:08.480 --> 0:09:11.920 at the National Institutes of Health the health care side. 0:09:11.960 --> 0:09:14.120 Can you talk a bit about what it is IBM 0:09:14.200 --> 0:09:18.439 is doing in that space, what are your teams actually pursuing. 0:09:19.000 --> 0:09:24.199 Health is so foundational and essential to the value proposition 0:09:24.280 --> 0:09:27.000 that I hope and I believe IBM has played and 0:09:27.040 --> 0:09:30.560 will play during this crisis and beyond UM. If you 0:09:30.600 --> 0:09:37.360 think about UM information, data, analytics, and and and the 0:09:37.400 --> 0:09:42.040 opportunities around artificial intelligence and machine learning and analytics and 0:09:42.080 --> 0:09:48.400 predictive analytics UM, there's such an important role to address 0:09:48.520 --> 0:09:55.079 health UM for the what I would call the multiple 0:09:55.120 --> 0:09:59.400 stakeholders in a health ecosystem. If you think about how 0:10:00.679 --> 0:10:05.240 data and even care or money flows in healthcare, which 0:10:05.320 --> 0:10:07.719 in the US represents one in five dollars and in 0:10:08.520 --> 0:10:11.920 most developed countries one in ten dollars and in most 0:10:11.960 --> 0:10:16.920 developing countries one and twenty. The the nature of health 0:10:16.960 --> 0:10:20.200 and health care is that it typically you've got a patient, 0:10:20.280 --> 0:10:24.520 a citizen, a consumer who comes in with a challenge 0:10:24.520 --> 0:10:30.280 on an issue. UM. We know that of that spend 0:10:31.040 --> 0:10:33.520 of that challenge in terms of costs is related to 0:10:33.640 --> 0:10:39.480 chronic diseases like diabetes, like heart disease, like asthma, like COPD, 0:10:39.640 --> 0:10:44.320 like depression, like arthritis, like cancer. And there is a 0:10:45.240 --> 0:10:49.480 encounter that I that Irene had, you know, have the 0:10:49.520 --> 0:10:51.840 good fortunate as physicians to take care of in that 0:10:51.960 --> 0:10:55.360 privileged moment to take care of a patient you know, 0:10:56.080 --> 0:10:59.880 and and and and offer support in those short you know, 0:11:00.040 --> 0:11:04.240 five to ten minutes sometimes twenty minutes conversations and interactions, 0:11:04.360 --> 0:11:07.480 and and that data flows in a certain way. It 0:11:07.520 --> 0:11:10.560 flows to a pay er a health plan. It flows 0:11:10.880 --> 0:11:16.280 to an employer who often is the one who pays 0:11:16.280 --> 0:11:20.840 those bills for for their workforce and their family members. 0:11:21.520 --> 0:11:25.000 It flows potentially to a farmer company as they think 0:11:25.000 --> 0:11:29.200 about studies and trials um. It flows to a government 0:11:29.240 --> 0:11:31.320 if you think about all the testing that's happening with 0:11:31.400 --> 0:11:35.640 COVID nineteen now and and and the challenges of contact 0:11:35.679 --> 0:11:41.000 tracing and treatment in isolation and quarantining. So there's an 0:11:41.040 --> 0:11:45.440 ecosystem here as it relates to health and the impact 0:11:45.480 --> 0:11:50.120 that health has on has on you know, people, communities, families, 0:11:50.240 --> 0:11:53.480 and the role that data analytics and AI and the 0:11:53.520 --> 0:11:58.000 expertise of people at IBM who you know, are experts 0:11:58.040 --> 0:12:01.360 in data science, are expert into and computing, our experts 0:12:01.400 --> 0:12:05.760 into you know, Watson and machine learning to bring those 0:12:05.800 --> 0:12:09.040 worlds together of tech and health and healthcare. I mean, 0:12:09.040 --> 0:12:11.880 what better endeavor than to try to improve the health 0:12:11.920 --> 0:12:17.360 of populations. Fantastic And this kind of also brings us 0:12:17.400 --> 0:12:21.000 to the discussion at hand for today. This is a 0:12:21.120 --> 0:12:25.800 huge topic disparity in access to healthcare, to health services, 0:12:26.000 --> 0:12:30.040 to health information, and that it has as of itself. 0:12:30.280 --> 0:12:32.440 It's it's such an enormous thing and it has so 0:12:32.480 --> 0:12:36.880 many facets. It's challenging to talk about because there are 0:12:36.920 --> 0:12:38.840 so many different ways we could go at it. We 0:12:38.880 --> 0:12:45.640 could look at it along aspects of socio economic levels, regions, 0:12:45.760 --> 0:12:49.520 we could look at it by race, and it is 0:12:49.880 --> 0:12:53.680 a complicated issue. Can you talk a little bit about 0:12:53.679 --> 0:12:58.640 the overall concept of health disparity. We have a challenge 0:12:58.840 --> 0:13:03.480 globally domad e stickally in communities all across this country 0:13:03.480 --> 0:13:06.120 and all across the globe where there are members of 0:13:06.120 --> 0:13:10.920 our family who are ill, who suffer disproportionately from illness 0:13:11.000 --> 0:13:15.400 from those chronic diseases like diabetes, like heart disease, like cancer, 0:13:16.040 --> 0:13:20.080 like asthma, like depression, And we have an opportunity and 0:13:20.200 --> 0:13:24.840 responsibility in terms of our values to find ways to 0:13:24.960 --> 0:13:28.600 bring those folks back to better health. And unfortunately, many 0:13:28.640 --> 0:13:33.040 of the factors that represent how those family members are 0:13:33.080 --> 0:13:38.240 ill are are are based on risk factors that are 0:13:38.280 --> 0:13:44.079 associated with things like race or ethnicity, or sexual orientation 0:13:44.320 --> 0:13:49.479 or socio economic status or you know, education or employment. 0:13:49.600 --> 0:13:53.440 And so it's so essential for us to to to 0:13:53.559 --> 0:13:57.560 think about this as a society, to think about the 0:13:57.640 --> 0:14:00.800 values that we believe are essential for us to be 0:14:01.080 --> 0:14:04.320 you know, you know, to support our family members, but 0:14:04.440 --> 0:14:08.800 also you know, create a dynamic where we we we 0:14:08.880 --> 0:14:12.280 we bring those those health disparity populations back up in 0:14:12.400 --> 0:14:15.840 terms of health. So that's how I you know, simplified 0:14:15.960 --> 0:14:18.000 or think about it in terms of health disparities. And 0:14:18.040 --> 0:14:22.760 to me, equity represents that hope that all members of 0:14:22.800 --> 0:14:26.360 our family are are healthy and how do we achieve that? Yes, 0:14:26.440 --> 0:14:31.240 and I can add to UM the concept of health 0:14:31.320 --> 0:14:36.800 disparities and and even share a story UM as an example. 0:14:38.400 --> 0:14:44.560 So there are health differences and their health disparities, and 0:14:44.600 --> 0:14:48.160 when we the health difference for example, a health difference 0:14:48.200 --> 0:14:56.640 for example is UM the elderly population having more you know, diseases, 0:14:56.760 --> 0:15:00.320 or morbidity than the younger population. Right, So that's that's 0:15:00.320 --> 0:15:05.560 a health difference. And when we talk about health disparities, 0:15:06.160 --> 0:15:10.200 we are referring to that particular type of health difference 0:15:11.040 --> 0:15:18.280 that is linked with a social or economic or environmental disadvantage. 0:15:18.760 --> 0:15:22.520 So UM, in terms of address and health disparities, we 0:15:23.000 --> 0:15:25.920 try to understand the root causes of why they exist 0:15:26.000 --> 0:15:31.880 because they are complex. UM. Disease and illness are complex, 0:15:32.080 --> 0:15:35.320 not just from one factor or due to one single factor, 0:15:35.400 --> 0:15:41.080 but due to multiple structural policy decisions that we make 0:15:41.120 --> 0:15:45.160 as a society. UM. For example, having access to clean, 0:15:45.440 --> 0:15:50.040 safe and healthy environment, having access to healthy food UM, 0:15:50.480 --> 0:15:56.840 and overall UM not being breadened by everyday stresses. UM. 0:15:56.880 --> 0:16:00.360 There's there's a lot of stress from being for low 0:16:00.440 --> 0:16:06.120 income or unemployed UM. And so the stresses that are 0:16:06.320 --> 0:16:12.560 experienced disproportionately as he was mentioning by people with social 0:16:12.600 --> 0:16:18.320 disadvantage or by those who have experienced racism or discrimination 0:16:19.480 --> 0:16:24.840 UM are all as impact health and and are seen 0:16:24.880 --> 0:16:29.840 as health disparities. And so solutions for health disparities are 0:16:29.920 --> 0:16:36.760 not always just medical or clinical UM. And I and 0:16:36.800 --> 0:16:39.400 in terms of a story that I wanted to share, 0:16:39.920 --> 0:16:45.240 UM sort of a close and personalities around racial disparities 0:16:45.520 --> 0:16:51.160 in maternal UM and infant health or pre timbers or 0:16:51.200 --> 0:16:55.400 infant mortality. I think, UM, you know, there's a lot 0:16:55.520 --> 0:17:00.680 of literature and scientific evidence UM around the huge, huge 0:17:00.720 --> 0:17:06.040 disparity GAS between African American UM blacks and white um 0:17:06.119 --> 0:17:10.920 in in in maternal mortality, in infan mortality UM as 0:17:10.920 --> 0:17:14.960 well as pre timbers UM or premature new natal birds. 0:17:16.160 --> 0:17:21.840 There's ample evidence from studies that show that cettain maternal 0:17:22.000 --> 0:17:28.959 risk factors UM that explained these disparities are mostly from 0:17:29.200 --> 0:17:35.680 racism or racial disparities, and the experience of systemic racial bias, 0:17:36.000 --> 0:17:40.720 not not just raise itself, can compromise health UM in 0:17:40.720 --> 0:17:46.560 In Ghana, infant mortality was was high, I mean, and 0:17:46.600 --> 0:17:50.800 it was seen mostly with women from loyal socio economic 0:17:50.880 --> 0:17:55.119 status or regions with very very poor health care infrastructure. 0:17:55.880 --> 0:18:00.159 But however, in the US, in the United States UM 0:18:01.119 --> 0:18:06.800 in fun mortality or blacks, US blacks across all socio 0:18:06.840 --> 0:18:11.440 economic status have poor maternal health outcomes and infant mortality 0:18:11.480 --> 0:18:16.480 outcomes compared to their UM white counterparts, even in the 0:18:16.560 --> 0:18:19.960 same socio economic status UM. And in fact, there's a 0:18:20.040 --> 0:18:24.359 study that showed that Black woman with a higher education 0:18:24.640 --> 0:18:30.399 like college level or graduate level degree have similar rates 0:18:30.440 --> 0:18:34.240 compared to similar rates to whites who only have a 0:18:34.480 --> 0:18:39.040 high school education in terms of UM in fun mortality. 0:18:39.760 --> 0:18:44.359 So a lot of African American families, for example, I mean, 0:18:44.400 --> 0:18:49.160 so this is all due to UM, some underlying systemic 0:18:49.880 --> 0:18:55.959 UM or determinants or social determinants of health. UM. And 0:18:56.000 --> 0:19:00.919 of course there's ongoing research to really tease out UM 0:19:01.160 --> 0:19:04.040 why that is the case. And I myself, you know, 0:19:04.119 --> 0:19:08.520 surprisingly experienced the high risk pregnancy. UM. I had a 0:19:08.600 --> 0:19:12.200 pretember UM and I thought I was doing everything right. 0:19:12.320 --> 0:19:14.760 I mean, for myself, I was eating well. I thought 0:19:14.800 --> 0:19:20.440 being educated put me at least risk for any adverse outcomes. UM. 0:19:20.640 --> 0:19:24.520 Deadly in Ghana, it would have so UM, you know, 0:19:24.680 --> 0:19:29.159 being in the u United States environment, being in this 0:19:29.440 --> 0:19:32.360 probably you know, I'm not saying, I'm not sure exactly 0:19:32.359 --> 0:19:34.600 what calls it, but you know, I, as an example, 0:19:34.760 --> 0:19:38.399 was quite surprised. Um. Of course everything has turned out fine. 0:19:38.640 --> 0:19:41.520 My daughter is will turn eighteen at the end of 0:19:41.560 --> 0:19:45.120 this month, and UM, she's off to college. So it's 0:19:46.359 --> 0:19:49.960 think think everything works out, work down well. So that's 0:19:50.000 --> 0:19:53.040 sort of my story that I wanted to share UM 0:19:53.119 --> 0:19:57.440 that yes, you know, this is an example of the disparities. 0:19:57.840 --> 0:20:01.520 And I believe it's safe to ay that the current 0:20:01.800 --> 0:20:07.200 COVID nineteen crisis has really highlighted disparities in a lot 0:20:07.240 --> 0:20:11.880 of ways. That we've seen this play out tragically with 0:20:12.080 --> 0:20:18.200 the response to to COVID nineteen within certain communities. How 0:20:18.320 --> 0:20:25.800 has COVID nineteen impacted or affected this issue? Oh goodness, UM, 0:20:26.080 --> 0:20:32.040 COVID has definitely shaped our world. UM. It has exposed 0:20:32.160 --> 0:20:38.160 huge inequalities and health UM in healthcare UM, the burden 0:20:38.200 --> 0:20:44.560 of underlying disease and access outcomes UM from from COVID nineteen. 0:20:44.760 --> 0:20:47.600 What we see and then hearing actually from the front 0:20:47.640 --> 0:20:54.440 lines are UM glaring inequalities not only in the US, 0:20:54.560 --> 0:20:58.119 but also globally. UM. In the United States, we're actually 0:20:58.160 --> 0:21:04.760 seen the spread is from COVID across race as well race, ethnicity, 0:21:04.800 --> 0:21:11.000 and geography, right, so mostly UM along socio economic lines. UM. 0:21:11.080 --> 0:21:15.120 The disparities that we're seeing are in you know, testing 0:21:15.240 --> 0:21:21.040 rates and infection, severe disease, illness, UM, hospitalization and even 0:21:21.119 --> 0:21:27.400 I see you UM outcomes And so basically what we're 0:21:27.400 --> 0:21:32.480 seeing is something really structural and systemic. In addition to 0:21:32.640 --> 0:21:38.160 what we know are higher COOL mobilities in low sitio 0:21:38.160 --> 0:21:44.280 economic status UM minorities as well as with with racio 0:21:44.320 --> 0:21:49.720 ethnic minorities or people from communities of color. UM. We've 0:21:49.760 --> 0:21:53.520 come to find out that it's also UM higher among 0:21:53.760 --> 0:22:01.199 language minorities. So the COVID positive cases or infection UM 0:22:01.359 --> 0:22:05.040 is not also only occurring along racial ethnic clients, but 0:22:06.000 --> 0:22:11.960 UM also according to the we see that along the 0:22:12.040 --> 0:22:16.000 impact of we've seen the impact of racial segregation, I 0:22:16.000 --> 0:22:22.040 would say, UM, working class UM, the lack of home 0:22:22.080 --> 0:22:25.920 ownership or wealth, and how people living in these communities 0:22:26.640 --> 0:22:31.240 really are impacted by the labor market right UM. These 0:22:31.280 --> 0:22:34.040 are we've seen individuals who really have to go to work, 0:22:34.200 --> 0:22:37.439 you know that to pay their bills, leading to higher 0:22:37.440 --> 0:22:41.080 exposure dr read What are some of the most challenging 0:22:41.480 --> 0:22:44.640 racial and ethnic disparities in public health and health care 0:22:44.680 --> 0:22:48.240 as you see it? One quick truth to recognize is 0:22:48.280 --> 0:22:50.840 that health is so much more than health care. That 0:22:50.920 --> 0:22:53.959 doesn't mean health care isn't essentially it is, but if 0:22:54.000 --> 0:22:57.919 you think about the determinants that play a role in 0:22:58.000 --> 0:23:01.800 health outcomes, it's almost you could think one, two, three, four, 0:23:04.600 --> 0:23:11.320 which adds up to UM. The the broader recognition. When 0:23:11.359 --> 0:23:15.200 you talk about disparities and inequities, you have to recognize 0:23:16.080 --> 0:23:20.600 the ten percent which is clinical genomics and and and 0:23:20.760 --> 0:23:26.840 some of that's connected to this this common history UM. 0:23:27.520 --> 0:23:30.159 You know, family history of chronic diseases makes you a 0:23:30.280 --> 0:23:33.119 higher risk of having a chronic disease, so UM. And 0:23:33.160 --> 0:23:39.679 then the thirty social, environmental, and behavioral and so so 0:23:39.760 --> 0:23:42.679 many of our communities are challenged where the statement that 0:23:42.760 --> 0:23:45.200 your zip code is more important than your genetic code 0:23:46.400 --> 0:23:50.399 is really true. Place matters, UM. You can in the 0:23:50.560 --> 0:23:54.960 same we use this UM reference in in d C. 0:23:55.160 --> 0:23:58.840 When I worked serving and underserved communities in DC. You know, 0:23:58.920 --> 0:24:01.480 you could go on the metro and you can go 0:24:01.560 --> 0:24:05.480 from one stop to another stop and literally span twenty 0:24:05.520 --> 0:24:10.000 to thirty years of life expectancy. So I want to 0:24:10.600 --> 0:24:16.520 highlight that. So when you look across almost every health condition, 0:24:16.560 --> 0:24:20.879 there are disparities that exists, and there's opportunities for equity. 0:24:21.040 --> 0:24:25.080 And it requires requires us to understand the determinants. It 0:24:25.119 --> 0:24:29.000 requires us to bring in the right data and then 0:24:29.040 --> 0:24:33.080 to influence the decision makers. So I'm on this three 0:24:33.160 --> 0:24:37.720 D commission, we call it Determinants Data Decisions that's sponsored 0:24:37.720 --> 0:24:42.480 by the Rockefeller Foundation and UM and the School Public 0:24:42.480 --> 0:24:45.800 Health at BEU. And this is very important to think 0:24:45.840 --> 0:24:49.840 about because we've we've got an opportunity now with COVID 0:24:50.760 --> 0:24:53.879 and all the data and the determinants science we know 0:24:54.680 --> 0:24:59.960 to influence decisions in the disparities that exist in public 0:25:00.040 --> 0:25:03.440 health and healthcare. UM. COVID is making us more aware 0:25:03.480 --> 0:25:08.280 of mental illness, their disparities there UM in depression and 0:25:08.400 --> 0:25:12.639 anxiety disorders and psychotic disorders and addictions that exists that 0:25:12.680 --> 0:25:15.919 need to be addressed and flatten those curves have to 0:25:15.920 --> 0:25:20.800 be flattened as well, there are chronic disease curves that 0:25:20.840 --> 0:25:24.879 need to be flattened. UM. That's worsening, especially for communities 0:25:24.880 --> 0:25:29.720 of color and poverty. So you know, all the chronic 0:25:29.760 --> 0:25:32.639 diseases of already referenced from diabetes, are heart disease to 0:25:32.760 --> 0:25:37.840 asthma to SELPD, to depression, to arthritis to cancer. And 0:25:37.880 --> 0:25:41.200 then the other curve that we have to really address 0:25:41.320 --> 0:25:44.879 and and be really you know, open about, is the 0:25:45.000 --> 0:25:48.840 curve of inequities and the role of structural racism and 0:25:48.880 --> 0:25:52.240 discrimination in our in our systems, and how can we 0:25:52.280 --> 0:25:58.120 address that UM and really invest in diversity and inclusion 0:25:58.680 --> 0:26:04.080 and equity so UM. I mean, there's so much opportunity 0:26:04.160 --> 0:26:08.520 here for us to take this moment with COVID nineteen 0:26:09.720 --> 0:26:13.320 to be upfront about what we have, what we need 0:26:13.359 --> 0:26:17.960 to do, and what we want to build in terms 0:26:18.000 --> 0:26:20.119 of the health system of the future. It sounds like 0:26:20.160 --> 0:26:23.120 there's a great deal of work to be done. I'm 0:26:23.240 --> 0:26:26.280 very curious to learn more about what it is that 0:26:26.440 --> 0:26:30.120 IBM and more specifically what IBM what's in health are 0:26:30.200 --> 0:26:34.679 doing in an effort to try and address these challenges. 0:26:34.720 --> 0:26:39.199 I mean, clearly, this is something bigger than what is 0:26:39.200 --> 0:26:42.280 going to take you know, a tech solution. Dr you 0:26:42.359 --> 0:26:44.879 mentioned that earlier, it's going to require a lot of 0:26:44.920 --> 0:26:49.120 different work. But what is IBMS peace in this What 0:26:49.160 --> 0:26:52.320 are what are you guys doing in your efforts to 0:26:52.480 --> 0:26:58.359 kind of address the issue of health disparity. So we 0:26:58.560 --> 0:27:04.679 see r ole by leveraging technology, but ultimately by by 0:27:04.880 --> 0:27:08.800 looking at trust. I think so much of health and 0:27:08.840 --> 0:27:13.640 healthcare is still foundationally about relationships and trust, and so 0:27:13.720 --> 0:27:17.280 as you think about a journey with IBM, it is 0:27:18.240 --> 0:27:27.639 working with life science companies, hospitals, health systems, governments, um 0:27:27.800 --> 0:27:33.000 and employers and businesses and health plans in a partnership 0:27:33.359 --> 0:27:36.919 with what I would call shared expertise. You know, some 0:27:37.000 --> 0:27:40.760 of the brightest minds and the smartest minds who are 0:27:40.840 --> 0:27:44.280 very global and very diverse across the globe in data 0:27:44.359 --> 0:27:48.040 science and AI and health and healthcare, like like we've 0:27:48.040 --> 0:27:51.280 got with Dr Danko Mullen and and others on our 0:27:51.359 --> 0:27:55.560 our team to work with those other partners and clients 0:27:56.240 --> 0:28:02.240 and to evolve that shared expertise into conversations about data. 0:28:02.520 --> 0:28:05.280 How do we connect these unique data sets, How do 0:28:05.359 --> 0:28:08.480 we protect these data sets because so much about data 0:28:08.520 --> 0:28:12.439 is about trust UM, And then how do we bring 0:28:12.480 --> 0:28:16.720 them together and apply analytics and artificial intelligence and advanced 0:28:16.720 --> 0:28:22.639 analytics to bring insights that better predict, that better personalized, 0:28:23.480 --> 0:28:29.080 and that better prevents bad outcomes and promote good outcomes. 0:28:29.160 --> 0:28:33.240 And so, you know, we're very proud of the work 0:28:33.240 --> 0:28:37.119 we've done with so many different clients to deliver that 0:28:37.280 --> 0:28:43.960 value and that shared expertise UM and those insights from 0:28:44.000 --> 0:28:47.800 the data, analytics and AI. Dr don Komlin, I have 0:28:47.880 --> 0:28:51.200 a question for you about data and analytics. I mean, 0:28:51.240 --> 0:28:55.480 we we know that data is important, but obviously data 0:28:55.680 --> 0:28:59.160 doesn't matter so much unless you're able to do something 0:28:59.520 --> 0:29:04.920 actionable with it. So, how can organizations actually apply data 0:29:05.040 --> 0:29:09.520 and analytics to make better decisions or to create better 0:29:09.560 --> 0:29:15.880 outcomes for themselves? What are some actual processes that you 0:29:16.000 --> 0:29:23.720 look at. Data is actually a very powerful tool UM. 0:29:23.760 --> 0:29:29.080 I've heard someone saying how data is actually a lifeline. UM. 0:29:29.200 --> 0:29:37.959 It's it's very important. And so organizations can definitely you know, 0:29:38.080 --> 0:29:44.880 incorporated health equity UM lens when applying data and analytics, 0:29:45.680 --> 0:29:51.880 especially for decision making UM, in order to really see 0:29:51.960 --> 0:29:57.959 improved outcomes or or better outcomes UM. And they of 0:29:58.000 --> 0:30:01.680 course they also need to ensure that while using the 0:30:01.800 --> 0:30:08.160 data that we are addressing any transparency UM bias as 0:30:08.160 --> 0:30:12.040 well as UM ethical issues that are usually at the 0:30:12.120 --> 0:30:17.080 core of UM data use. UH. You know, in terms 0:30:17.160 --> 0:30:20.520 of our even the current strategy to address COVID response 0:30:21.120 --> 0:30:26.200 M recovery and even preparedness for for a potential wave 0:30:26.320 --> 0:30:30.600