1 00:00:00,680 --> 00:00:05,279 Speaker 1: This is a very real problem that directly affects millions 2 00:00:05,320 --> 00:00:08,640 Speaker 1: of people around the world. If we expand that to 3 00:00:08,720 --> 00:00:12,479 Speaker 1: look at the indirect effects, such as how disparity creates 4 00:00:12,600 --> 00:00:17,280 Speaker 1: enormous stress on communities around the world, we're talking about 5 00:00:17,480 --> 00:00:21,400 Speaker 1: billions of people who are impacted by this issue. And 6 00:00:21,480 --> 00:00:25,840 Speaker 1: for me at least, this was a really big problem 7 00:00:26,040 --> 00:00:30,800 Speaker 1: where using technology wasn't an apparent path. If you were 8 00:00:30,840 --> 00:00:34,240 Speaker 1: to ask me how technology could help address an issue 9 00:00:34,440 --> 00:00:37,640 Speaker 1: like the disparity among different populations when it comes to 10 00:00:37,720 --> 00:00:41,320 Speaker 1: health care access, I would be at a loss. But 11 00:00:41,400 --> 00:00:45,240 Speaker 1: as it turns out, technology can play an important role 12 00:00:45,479 --> 00:00:49,720 Speaker 1: in that effort. As we'll learn, tech is really one 13 00:00:49,720 --> 00:00:54,320 Speaker 1: piece of it. Real solutions to eliminating disparity will require 14 00:00:54,480 --> 00:00:58,720 Speaker 1: much more than technology. I spoke with Dr q Ree, 15 00:00:59,160 --> 00:01:02,400 Speaker 1: Chief Health off Sir at IBM and Dr Irene don 16 00:01:02,480 --> 00:01:06,880 Speaker 1: Qua Mullen, Deputy Chief Health Officer IBM Watson Health about 17 00:01:06,920 --> 00:01:10,800 Speaker 1: this issue. Both doctors have dedicated an enormous amount of 18 00:01:10,840 --> 00:01:14,840 Speaker 1: time and effort as positions and data experts to create 19 00:01:14,840 --> 00:01:18,960 Speaker 1: a more equitable access to health services across all communities. 20 00:01:19,440 --> 00:01:22,440 Speaker 1: They helped me get a deeper understanding of the challenges 21 00:01:22,520 --> 00:01:26,640 Speaker 1: we face, how they impact millions of people, and the 22 00:01:26,680 --> 00:01:31,280 Speaker 1: way technology plays a part in addressing the problem. Thank 23 00:01:31,280 --> 00:01:33,600 Speaker 1: you both for joining me today. We have a lot 24 00:01:33,640 --> 00:01:36,800 Speaker 1: of ground to cover and a big topic to talk about, 25 00:01:36,840 --> 00:01:39,640 Speaker 1: but before we really dive into that, I wanted to 26 00:01:39,720 --> 00:01:42,720 Speaker 1: hear a little bit about your personal story, about your 27 00:01:42,840 --> 00:01:46,319 Speaker 1: journey and how you got to where you are today 28 00:01:46,400 --> 00:01:52,400 Speaker 1: and your personal reflections upon the really big challenge of 29 00:01:52,640 --> 00:01:56,640 Speaker 1: disparities and access to health and health care services. Dr Rey, 30 00:01:57,000 --> 00:01:59,600 Speaker 1: would you care to share a little bit of your 31 00:01:59,640 --> 00:02:03,840 Speaker 1: background owned and your personal experience. Sure, now, I appreciate that. 32 00:02:04,080 --> 00:02:08,120 Speaker 1: So I currently serve as the chief Health Officer for 33 00:02:08,200 --> 00:02:13,960 Speaker 1: IBM and Watson Health. I'm a physician by training and 34 00:02:14,200 --> 00:02:19,200 Speaker 1: also UM have some background in in health policy. But 35 00:02:19,880 --> 00:02:23,800 Speaker 1: if I were to reflect on how my journey in 36 00:02:23,960 --> 00:02:27,600 Speaker 1: health and healthcare started, in some ways, it might have 37 00:02:27,639 --> 00:02:32,040 Speaker 1: started since I was born. UM. I was born in Soul, Korea. 38 00:02:32,960 --> 00:02:39,480 Speaker 1: My I was the eldest child of of my generation 39 00:02:40,360 --> 00:02:44,519 Speaker 1: and UM I got very sick at a very young age. 40 00:02:45,360 --> 00:02:48,760 Speaker 1: When I was several months old, I was having UM 41 00:02:49,160 --> 00:02:52,239 Speaker 1: what was known as failure to thrive and I wasn't 42 00:02:52,280 --> 00:02:57,120 Speaker 1: able to gain weight and the the health care system 43 00:02:57,160 --> 00:03:01,519 Speaker 1: at the time, UM wasn't able to find a solution, 44 00:03:01,600 --> 00:03:08,160 Speaker 1: and my my parents, UM we're told as go go home, 45 00:03:08,200 --> 00:03:12,079 Speaker 1: and and likely that I wasn't going to UM make it, 46 00:03:12,639 --> 00:03:17,200 Speaker 1: and so UM, in a fascinating way, things things changed. 47 00:03:17,720 --> 00:03:21,919 Speaker 1: I was able to gain weight, and my mother, who 48 00:03:21,960 --> 00:03:26,919 Speaker 1: was a nurse, UM, you know uh uh supported my 49 00:03:26,919 --> 00:03:31,920 Speaker 1: my growth. And then we immigrated to the US, a 50 00:03:32,000 --> 00:03:36,640 Speaker 1: country of extraordinary opportunity to to start a new life 51 00:03:36,720 --> 00:03:39,440 Speaker 1: with my dad who's an economist in the World Bank, 52 00:03:39,480 --> 00:03:43,040 Speaker 1: and my mom is a nurse. And UM was fortunate 53 00:03:43,160 --> 00:03:48,120 Speaker 1: enough as an immigrant to to really have a supportive family, 54 00:03:48,200 --> 00:03:53,200 Speaker 1: plus an extraordinary education that helped me recognize, you know, 55 00:03:53,240 --> 00:03:55,200 Speaker 1: the value of health and and the role that we 56 00:03:55,240 --> 00:03:59,760 Speaker 1: could play UM in advancing and improving the health of populations. 57 00:04:00,720 --> 00:04:04,400 Speaker 1: I trained as a physician in internal medicine and pediatrics 58 00:04:05,080 --> 00:04:08,680 Speaker 1: to take care of families. I had the the good 59 00:04:08,720 --> 00:04:11,480 Speaker 1: fortune of taking care of a lot of families, many 60 00:04:11,480 --> 00:04:16,800 Speaker 1: of whom were immigrant families UH in d c. And Baltimore, 61 00:04:17,640 --> 00:04:23,240 Speaker 1: and many from communities of color and poverty. And then 62 00:04:23,480 --> 00:04:27,960 Speaker 1: UM had the good fortune to work in the federal 63 00:04:28,000 --> 00:04:32,719 Speaker 1: government as a health policymaker and look at health disparities 64 00:04:33,240 --> 00:04:38,159 Speaker 1: and the challenges of health and healthcare across the country domestically, 65 00:04:39,279 --> 00:04:43,400 Speaker 1: and even play a small role in the health policy 66 00:04:43,440 --> 00:04:46,280 Speaker 1: around the Affordable Care Act, which played a very significant 67 00:04:46,360 --> 00:04:51,080 Speaker 1: role in expanding care for underserved populations. And now have 68 00:04:51,320 --> 00:04:54,680 Speaker 1: the good fortune nearly a decade working for IBM and 69 00:04:54,760 --> 00:04:57,160 Speaker 1: looking at global public health and and looking at ways 70 00:04:57,160 --> 00:05:00,559 Speaker 1: in which data, analytics and AI can support the health 71 00:05:00,600 --> 00:05:03,840 Speaker 1: of the populations of the clients and partners we serve. 72 00:05:04,279 --> 00:05:07,240 Speaker 1: Fascinating a doctor don Qua Mullin, can you tell us 73 00:05:07,240 --> 00:05:11,400 Speaker 1: a little bit about your background and your journey. Yes, absolutely, 74 00:05:11,839 --> 00:05:16,600 Speaker 1: um so I also a Service Deputy Chief Health Officer 75 00:05:17,320 --> 00:05:21,560 Speaker 1: UM at IBM Watson Health and Chief Health Equity Officer, 76 00:05:21,680 --> 00:05:26,919 Speaker 1: and I have primary responsibility for science, data and evidence 77 00:05:27,800 --> 00:05:32,279 Speaker 1: research and evaluation. And I also as Chief Health Equity 78 00:05:32,360 --> 00:05:40,640 Speaker 1: Officer UM basically helps to ensure AH equity, health, health equity, 79 00:05:40,680 --> 00:05:48,599 Speaker 1: diversity and inclusion UM working with Q and the brilliant 80 00:05:48,600 --> 00:05:52,360 Speaker 1: team at IBM Watson Health. In terms of my journey, 81 00:05:53,560 --> 00:05:59,160 Speaker 1: am I grew up in Ghana. I knew a woman 82 00:05:59,480 --> 00:06:02,680 Speaker 1: at our heart who was also called Irene Um. She 83 00:06:02,839 --> 00:06:06,280 Speaker 1: was a dentist UH and I actually called an anti 84 00:06:06,360 --> 00:06:08,880 Speaker 1: Irene even though there were no relation because she was 85 00:06:09,920 --> 00:06:13,279 Speaker 1: At that time, there were very few women doctors in 86 00:06:13,279 --> 00:06:16,480 Speaker 1: Ghana that I knew, and I was really inspired by 87 00:06:16,560 --> 00:06:22,800 Speaker 1: hair so Um. To be honest, I was also motivated 88 00:06:22,880 --> 00:06:28,040 Speaker 1: by the idea of being thought as someone smart, intelligent, caring, 89 00:06:28,080 --> 00:06:32,440 Speaker 1: and a dedicated physician who was also a woman. So 90 00:06:32,560 --> 00:06:35,159 Speaker 1: I really really wanted to have those qualities that I 91 00:06:35,200 --> 00:06:39,800 Speaker 1: saw in in anti Irene. Um. In addition to growing 92 00:06:39,839 --> 00:06:43,960 Speaker 1: up in Ghana, which was which is quite different right 93 00:06:44,160 --> 00:06:47,479 Speaker 1: from the US, there was a lot of illness that 94 00:06:47,600 --> 00:06:52,200 Speaker 1: I saw that were chronic um and diseases that were 95 00:06:52,240 --> 00:06:58,360 Speaker 1: easily preventable with either vaccination or it's cleaning and early detection. UM. 96 00:06:58,400 --> 00:07:02,760 Speaker 1: I actually remember getting moms. I remember, UM. I don't 97 00:07:02,800 --> 00:07:05,400 Speaker 1: remember getting measles, but I was told I had measles 98 00:07:05,480 --> 00:07:09,000 Speaker 1: as a child. Um. You know, a health care system 99 00:07:09,080 --> 00:07:13,080 Speaker 1: was was overburdened, health care was rationed and I and 100 00:07:13,120 --> 00:07:16,120 Speaker 1: I experienced and I saw all of this. But I 101 00:07:16,160 --> 00:07:21,080 Speaker 1: was also drawn by this opportunity to pursue a deeper 102 00:07:21,400 --> 00:07:25,160 Speaker 1: um scientific understanding of the human body, right the physiology 103 00:07:25,160 --> 00:07:29,760 Speaker 1: of the disease, why it occurs, UM and UM I 104 00:07:29,800 --> 00:07:32,680 Speaker 1: sort of really knew that there was disease outside of 105 00:07:32,760 --> 00:07:36,840 Speaker 1: just UM clinical care because of what I saw with 106 00:07:37,440 --> 00:07:43,000 Speaker 1: you know, lack of nutrition and hydration UM in in 107 00:07:43,720 --> 00:07:46,800 Speaker 1: growing up, and so as I entered I came here 108 00:07:47,280 --> 00:07:51,200 Speaker 1: UM actually for college after I finished high school UM I, 109 00:07:51,520 --> 00:07:56,760 Speaker 1: and when I entered medical school and residency and learn 110 00:07:56,800 --> 00:07:59,240 Speaker 1: more about determinants of health, it was sort of on 111 00:07:59,320 --> 00:08:04,400 Speaker 1: aha moment and UM in medical school, I went into 112 00:08:04,440 --> 00:08:07,640 Speaker 1: public health as well, so I did a double public 113 00:08:07,680 --> 00:08:12,840 Speaker 1: health medicine degree and UM MY and becoming a primary 114 00:08:12,880 --> 00:08:16,520 Speaker 1: care physician UM was what I wanted to do. And 115 00:08:16,560 --> 00:08:20,120 Speaker 1: I realized really, yes, I wanted to care for vulnerable 116 00:08:20,200 --> 00:08:24,920 Speaker 1: and socially disadvantage populations. I wanted to be more compassionate, 117 00:08:25,440 --> 00:08:30,880 Speaker 1: you know, listen and understanding and value UM their culture 118 00:08:31,040 --> 00:08:35,800 Speaker 1: and beliefs. UM. But but I also had that motivation 119 00:08:35,840 --> 00:08:39,120 Speaker 1: about changing the way medicine was always focused on clinical 120 00:08:39,160 --> 00:08:42,440 Speaker 1: care in the US, you know, seeing the sick chronically ill, 121 00:08:43,160 --> 00:08:48,720 Speaker 1: to focus on also addressing determinants and disparities and and 122 00:08:48,800 --> 00:08:55,040 Speaker 1: supporting interventions around what we know as social determinants, and 123 00:08:55,480 --> 00:08:59,760 Speaker 1: you know, both need to work together. So I had 124 00:09:00,120 --> 00:09:04,480 Speaker 1: do you just like UM Dr v i UM worked 125 00:09:04,520 --> 00:09:08,440 Speaker 1: in public health and also had the opportunity to work 126 00:09:08,480 --> 00:09:11,920 Speaker 1: at the National Institutes of Health the health care side. 127 00:09:11,960 --> 00:09:14,120 Speaker 1: Can you talk a bit about what it is IBM 128 00:09:14,200 --> 00:09:18,439 Speaker 1: is doing in that space, what are your teams actually pursuing. 129 00:09:19,000 --> 00:09:24,199 Speaker 1: Health is so foundational and essential to the value proposition 130 00:09:24,280 --> 00:09:27,000 Speaker 1: that I hope and I believe IBM has played and 131 00:09:27,040 --> 00:09:30,560 Speaker 1: will play during this crisis and beyond UM. If you 132 00:09:30,600 --> 00:09:37,360 Speaker 1: think about UM information, data, analytics, and and and the 133 00:09:37,400 --> 00:09:42,040 Speaker 1: opportunities around artificial intelligence and machine learning and analytics and 134 00:09:42,080 --> 00:09:48,400 Speaker 1: predictive analytics UM, there's such an important role to address 135 00:09:48,520 --> 00:09:55,079 Speaker 1: health UM for the what I would call the multiple 136 00:09:55,120 --> 00:09:59,400 Speaker 1: stakeholders in a health ecosystem. If you think about how 137 00:10:00,679 --> 00:10:05,240 Speaker 1: data and even care or money flows in healthcare, which 138 00:10:05,320 --> 00:10:07,719 Speaker 1: in the US represents one in five dollars and in 139 00:10:08,520 --> 00:10:11,920 Speaker 1: most developed countries one in ten dollars and in most 140 00:10:11,960 --> 00:10:16,920 Speaker 1: developing countries one and twenty. The the nature of health 141 00:10:16,960 --> 00:10:20,200 Speaker 1: and health care is that it typically you've got a patient, 142 00:10:20,280 --> 00:10:24,520 Speaker 1: a citizen, a consumer who comes in with a challenge 143 00:10:24,520 --> 00:10:30,280 Speaker 1: on an issue. UM. We know that of that spend 144 00:10:31,040 --> 00:10:33,520 Speaker 1: of that challenge in terms of costs is related to 145 00:10:33,640 --> 00:10:39,480 Speaker 1: chronic diseases like diabetes, like heart disease, like asthma, like COPD, 146 00:10:39,640 --> 00:10:44,320 Speaker 1: like depression, like arthritis, like cancer. And there is a 147 00:10:45,240 --> 00:10:49,480 Speaker 1: encounter that I that Irene had, you know, have the 148 00:10:49,520 --> 00:10:51,840 Speaker 1: good fortunate as physicians to take care of in that 149 00:10:51,960 --> 00:10:55,360 Speaker 1: privileged moment to take care of a patient you know, 150 00:10:56,080 --> 00:10:59,880 Speaker 1: and and and and offer support in those short you know, 151 00:11:00,040 --> 00:11:04,240 Speaker 1: five to ten minutes sometimes twenty minutes conversations and interactions, 152 00:11:04,360 --> 00:11:07,480 Speaker 1: and and that data flows in a certain way. It 153 00:11:07,520 --> 00:11:10,560 Speaker 1: flows to a pay er a health plan. It flows 154 00:11:10,880 --> 00:11:16,280 Speaker 1: to an employer who often is the one who pays 155 00:11:16,280 --> 00:11:20,840 Speaker 1: those bills for for their workforce and their family members. 156 00:11:21,520 --> 00:11:25,000 Speaker 1: It flows potentially to a farmer company as they think 157 00:11:25,000 --> 00:11:29,200 Speaker 1: about studies and trials um. It flows to a government 158 00:11:29,240 --> 00:11:31,320 Speaker 1: if you think about all the testing that's happening with 159 00:11:31,400 --> 00:11:35,640 Speaker 1: COVID nineteen now and and and the challenges of contact 160 00:11:35,679 --> 00:11:41,000 Speaker 1: tracing and treatment in isolation and quarantining. So there's an 161 00:11:41,040 --> 00:11:45,440 Speaker 1: ecosystem here as it relates to health and the impact 162 00:11:45,480 --> 00:11:50,120 Speaker 1: that health has on has on you know, people, communities, families, 163 00:11:50,240 --> 00:11:53,480 Speaker 1: and the role that data analytics and AI and the 164 00:11:53,520 --> 00:11:58,000 Speaker 1: expertise of people at IBM who you know, are experts 165 00:11:58,040 --> 00:12:01,360 Speaker 1: in data science, are expert into and computing, our experts 166 00:12:01,400 --> 00:12:05,760 Speaker 1: into you know, Watson and machine learning to bring those 167 00:12:05,800 --> 00:12:09,040 Speaker 1: worlds together of tech and health and healthcare. I mean, 168 00:12:09,040 --> 00:12:11,880 Speaker 1: what better endeavor than to try to improve the health 169 00:12:11,920 --> 00:12:17,360 Speaker 1: of populations. Fantastic And this kind of also brings us 170 00:12:17,400 --> 00:12:21,000 Speaker 1: to the discussion at hand for today. This is a 171 00:12:21,120 --> 00:12:25,800 Speaker 1: huge topic disparity in access to healthcare, to health services, 172 00:12:26,000 --> 00:12:30,040 Speaker 1: to health information, and that it has as of itself. 173 00:12:30,280 --> 00:12:32,440 Speaker 1: It's it's such an enormous thing and it has so 174 00:12:32,480 --> 00:12:36,880 Speaker 1: many facets. It's challenging to talk about because there are 175 00:12:36,920 --> 00:12:38,840 Speaker 1: so many different ways we could go at it. We 176 00:12:38,880 --> 00:12:45,640 Speaker 1: could look at it along aspects of socio economic levels, regions, 177 00:12:45,760 --> 00:12:49,520 Speaker 1: we could look at it by race, and it is 178 00:12:49,880 --> 00:12:53,680 Speaker 1: a complicated issue. Can you talk a little bit about 179 00:12:53,679 --> 00:12:58,640 Speaker 1: the overall concept of health disparity. We have a challenge 180 00:12:58,840 --> 00:13:03,480 Speaker 1: globally domad e stickally in communities all across this country 181 00:13:03,480 --> 00:13:06,120 Speaker 1: and all across the globe where there are members of 182 00:13:06,120 --> 00:13:10,920 Speaker 1: our family who are ill, who suffer disproportionately from illness 183 00:13:11,000 --> 00:13:15,400 Speaker 1: from those chronic diseases like diabetes, like heart disease, like cancer, 184 00:13:16,040 --> 00:13:20,080 Speaker 1: like asthma, like depression, And we have an opportunity and 185 00:13:20,200 --> 00:13:24,840 Speaker 1: responsibility in terms of our values to find ways to 186 00:13:24,960 --> 00:13:28,600 Speaker 1: bring those folks back to better health. And unfortunately, many 187 00:13:28,640 --> 00:13:33,040 Speaker 1: of the factors that represent how those family members are 188 00:13:33,080 --> 00:13:38,240 Speaker 1: ill are are are based on risk factors that are 189 00:13:38,280 --> 00:13:44,079 Speaker 1: associated with things like race or ethnicity, or sexual orientation 190 00:13:44,320 --> 00:13:49,479 Speaker 1: or socio economic status or you know, education or employment. 191 00:13:49,600 --> 00:13:53,440 Speaker 1: And so it's so essential for us to to to 192 00:13:53,559 --> 00:13:57,560 Speaker 1: think about this as a society, to think about the 193 00:13:57,640 --> 00:14:00,800 Speaker 1: values that we believe are essential for us to be 194 00:14:01,080 --> 00:14:04,320 Speaker 1: you know, you know, to support our family members, but 195 00:14:04,440 --> 00:14:08,800 Speaker 1: also you know, create a dynamic where we we we 196 00:14:08,880 --> 00:14:12,280 Speaker 1: we bring those those health disparity populations back up in 197 00:14:12,400 --> 00:14:15,840 Speaker 1: terms of health. So that's how I you know, simplified 198 00:14:15,960 --> 00:14:18,000 Speaker 1: or think about it in terms of health disparities. And 199 00:14:18,040 --> 00:14:22,760 Speaker 1: to me, equity represents that hope that all members of 200 00:14:22,800 --> 00:14:26,360 Speaker 1: our family are are healthy and how do we achieve that? Yes, 201 00:14:26,440 --> 00:14:31,240 Speaker 1: and I can add to UM the concept of health 202 00:14:31,320 --> 00:14:36,800 Speaker 1: disparities and and even share a story UM as an example. 203 00:14:38,400 --> 00:14:44,560 Speaker 1: So there are health differences and their health disparities, and 204 00:14:44,600 --> 00:14:48,160 Speaker 1: when we the health difference for example, a health difference 205 00:14:48,200 --> 00:14:56,640 Speaker 1: for example is UM the elderly population having more you know, diseases, 206 00:14:56,760 --> 00:15:00,320 Speaker 1: or morbidity than the younger population. Right, So that's that's 207 00:15:00,320 --> 00:15:05,560 Speaker 1: a health difference. And when we talk about health disparities, 208 00:15:06,160 --> 00:15:10,200 Speaker 1: we are referring to that particular type of health difference 209 00:15:11,040 --> 00:15:18,280 Speaker 1: that is linked with a social or economic or environmental disadvantage. 210 00:15:18,760 --> 00:15:22,520 Speaker 1: So UM, in terms of address and health disparities, we 211 00:15:23,000 --> 00:15:25,920 Speaker 1: try to understand the root causes of why they exist 212 00:15:26,000 --> 00:15:31,880 Speaker 1: because they are complex. UM. Disease and illness are complex, 213 00:15:32,080 --> 00:15:35,320 Speaker 1: not just from one factor or due to one single factor, 214 00:15:35,400 --> 00:15:41,080 Speaker 1: but due to multiple structural policy decisions that we make 215 00:15:41,120 --> 00:15:45,160 Speaker 1: as a society. UM. For example, having access to clean, 216 00:15:45,440 --> 00:15:50,040 Speaker 1: safe and healthy environment, having access to healthy food UM, 217 00:15:50,480 --> 00:15:56,840 Speaker 1: and overall UM not being breadened by everyday stresses. UM. 218 00:15:56,880 --> 00:16:00,360 Speaker 1: There's there's a lot of stress from being for low 219 00:16:00,440 --> 00:16:06,120 Speaker 1: income or unemployed UM. And so the stresses that are 220 00:16:06,320 --> 00:16:12,560 Speaker 1: experienced disproportionately as he was mentioning by people with social 221 00:16:12,600 --> 00:16:18,320 Speaker 1: disadvantage or by those who have experienced racism or discrimination 222 00:16:19,480 --> 00:16:24,840 Speaker 1: UM are all as impact health and and are seen 223 00:16:24,880 --> 00:16:29,840 Speaker 1: as health disparities. And so solutions for health disparities are 224 00:16:29,920 --> 00:16:36,760 Speaker 1: not always just medical or clinical UM. And I and 225 00:16:36,800 --> 00:16:39,400 Speaker 1: in terms of a story that I wanted to share, 226 00:16:39,920 --> 00:16:45,240 Speaker 1: UM sort of a close and personalities around racial disparities 227 00:16:45,520 --> 00:16:51,160 Speaker 1: in maternal UM and infant health or pre timbers or 228 00:16:51,200 --> 00:16:55,400 Speaker 1: infant mortality. I think, UM, you know, there's a lot 229 00:16:55,520 --> 00:17:00,680 Speaker 1: of literature and scientific evidence UM around the huge, huge 230 00:17:00,720 --> 00:17:06,040 Speaker 1: disparity GAS between African American UM blacks and white um 231 00:17:06,119 --> 00:17:10,920 Speaker 1: in in in maternal mortality, in infan mortality UM as 232 00:17:10,920 --> 00:17:14,960 Speaker 1: well as pre timbers UM or premature new natal birds. 233 00:17:16,160 --> 00:17:21,840 Speaker 1: There's ample evidence from studies that show that cettain maternal 234 00:17:22,000 --> 00:17:28,959 Speaker 1: risk factors UM that explained these disparities are mostly from 235 00:17:29,200 --> 00:17:35,680 Speaker 1: racism or racial disparities, and the experience of systemic racial bias, 236 00:17:36,000 --> 00:17:40,720 Speaker 1: not not just raise itself, can compromise health UM in 237 00:17:40,720 --> 00:17:46,560 Speaker 1: In Ghana, infant mortality was was high, I mean, and 238 00:17:46,600 --> 00:17:50,800 Speaker 1: it was seen mostly with women from loyal socio economic 239 00:17:50,880 --> 00:17:55,119 Speaker 1: status or regions with very very poor health care infrastructure. 240 00:17:55,880 --> 00:18:00,159 Speaker 1: But however, in the US, in the United States UM 241 00:18:01,119 --> 00:18:06,800 Speaker 1: in fun mortality or blacks, US blacks across all socio 242 00:18:06,840 --> 00:18:11,440 Speaker 1: economic status have poor maternal health outcomes and infant mortality 243 00:18:11,480 --> 00:18:16,480 Speaker 1: outcomes compared to their UM white counterparts, even in the 244 00:18:16,560 --> 00:18:19,960 Speaker 1: same socio economic status UM. And in fact, there's a 245 00:18:20,040 --> 00:18:24,359 Speaker 1: study that showed that Black woman with a higher education 246 00:18:24,640 --> 00:18:30,399 Speaker 1: like college level or graduate level degree have similar rates 247 00:18:30,440 --> 00:18:34,240 Speaker 1: compared to similar rates to whites who only have a 248 00:18:34,480 --> 00:18:39,040 Speaker 1: high school education in terms of UM in fun mortality. 249 00:18:39,760 --> 00:18:44,359 Speaker 1: So a lot of African American families, for example, I mean, 250 00:18:44,400 --> 00:18:49,160 Speaker 1: so this is all due to UM, some underlying systemic 251 00:18:49,880 --> 00:18:55,959 Speaker 1: UM or determinants or social determinants of health. UM. And 252 00:18:56,000 --> 00:19:00,919 Speaker 1: of course there's ongoing research to really tease out UM 253 00:19:01,160 --> 00:19:04,040 Speaker 1: why that is the case. And I myself, you know, 254 00:19:04,119 --> 00:19:08,520 Speaker 1: surprisingly experienced the high risk pregnancy. UM. I had a 255 00:19:08,600 --> 00:19:12,200 Speaker 1: pretember UM and I thought I was doing everything right. 256 00:19:12,320 --> 00:19:14,760 Speaker 1: I mean, for myself, I was eating well. I thought 257 00:19:14,800 --> 00:19:20,440 Speaker 1: being educated put me at least risk for any adverse outcomes. UM. 258 00:19:20,640 --> 00:19:24,520 Speaker 1: Deadly in Ghana, it would have so UM, you know, 259 00:19:24,680 --> 00:19:29,159 Speaker 1: being in the u United States environment, being in this 260 00:19:29,440 --> 00:19:32,360 Speaker 1: probably you know, I'm not saying, I'm not sure exactly 261 00:19:32,359 --> 00:19:34,600 Speaker 1: what calls it, but you know, I, as an example, 262 00:19:34,760 --> 00:19:38,399 Speaker 1: was quite surprised. Um. Of course everything has turned out fine. 263 00:19:38,640 --> 00:19:41,520 Speaker 1: My daughter is will turn eighteen at the end of 264 00:19:41,560 --> 00:19:45,120 Speaker 1: this month, and UM, she's off to college. So it's 265 00:19:46,359 --> 00:19:49,960 Speaker 1: think think everything works out, work down well. So that's 266 00:19:50,000 --> 00:19:53,040 Speaker 1: sort of my story that I wanted to share UM 267 00:19:53,119 --> 00:19:57,440 Speaker 1: that yes, you know, this is an example of the disparities. 268 00:19:57,840 --> 00:20:01,520 Speaker 1: And I believe it's safe to ay that the current 269 00:20:01,800 --> 00:20:07,200 Speaker 1: COVID nineteen crisis has really highlighted disparities in a lot 270 00:20:07,240 --> 00:20:11,880 Speaker 1: of ways. That we've seen this play out tragically with 271 00:20:12,080 --> 00:20:18,200 Speaker 1: the response to to COVID nineteen within certain communities. How 272 00:20:18,320 --> 00:20:25,800 Speaker 1: has COVID nineteen impacted or affected this issue? Oh goodness, UM, 273 00:20:26,080 --> 00:20:32,040 Speaker 1: COVID has definitely shaped our world. UM. It has exposed 274 00:20:32,160 --> 00:20:38,160 Speaker 1: huge inequalities and health UM in healthcare UM, the burden 275 00:20:38,200 --> 00:20:44,560 Speaker 1: of underlying disease and access outcomes UM from from COVID nineteen. 276 00:20:44,760 --> 00:20:47,600 Speaker 1: What we see and then hearing actually from the front 277 00:20:47,640 --> 00:20:54,440 Speaker 1: lines are UM glaring inequalities not only in the US, 278 00:20:54,560 --> 00:20:58,119 Speaker 1: but also globally. UM. In the United States, we're actually 279 00:20:58,160 --> 00:21:04,760 Speaker 1: seen the spread is from COVID across race as well race, ethnicity, 280 00:21:04,800 --> 00:21:11,000 Speaker 1: and geography, right, so mostly UM along socio economic lines. UM. 281 00:21:11,080 --> 00:21:15,120 Speaker 1: The disparities that we're seeing are in you know, testing 282 00:21:15,240 --> 00:21:21,040 Speaker 1: rates and infection, severe disease, illness, UM, hospitalization and even 283 00:21:21,119 --> 00:21:27,400 Speaker 1: I see you UM outcomes And so basically what we're 284 00:21:27,400 --> 00:21:32,480 Speaker 1: seeing is something really structural and systemic. In addition to 285 00:21:32,640 --> 00:21:38,160 Speaker 1: what we know are higher COOL mobilities in low sitio 286 00:21:38,160 --> 00:21:44,280 Speaker 1: economic status UM minorities as well as with with racio 287 00:21:44,320 --> 00:21:49,720 Speaker 1: ethnic minorities or people from communities of color. UM. We've 288 00:21:49,760 --> 00:21:53,520 Speaker 1: come to find out that it's also UM higher among 289 00:21:53,760 --> 00:22:01,199 Speaker 1: language minorities. So the COVID positive cases or infection UM 290 00:22:01,359 --> 00:22:05,040 Speaker 1: is not also only occurring along racial ethnic clients, but 291 00:22:06,000 --> 00:22:11,960 Speaker 1: UM also according to the we see that along the 292 00:22:12,040 --> 00:22:16,000 Speaker 1: impact of we've seen the impact of racial segregation, I 293 00:22:16,000 --> 00:22:22,040 Speaker 1: would say, UM, working class UM, the lack of home 294 00:22:22,080 --> 00:22:25,920 Speaker 1: ownership or wealth, and how people living in these communities 295 00:22:26,640 --> 00:22:31,240 Speaker 1: really are impacted by the labor market right UM. These 296 00:22:31,280 --> 00:22:34,040 Speaker 1: are we've seen individuals who really have to go to work, 297 00:22:34,200 --> 00:22:37,439 Speaker 1: you know that to pay their bills, leading to higher 298 00:22:37,440 --> 00:22:41,080 Speaker 1: exposure dr read What are some of the most challenging 299 00:22:41,480 --> 00:22:44,640 Speaker 1: racial and ethnic disparities in public health and health care 300 00:22:44,680 --> 00:22:48,240 Speaker 1: as you see it? One quick truth to recognize is 301 00:22:48,280 --> 00:22:50,840 Speaker 1: that health is so much more than health care. That 302 00:22:50,920 --> 00:22:53,959 Speaker 1: doesn't mean health care isn't essentially it is, but if 303 00:22:54,000 --> 00:22:57,919 Speaker 1: you think about the determinants that play a role in 304 00:22:58,000 --> 00:23:01,800 Speaker 1: health outcomes, it's almost you could think one, two, three, four, 305 00:23:04,600 --> 00:23:11,320 Speaker 1: which adds up to UM. The the broader recognition. When 306 00:23:11,359 --> 00:23:15,200 Speaker 1: you talk about disparities and inequities, you have to recognize 307 00:23:16,080 --> 00:23:20,600 Speaker 1: the ten percent which is clinical genomics and and and 308 00:23:20,760 --> 00:23:26,840 Speaker 1: some of that's connected to this this common history UM. 309 00:23:27,520 --> 00:23:30,159 Speaker 1: You know, family history of chronic diseases makes you a 310 00:23:30,280 --> 00:23:33,119 Speaker 1: higher risk of having a chronic disease, so UM. And 311 00:23:33,160 --> 00:23:39,679 Speaker 1: then the thirty social, environmental, and behavioral and so so 312 00:23:39,760 --> 00:23:42,679 Speaker 1: many of our communities are challenged where the statement that 313 00:23:42,760 --> 00:23:45,200 Speaker 1: your zip code is more important than your genetic code 314 00:23:46,400 --> 00:23:50,399 Speaker 1: is really true. Place matters, UM. You can in the 315 00:23:50,560 --> 00:23:54,960 Speaker 1: same we use this UM reference in in d C. 316 00:23:55,160 --> 00:23:58,840 Speaker 1: When I worked serving and underserved communities in DC. You know, 317 00:23:58,920 --> 00:24:01,480 Speaker 1: you could go on the metro and you can go 318 00:24:01,560 --> 00:24:05,480 Speaker 1: from one stop to another stop and literally span twenty 319 00:24:05,520 --> 00:24:10,000 Speaker 1: to thirty years of life expectancy. So I want to 320 00:24:10,600 --> 00:24:16,520 Speaker 1: highlight that. So when you look across almost every health condition, 321 00:24:16,560 --> 00:24:20,879 Speaker 1: there are disparities that exists, and there's opportunities for equity. 322 00:24:21,040 --> 00:24:25,080 Speaker 1: And it requires requires us to understand the determinants. It 323 00:24:25,119 --> 00:24:29,000 Speaker 1: requires us to bring in the right data and then 324 00:24:29,040 --> 00:24:33,080 Speaker 1: to influence the decision makers. So I'm on this three 325 00:24:33,160 --> 00:24:37,720 Speaker 1: D commission, we call it Determinants Data Decisions that's sponsored 326 00:24:37,720 --> 00:24:42,480 Speaker 1: by the Rockefeller Foundation and UM and the School Public 327 00:24:42,480 --> 00:24:45,800 Speaker 1: Health at BEU. And this is very important to think 328 00:24:45,840 --> 00:24:49,840 Speaker 1: about because we've we've got an opportunity now with COVID 329 00:24:50,760 --> 00:24:53,879 Speaker 1: and all the data and the determinants science we know 330 00:24:54,680 --> 00:24:59,960 Speaker 1: to influence decisions in the disparities that exist in public 331 00:25:00,040 --> 00:25:03,440 Speaker 1: health and healthcare. UM. COVID is making us more aware 332 00:25:03,480 --> 00:25:08,280 Speaker 1: of mental illness, their disparities there UM in depression and 333 00:25:08,400 --> 00:25:12,639 Speaker 1: anxiety disorders and psychotic disorders and addictions that exists that 334 00:25:12,680 --> 00:25:15,919 Speaker 1: need to be addressed and flatten those curves have to 335 00:25:15,920 --> 00:25:20,800 Speaker 1: be flattened as well, there are chronic disease curves that 336 00:25:20,840 --> 00:25:24,879 Speaker 1: need to be flattened. UM. That's worsening, especially for communities 337 00:25:24,880 --> 00:25:29,720 Speaker 1: of color and poverty. So you know, all the chronic 338 00:25:29,760 --> 00:25:32,639 Speaker 1: diseases of already referenced from diabetes, are heart disease to 339 00:25:32,760 --> 00:25:37,840 Speaker 1: asthma to SELPD, to depression, to arthritis to cancer. And 340 00:25:37,880 --> 00:25:41,200 Speaker 1: then the other curve that we have to really address 341 00:25:41,320 --> 00:25:44,879 Speaker 1: and and be really you know, open about, is the 342 00:25:45,000 --> 00:25:48,840 Speaker 1: curve of inequities and the role of structural racism and 343 00:25:48,880 --> 00:25:52,240 Speaker 1: discrimination in our in our systems, and how can we 344 00:25:52,280 --> 00:25:58,120 Speaker 1: address that UM and really invest in diversity and inclusion 345 00:25:58,680 --> 00:26:04,080 Speaker 1: and equity so UM. I mean, there's so much opportunity 346 00:26:04,160 --> 00:26:08,520 Speaker 1: here for us to take this moment with COVID nineteen 347 00:26:09,720 --> 00:26:13,320 Speaker 1: to be upfront about what we have, what we need 348 00:26:13,359 --> 00:26:17,960 Speaker 1: to do, and what we want to build in terms 349 00:26:18,000 --> 00:26:20,119 Speaker 1: of the health system of the future. It sounds like 350 00:26:20,160 --> 00:26:23,120 Speaker 1: there's a great deal of work to be done. I'm 351 00:26:23,240 --> 00:26:26,280 Speaker 1: very curious to learn more about what it is that 352 00:26:26,440 --> 00:26:30,120 Speaker 1: IBM and more specifically what IBM what's in health are 353 00:26:30,200 --> 00:26:34,679 Speaker 1: doing in an effort to try and address these challenges. 354 00:26:34,720 --> 00:26:39,199 Speaker 1: I mean, clearly, this is something bigger than what is 355 00:26:39,200 --> 00:26:42,280 Speaker 1: going to take you know, a tech solution. Dr you 356 00:26:42,359 --> 00:26:44,879 Speaker 1: mentioned that earlier, it's going to require a lot of 357 00:26:44,920 --> 00:26:49,120 Speaker 1: different work. But what is IBMS peace in this What 358 00:26:49,160 --> 00:26:52,320 Speaker 1: are what are you guys doing in your efforts to 359 00:26:52,480 --> 00:26:58,359 Speaker 1: kind of address the issue of health disparity. So we 360 00:26:58,560 --> 00:27:04,679 Speaker 1: see r ole by leveraging technology, but ultimately by by 361 00:27:04,880 --> 00:27:08,800 Speaker 1: looking at trust. I think so much of health and 362 00:27:08,840 --> 00:27:13,640 Speaker 1: healthcare is still foundationally about relationships and trust, and so 363 00:27:13,720 --> 00:27:17,280 Speaker 1: as you think about a journey with IBM, it is 364 00:27:18,240 --> 00:27:27,639 Speaker 1: working with life science companies, hospitals, health systems, governments, um 365 00:27:27,800 --> 00:27:33,000 Speaker 1: and employers and businesses and health plans in a partnership 366 00:27:33,359 --> 00:27:36,919 Speaker 1: with what I would call shared expertise. You know, some 367 00:27:37,000 --> 00:27:40,760 Speaker 1: of the brightest minds and the smartest minds who are 368 00:27:40,840 --> 00:27:44,280 Speaker 1: very global and very diverse across the globe in data 369 00:27:44,359 --> 00:27:48,040 Speaker 1: science and AI and health and healthcare, like like we've 370 00:27:48,040 --> 00:27:51,280 Speaker 1: got with Dr Danko Mullen and and others on our 371 00:27:51,359 --> 00:27:55,560 Speaker 1: our team to work with those other partners and clients 372 00:27:56,240 --> 00:28:02,240 Speaker 1: and to evolve that shared expertise into conversations about data. 373 00:28:02,520 --> 00:28:05,280 Speaker 1: How do we connect these unique data sets, How do 374 00:28:05,359 --> 00:28:08,480 Speaker 1: we protect these data sets because so much about data 375 00:28:08,520 --> 00:28:12,439 Speaker 1: is about trust UM, And then how do we bring 376 00:28:12,480 --> 00:28:16,720 Speaker 1: them together and apply analytics and artificial intelligence and advanced 377 00:28:16,720 --> 00:28:22,639 Speaker 1: analytics to bring insights that better predict, that better personalized, 378 00:28:23,480 --> 00:28:29,080 Speaker 1: and that better prevents bad outcomes and promote good outcomes. 379 00:28:29,160 --> 00:28:33,240 Speaker 1: And so, you know, we're very proud of the work 380 00:28:33,240 --> 00:28:37,119 Speaker 1: we've done with so many different clients to deliver that 381 00:28:37,280 --> 00:28:43,960 Speaker 1: value and that shared expertise UM and those insights from 382 00:28:44,000 --> 00:28:47,800 Speaker 1: the data, analytics and AI. Dr don Komlin, I have 383 00:28:47,880 --> 00:28:51,200 Speaker 1: a question for you about data and analytics. I mean, 384 00:28:51,240 --> 00:28:55,480 Speaker 1: we we know that data is important, but obviously data 385 00:28:55,680 --> 00:28:59,160 Speaker 1: doesn't matter so much unless you're able to do something 386 00:28:59,520 --> 00:29:04,920 Speaker 1: actionable with it. So, how can organizations actually apply data 387 00:29:05,040 --> 00:29:09,520 Speaker 1: and analytics to make better decisions or to create better 388 00:29:09,560 --> 00:29:15,880 Speaker 1: outcomes for themselves? What are some actual processes that you 389 00:29:16,000 --> 00:29:23,720 Speaker 1: look at. Data is actually a very powerful tool UM. 390 00:29:23,760 --> 00:29:29,080 Speaker 1: I've heard someone saying how data is actually a lifeline. UM. 391 00:29:29,200 --> 00:29:37,959 Speaker 1: It's it's very important. And so organizations can definitely you know, 392 00:29:38,080 --> 00:29:44,880 Speaker 1: incorporated health equity UM lens when applying data and analytics, 393 00:29:45,680 --> 00:29:51,880 Speaker 1: especially for decision making UM, in order to really see 394 00:29:51,960 --> 00:29:57,959 Speaker 1: improved outcomes or or better outcomes UM. And they of 395 00:29:58,000 --> 00:30:01,680 Speaker 1: course they also need to ensure that while using the 396 00:30:01,800 --> 00:30:08,160 Speaker 1: data that we are addressing any transparency UM bias as 397 00:30:08,160 --> 00:30:12,040 Speaker 1: well as UM ethical issues that are usually at the 398 00:30:12,120 --> 00:30:17,080 Speaker 1: core of UM data use. UH. You know, in terms 399 00:30:17,160 --> 00:30:20,520 Speaker 1: of our even the current strategy to address COVID response 400 00:30:21,120 --> 00:30:26,200 Speaker 1: M recovery and even preparedness for for a potential wave 401 00:30:26,320 --> 00:30:30,600 Speaker 1: or increase in cases, we really need sort of a 402 00:30:30,640 --> 00:30:36,760 Speaker 1: considered coordinated effort using accurate data or complete data UM 403 00:30:37,040 --> 00:30:43,920 Speaker 1: that that is informed by UM health equity and integrated 404 00:30:43,960 --> 00:30:46,480 Speaker 1: into all of our all of our policies, all of 405 00:30:46,520 --> 00:30:52,560 Speaker 1: our interventions UM in scientific evidence. So I would basically 406 00:30:53,480 --> 00:30:59,600 Speaker 1: UM say that the use of data for and technology 407 00:30:59,680 --> 00:31:05,640 Speaker 1: for we're feel good. UM. It's what organizations UM need 408 00:31:06,280 --> 00:31:10,600 Speaker 1: and so that we can make better informed decisions and 409 00:31:11,080 --> 00:31:14,840 Speaker 1: produce better outcomes as well as at risk of health 410 00:31:14,840 --> 00:31:19,440 Speaker 1: disparity scale. We've talked a lot about what IBM is doing, 411 00:31:19,720 --> 00:31:21,720 Speaker 1: and I know there are a lot of people out there, 412 00:31:22,040 --> 00:31:26,400 Speaker 1: whether they are currently having issues accessing health services, maybe 413 00:31:26,400 --> 00:31:29,640 Speaker 1: they've been affected by disparity. Do you have any advice 414 00:31:29,680 --> 00:31:33,160 Speaker 1: for people who want to work to eliminate health disparities? 415 00:31:33,280 --> 00:31:36,120 Speaker 1: What can the average person do that can be helpful. 416 00:31:36,560 --> 00:31:39,960 Speaker 1: It starts with your people, UM, as I was saying, 417 00:31:39,960 --> 00:31:45,280 Speaker 1: and the diversity that you need to respect amongst your people. UM. 418 00:31:45,320 --> 00:31:47,920 Speaker 1: It also then goes to what I would call the 419 00:31:48,040 --> 00:31:51,400 Speaker 1: data and the nature in which you collect data, how 420 00:31:51,440 --> 00:31:54,640 Speaker 1: you build trust, and the diversity of your data sets 421 00:31:54,640 --> 00:31:58,120 Speaker 1: and the transparency you have about your data, but also 422 00:31:58,200 --> 00:32:01,240 Speaker 1: the protections you have, the privacy protections because as I 423 00:32:01,280 --> 00:32:05,680 Speaker 1: said earlier, you can't you can't you can't trust data 424 00:32:06,440 --> 00:32:08,760 Speaker 1: or share data. You don't share data with people you 425 00:32:08,800 --> 00:32:11,600 Speaker 1: don't trust. So that's a very key piece I think 426 00:32:11,600 --> 00:32:14,719 Speaker 1: in this this clash between the culture of tech and 427 00:32:14,760 --> 00:32:18,320 Speaker 1: healthcare and public health. You need companies that you can 428 00:32:18,360 --> 00:32:22,080 Speaker 1: trust who will protect and secure that data, and that 429 00:32:22,120 --> 00:32:27,040 Speaker 1: our values based. It then is about analytics, and we 430 00:32:27,120 --> 00:32:31,240 Speaker 1: were very proud to be doing analytics that support what 431 00:32:31,360 --> 00:32:34,680 Speaker 1: I would call this concept of equity dashboards, where people 432 00:32:34,720 --> 00:32:39,640 Speaker 1: can see the disparities that exist in the populations they serve, 433 00:32:39,680 --> 00:32:42,239 Speaker 1: whether you're a hospital, whether you're an employer, whether you're 434 00:32:42,280 --> 00:32:45,040 Speaker 1: a health plan, whether you're a government UM. And then 435 00:32:45,160 --> 00:32:49,080 Speaker 1: the last piece is AI, and I think ethical, transparent, 436 00:32:49,680 --> 00:32:53,360 Speaker 1: and equitable AI is going to be so essential. Many 437 00:32:53,400 --> 00:32:55,720 Speaker 1: companies want to create what I would call a black 438 00:32:55,800 --> 00:33:00,600 Speaker 1: box for AI, and you know, you know, we believe 439 00:33:00,680 --> 00:33:03,880 Speaker 1: that you need transparency. UM. You need to know who 440 00:33:03,960 --> 00:33:08,600 Speaker 1: trains these AI systems because in many ways, the biases 441 00:33:08,680 --> 00:33:12,360 Speaker 1: that they may have might be continued or extrapolated if 442 00:33:12,400 --> 00:33:15,000 Speaker 1: you're not transparent. And people need to know how they're trained, 443 00:33:15,040 --> 00:33:16,920 Speaker 1: they need to know the data sets they're trained on, 444 00:33:17,640 --> 00:33:21,560 Speaker 1: and they need to recognize the limitations of AI. And 445 00:33:21,680 --> 00:33:26,280 Speaker 1: we've always suggested that the value prop is not humans 446 00:33:26,680 --> 00:33:32,000 Speaker 1: or AI, it's humans plus AI to make better decisions. 447 00:33:32,640 --> 00:33:35,800 Speaker 1: And a part of making those better decisions is to 448 00:33:35,840 --> 00:33:40,560 Speaker 1: reduce the role of bias UH in those decisions by 449 00:33:40,560 --> 00:33:44,200 Speaker 1: by taking advantage the best of technology and the best 450 00:33:44,240 --> 00:33:47,680 Speaker 1: of human expertise together. Do you have advice for people 451 00:33:47,800 --> 00:33:51,600 Speaker 1: who want to work to eliminate health disparities? What can 452 00:33:51,640 --> 00:33:54,960 Speaker 1: the average person do that can be helpful. What I 453 00:33:55,000 --> 00:33:58,720 Speaker 1: love about IBM as a company and in our role 454 00:33:58,760 --> 00:34:01,760 Speaker 1: we have in society is we can catalyze these conversations 455 00:34:01,800 --> 00:34:05,040 Speaker 1: as we're doing today. And so there's so much anyone 456 00:34:05,080 --> 00:34:10,439 Speaker 1: can do to address health disparities and health inequities. Number One, 457 00:34:11,320 --> 00:34:15,359 Speaker 1: you you educate yourself, You make yourself aware UM as 458 00:34:15,400 --> 00:34:20,319 Speaker 1: it relates to the challenges as relates to you know, 459 00:34:20,360 --> 00:34:24,960 Speaker 1: the members of a community that are are facing these 460 00:34:24,960 --> 00:34:28,080 Speaker 1: disparities and and and and to me, I'm a big 461 00:34:28,120 --> 00:34:31,879 Speaker 1: believer in we uh, you know, and how we think 462 00:34:31,920 --> 00:34:36,320 Speaker 1: about our society and and and UM data brings people 463 00:34:36,360 --> 00:34:42,000 Speaker 1: together in many ways. UM and and analytics and and 464 00:34:42,200 --> 00:34:44,440 Speaker 1: companies like IBM can play an important role in that. 465 00:34:44,560 --> 00:34:48,520 Speaker 1: So think about, you know, educating yourself about these disparities 466 00:34:48,560 --> 00:34:53,480 Speaker 1: that exist, learn about them, and think about how you 467 00:34:53,560 --> 00:34:57,239 Speaker 1: can bring attention to that UM and and and more 468 00:34:57,320 --> 00:35:01,880 Speaker 1: knowledge and awareness. UH. A lot of this starts with 469 00:35:01,880 --> 00:35:04,799 Speaker 1: with what I call data and trust. It's it's it's 470 00:35:04,800 --> 00:35:08,800 Speaker 1: this idea. If you think about part of this journey 471 00:35:08,840 --> 00:35:12,000 Speaker 1: starts with how the data is collected. When you're in 472 00:35:12,400 --> 00:35:17,040 Speaker 1: UM a hospital, or you're you're an employer, and where 473 00:35:17,040 --> 00:35:19,920 Speaker 1: you're in the census, and you you share data about 474 00:35:19,920 --> 00:35:25,360 Speaker 1: yourself and and you're transparent about maybe you're limited English proficiency, 475 00:35:25,400 --> 00:35:28,520 Speaker 1: or you're transparent about your race, ethnicity, or your country 476 00:35:28,520 --> 00:35:33,080 Speaker 1: of origin. You're transparent about you know, topics like sexual orientation. 477 00:35:33,800 --> 00:35:37,200 Speaker 1: These these factors play a very important role in the 478 00:35:37,280 --> 00:35:41,520 Speaker 1: future of reducing those disparities. If the data isn't collected 479 00:35:41,560 --> 00:35:46,000 Speaker 1: accurately and then the disparities aren't identified, and then you 480 00:35:46,040 --> 00:35:50,080 Speaker 1: can't close those gaps. So there's a big conversation about 481 00:35:50,120 --> 00:35:53,440 Speaker 1: trust and how you how data is shared and how 482 00:35:53,480 --> 00:35:57,120 Speaker 1: it's used. And you should be comfortable asking those questions 483 00:35:57,120 --> 00:35:59,840 Speaker 1: like what is this data for and how's it being used, 484 00:35:59,840 --> 00:36:03,800 Speaker 1: but but challenging that you know, hopefully that you're willing 485 00:36:03,840 --> 00:36:07,839 Speaker 1: to share that data. UM. In my view, whenever we 486 00:36:07,880 --> 00:36:12,239 Speaker 1: analyze anything, I mean, you should look at disparities. You 487 00:36:12,239 --> 00:36:15,280 Speaker 1: should look at factors of race and socio economic status. 488 00:36:15,760 --> 00:36:18,399 Speaker 1: When you're running reports, when you're doing whatever you do, 489 00:36:18,920 --> 00:36:22,440 Speaker 1: you know, ask that question. You know, we know, for example, 490 00:36:22,480 --> 00:36:26,040 Speaker 1: blacks make fifty nine cents you know, compared to whites 491 00:36:26,080 --> 00:36:28,360 Speaker 1: in terms of income. We know that in terms of wealth, 492 00:36:28,400 --> 00:36:31,000 Speaker 1: they make ten cents for every dollar of wealth that 493 00:36:31,080 --> 00:36:34,759 Speaker 1: a person who's white makes. You know, we I know 494 00:36:34,960 --> 00:36:38,279 Speaker 1: my daughters, you know, when they grow up there, you 495 00:36:38,320 --> 00:36:41,200 Speaker 1: know right now they're competing in an environment where they'll 496 00:36:41,239 --> 00:36:43,839 Speaker 1: make seventy cents on the dollar that a man will make. 497 00:36:43,920 --> 00:36:48,400 Speaker 1: So if you don't include equity in your analytics, and 498 00:36:48,440 --> 00:36:51,200 Speaker 1: you don't include these factors, then in some ways you 499 00:36:51,320 --> 00:36:54,120 Speaker 1: you you're oblivious to the problem or the gaps that 500 00:36:54,200 --> 00:36:57,640 Speaker 1: you want to reduce. So we should ask and demand 501 00:36:57,760 --> 00:37:01,160 Speaker 1: to have those measures, you know, analyze and tracked, and 502 00:37:01,560 --> 00:37:03,840 Speaker 1: then ask questions about the root cause and how do 503 00:37:03,880 --> 00:37:08,200 Speaker 1: we reduce those gaps. Um I also believe so much 504 00:37:08,200 --> 00:37:11,520 Speaker 1: in the diversity of people. Like think about your own team, 505 00:37:11,560 --> 00:37:14,520 Speaker 1: think about the people you interact with, Think about how 506 00:37:14,560 --> 00:37:17,440 Speaker 1: you recruit your people, how you retain people. You know, 507 00:37:18,120 --> 00:37:22,600 Speaker 1: how are you thinking about diversity? Um in in in 508 00:37:22,600 --> 00:37:26,000 Speaker 1: your in your processes of of who you listen to 509 00:37:26,160 --> 00:37:29,520 Speaker 1: and who you recruit, who you retain. You know, I'm 510 00:37:29,520 --> 00:37:32,319 Speaker 1: a big believer in diversity and we're very proud at 511 00:37:32,320 --> 00:37:36,360 Speaker 1: IBM for was it twenty seven years being leader in 512 00:37:36,440 --> 00:37:39,120 Speaker 1: patents in the US? I mean twenty seven years in 513 00:37:39,120 --> 00:37:42,239 Speaker 1: a row being number one in patents and I am 514 00:37:42,280 --> 00:37:45,719 Speaker 1: a strong believer. A big source of that ability to 515 00:37:45,800 --> 00:37:49,360 Speaker 1: be leader in patents and that type of creativity is diversity. 516 00:37:49,520 --> 00:37:52,480 Speaker 1: And so many studies have shown that diversity breeds innovation. 517 00:37:53,160 --> 00:37:56,040 Speaker 1: And there's an r O I attached to being diverse. 518 00:37:56,840 --> 00:37:59,640 Speaker 1: So I would ask each of you to challenge yourself 519 00:37:59,719 --> 00:38:03,000 Speaker 1: to to think about the community and the people you're 520 00:38:03,040 --> 00:38:05,799 Speaker 1: with and how do you embrace diversity and whatever you do. 521 00:38:06,160 --> 00:38:09,600 Speaker 1: Dr Dougua Mullen, would you like to chime in about 522 00:38:09,600 --> 00:38:13,880 Speaker 1: this about what the average person can do to address disparity. 523 00:38:14,480 --> 00:38:19,920 Speaker 1: Health disparities are costly. I'm not sure if everyone realizes 524 00:38:20,360 --> 00:38:25,760 Speaker 1: UM and the and it's caused by a lot of 525 00:38:26,080 --> 00:38:32,200 Speaker 1: you know, determinants of healthy UM. It's also caused by 526 00:38:32,560 --> 00:38:38,400 Speaker 1: the fact that data UM that we are promoting or 527 00:38:38,440 --> 00:38:44,000 Speaker 1: working on is sometimes not complete or not accurate UM. 528 00:38:44,120 --> 00:38:48,279 Speaker 1: So in terms of you know, companies or individuals, I 529 00:38:48,320 --> 00:38:53,480 Speaker 1: think we should awareness is it's key and helping to 530 00:38:53,680 --> 00:39:00,080 Speaker 1: drive and address inequalities by UM, you know, promoting and 531 00:39:00,239 --> 00:39:06,760 Speaker 1: having a health equity lens and promoting UM data and 532 00:39:06,760 --> 00:39:11,560 Speaker 1: and analytics or artificial intelligence for for social good or 533 00:39:11,719 --> 00:39:15,960 Speaker 1: ensuring that we're all building technologies or working on solutions 534 00:39:16,040 --> 00:39:20,400 Speaker 1: that voll ensure the benefits for everyone and that unfairly 535 00:39:20,480 --> 00:39:25,960 Speaker 1: disadvantage other populations would help UM. And and we've seen 536 00:39:26,080 --> 00:39:31,040 Speaker 1: that this pandemic has actually resulted in you know, a 537 00:39:31,160 --> 00:39:35,440 Speaker 1: recession and a loss of economic livelihoods UM for a 538 00:39:35,520 --> 00:39:40,320 Speaker 1: lot of people. And so what we really need to 539 00:39:40,719 --> 00:39:47,200 Speaker 1: also look at is sporting UM programs, building up these 540 00:39:47,239 --> 00:39:54,000 Speaker 1: programs and interventional efforts that would really improve the economic 541 00:39:54,080 --> 00:40:01,160 Speaker 1: resiliency UM or or social capital, especially for despreading populations UM, 542 00:40:01,400 --> 00:40:05,000 Speaker 1: marginalized or under resource communities that have been really hard 543 00:40:05,120 --> 00:40:09,680 Speaker 1: hips UM. You know, it's hard to imagine how we 544 00:40:09,680 --> 00:40:12,520 Speaker 1: we could recover. I mean, there's been a number of 545 00:40:12,560 --> 00:40:15,360 Speaker 1: communities that have been left behind, for example, by the 546 00:40:15,480 --> 00:40:22,360 Speaker 1: digital UM and AI UM revolution, and so really helping 547 00:40:22,400 --> 00:40:28,279 Speaker 1: I mean with resources or UM technologies can help. I mean, 548 00:40:28,360 --> 00:40:34,799 Speaker 1: for example, schools education is a really strong determinant of 549 00:40:34,880 --> 00:40:41,120 Speaker 1: health and school education and cater UM these technologies for 550 00:40:41,640 --> 00:40:45,719 Speaker 1: black and brown communities that are in you know, socially 551 00:40:45,760 --> 00:40:52,239 Speaker 1: disadvantage communities may not always have to uh technology or computers. UM. 552 00:40:53,200 --> 00:40:58,080 Speaker 1: And so assisting with that, and this displays a larger 553 00:40:58,200 --> 00:41:01,839 Speaker 1: role in addressing, UM, a larger role in sort of 554 00:41:01,960 --> 00:41:07,560 Speaker 1: understanding these systemic or structural inequities. UM. Until helping with 555 00:41:07,640 --> 00:41:13,799 Speaker 1: that is really key. UM. I think as individuals. I mean, 556 00:41:13,840 --> 00:41:20,680 Speaker 1: I'm really pleased that we are having this conversation UM 557 00:41:20,719 --> 00:41:26,239 Speaker 1: around UM equity. Uh. You know, health disporities have asisted 558 00:41:26,400 --> 00:41:29,160 Speaker 1: for really long in the United States, UM, I mean 559 00:41:29,400 --> 00:41:33,759 Speaker 1: around the world as well. But I am I'm sort 560 00:41:33,760 --> 00:41:37,400 Speaker 1: of pleased with a with a conversation that's ongoing. So 561 00:41:38,040 --> 00:41:41,319 Speaker 1: if health disporities have existed for long, and we know 562 00:41:41,560 --> 00:41:46,680 Speaker 1: some of the root causes and promising interventions, we need 563 00:41:46,719 --> 00:41:49,719 Speaker 1: to ask yourself, you know, what are we contributing to 564 00:41:50,560 --> 00:41:54,239 Speaker 1: that legacy or the science right now? UM. You know 565 00:41:54,320 --> 00:41:56,640 Speaker 1: in the past, we would often say, oh, nothing can 566 00:41:56,680 --> 00:42:01,160 Speaker 1: be done about it, UM, you know, and what could 567 00:42:01,160 --> 00:42:04,279 Speaker 1: we do? And it's and so I think we're at 568 00:42:04,360 --> 00:42:07,080 Speaker 1: the right time in our history where a lot of 569 00:42:07,080 --> 00:42:12,080 Speaker 1: people are now caring more about inequalities, about racial justice. UM, 570 00:42:12,280 --> 00:42:16,120 Speaker 1: We're beginning to really address these difficult things and and 571 00:42:16,239 --> 00:42:21,960 Speaker 1: just talking about it, you know, UM, asking questions and 572 00:42:22,040 --> 00:42:25,560 Speaker 1: having that dialogue is is a great start. I mean 573 00:42:25,600 --> 00:42:30,880 Speaker 1: I'm inspired by uh, you know, the current movement Black 574 00:42:30,920 --> 00:42:37,560 Speaker 1: Lives Matter UM and and I think that that helps basically, 575 00:42:37,600 --> 00:42:40,000 Speaker 1: I mean, where people can start talking about the people 576 00:42:40,040 --> 00:42:42,879 Speaker 1: can look at you know, how can you we can 577 00:42:42,960 --> 00:42:49,480 Speaker 1: leverage our skills, UM and expertise and for the benefit 578 00:42:49,560 --> 00:42:53,040 Speaker 1: of everyone. I mean, I think playing all of us 579 00:42:53,120 --> 00:42:57,960 Speaker 1: playing our part in leveraging our skills and looking um 580 00:42:58,000 --> 00:43:03,160 Speaker 1: beyond competition, collaborating in our own space for the benefit 581 00:43:03,239 --> 00:43:06,520 Speaker 1: of everyone. Help. I could not agree more. I mean, 582 00:43:06,719 --> 00:43:11,160 Speaker 1: I feel that we are entering into a time where 583 00:43:11,280 --> 00:43:17,120 Speaker 1: more and more people are either realizing the realities that 584 00:43:17,719 --> 00:43:21,240 Speaker 1: have been in place forever but or or effectively forever 585 00:43:21,280 --> 00:43:25,600 Speaker 1: for all of our lifetimes. But maybe they weren't aware 586 00:43:25,640 --> 00:43:29,160 Speaker 1: of them because they'd never directly experienced them, or they're 587 00:43:29,200 --> 00:43:32,600 Speaker 1: they're acknowledging them. Perhaps they were at least subconsciously aware 588 00:43:32,960 --> 00:43:36,080 Speaker 1: but had not truly reflected upon it. We're starting to 589 00:43:36,120 --> 00:43:40,399 Speaker 1: see that change. I am also, like you, inspired by that, 590 00:43:40,560 --> 00:43:43,799 Speaker 1: and I am determined to do whatever I can in 591 00:43:43,880 --> 00:43:47,200 Speaker 1: my role as a voice of the media to continue 592 00:43:47,239 --> 00:43:50,120 Speaker 1: that conversation and to carry it forward, to make sure 593 00:43:50,160 --> 00:43:53,239 Speaker 1: people are still talking about this and thinking about this 594 00:43:53,360 --> 00:43:56,920 Speaker 1: and thinking about the aspects of the challenges that they 595 00:43:57,000 --> 00:44:00,799 Speaker 1: can rise to meet, and and why other ways we 596 00:44:00,840 --> 00:44:07,560 Speaker 1: can look to help others and to really, through helping others, 597 00:44:07,560 --> 00:44:11,160 Speaker 1: help everyone. I was wondering, Dr don Quamalen, if there 598 00:44:11,160 --> 00:44:15,000 Speaker 1: were any stories you could share about the work that 599 00:44:15,120 --> 00:44:18,760 Speaker 1: your team has done during the pandemic that you you're 600 00:44:18,760 --> 00:44:24,440 Speaker 1: particularly proud of. IBM has developed there's a website aimed 601 00:44:24,520 --> 00:44:28,959 Speaker 1: specifically for all COVID nineteen researchers and so it allows 602 00:44:30,040 --> 00:44:34,839 Speaker 1: users to upload information from electronic medical records or from 603 00:44:34,920 --> 00:44:40,000 Speaker 1: draft trials, UM or other sources and use these algorithms 604 00:44:40,040 --> 00:44:44,200 Speaker 1: to uncover new findings. Um AND and the side to 605 00:44:44,280 --> 00:44:46,760 Speaker 1: set up so that users can also keep their data 606 00:44:46,840 --> 00:44:50,960 Speaker 1: private um AND AND or share it as long as 607 00:44:51,160 --> 00:44:54,640 Speaker 1: UM they have privacy laws in place. But but I 608 00:44:54,719 --> 00:44:58,600 Speaker 1: hope is that the site that has been developed allows 609 00:44:58,680 --> 00:45:03,120 Speaker 1: researchers a around the world too collaborate and gain a 610 00:45:03,120 --> 00:45:06,640 Speaker 1: better insight into the understanding of the virus and how 611 00:45:07,360 --> 00:45:13,440 Speaker 1: it's um how it reacts in different populations UM and 612 00:45:14,920 --> 00:45:18,960 Speaker 1: you know, apart from the disparities that we're seeing UM 613 00:45:19,160 --> 00:45:24,759 Speaker 1: and and really have precise UM treatment. I mean, so 614 00:45:24,880 --> 00:45:30,000 Speaker 1: this that there is that there's also UM. The clinical 615 00:45:30,040 --> 00:45:34,920 Speaker 1: development journey is really extensive UM. But we are looking 616 00:45:34,960 --> 00:45:40,840 Speaker 1: at ways in which we can accelerate UM research and 617 00:45:41,120 --> 00:45:45,360 Speaker 1: using the cloud based technology at IBM and and and 618 00:45:45,520 --> 00:45:52,200 Speaker 1: streamlining data collection UM or its integration or standardization, especially 619 00:45:52,239 --> 00:45:56,600 Speaker 1: through the clinical trial process and vaccine development. We want 620 00:45:56,640 --> 00:46:00,480 Speaker 1: to make sure that it's inclusive and everyone UM and 621 00:46:00,520 --> 00:46:05,200 Speaker 1: it's safe for all populations UM. So those are some 622 00:46:05,320 --> 00:46:10,120 Speaker 1: of the ongoing work UM AND and some of it 623 00:46:10,400 --> 00:46:13,680 Speaker 1: that we hope to publish soon. But I would say, 624 00:46:14,040 --> 00:46:19,439 Speaker 1: you know, this collaboration, this website UM specifically for researchers 625 00:46:19,520 --> 00:46:24,279 Speaker 1: and especially to conduct equity work and look at electronic 626 00:46:24,320 --> 00:46:29,840 Speaker 1: health records UM and data has been UM really helpful. 627 00:46:30,640 --> 00:46:34,440 Speaker 1: So I find that extremely inspiring and I love learning 628 00:46:34,440 --> 00:46:37,440 Speaker 1: more about this because when we hear about on the news, 629 00:46:37,920 --> 00:46:41,480 Speaker 1: we typically hear about things in the context of doctors 630 00:46:41,560 --> 00:46:45,520 Speaker 1: are working on this, scientists are studying it, but it 631 00:46:45,560 --> 00:46:48,200 Speaker 1: tends to be at that level and we don't get 632 00:46:48,200 --> 00:46:52,600 Speaker 1: a deeper appreciation for what that actually means, what is 633 00:46:52,680 --> 00:46:57,760 Speaker 1: going into that process. What does it mean to analyze 634 00:46:58,360 --> 00:47:02,680 Speaker 1: the effects of COVID nineteen, or potential treatments for COVID nineteen, 635 00:47:03,320 --> 00:47:06,839 Speaker 1: or looking at even the spread of COVID nineteen and 636 00:47:06,880 --> 00:47:10,760 Speaker 1: how it spreads. So learning a little bit more about 637 00:47:10,800 --> 00:47:14,000 Speaker 1: that gives me a much deeper appreciation for the work 638 00:47:14,120 --> 00:47:17,440 Speaker 1: that that you and others that IBM are doing in 639 00:47:17,520 --> 00:47:22,440 Speaker 1: an effort to really address not just the COVID nineteen crisis, 640 00:47:22,560 --> 00:47:29,160 Speaker 1: but the overall challenge of providing health services, making sure 641 00:47:29,239 --> 00:47:33,200 Speaker 1: that you help others to provide health services to address 642 00:47:33,320 --> 00:47:37,520 Speaker 1: the issues of health disparity. One thing that became clear 643 00:47:37,640 --> 00:47:41,000 Speaker 1: in my conversation with doctors Re and don Qua Mullen 644 00:47:41,280 --> 00:47:45,000 Speaker 1: is that we can't really begin to address a problem 645 00:47:45,120 --> 00:47:50,640 Speaker 1: like disparity in health care access without data. To form solutions, 646 00:47:50,920 --> 00:47:55,760 Speaker 1: we first have to really understand the problem. Without that step, 647 00:47:56,160 --> 00:47:59,759 Speaker 1: any solution we attempt is bound to be insufficient to 648 00:47:59,840 --> 00:48:02,960 Speaker 1: me in our needs. It is therefore critical to have 649 00:48:03,080 --> 00:48:07,440 Speaker 1: sophisticated systems in place to collect information and to analyze it. 650 00:48:08,040 --> 00:48:11,839 Speaker 1: That's where the technology really comes in. We can lean 651 00:48:11,960 --> 00:48:14,919 Speaker 1: on tech to sift through information so that we can 652 00:48:14,960 --> 00:48:19,560 Speaker 1: glean insight from those findings. Cognitive systems can help guide 653 00:48:19,640 --> 00:48:22,480 Speaker 1: us to potential approaches that are more likely to have 654 00:48:22,560 --> 00:48:26,120 Speaker 1: a real impact and steer us away from actions that 655 00:48:26,239 --> 00:48:30,760 Speaker 1: might intuitively seem helpful but in reality have very little effect. 656 00:48:31,239 --> 00:48:35,239 Speaker 1: And as I learned, time is a critical component when 657 00:48:35,280 --> 00:48:39,440 Speaker 1: you're talking about health. To learn more about how IBM 658 00:48:39,520 --> 00:48:44,200 Speaker 1: is responding to COVID nineteen, including information that business leaders 659 00:48:44,200 --> 00:48:48,160 Speaker 1: can use to form their response plans and guide decision making, 660 00:48:48,760 --> 00:48:55,520 Speaker 1: visit IBM dot com slash impact slash COVID Dash nineteen. 661 00:48:56,080 --> 00:48:59,000 Speaker 1: I want to thank doctors Re and Donqua Mullin once 662 00:48:59,000 --> 00:49:01,919 Speaker 1: again for coming on a show and sharing their time 663 00:49:02,000 --> 00:49:05,839 Speaker 1: with me. I found it incredibly informative, and I hope 664 00:49:05,840 --> 00:49:08,960 Speaker 1: you did too. Make certain you check out all of 665 00:49:09,000 --> 00:49:12,279 Speaker 1: our past episodes of smart Talks. You can find those 666 00:49:12,320 --> 00:49:15,680 Speaker 1: past episodes in the Tech Stuff feed and the Stuff 667 00:49:15,719 --> 00:49:18,560 Speaker 1: to Blow your Mind feed. We've had a chance to 668 00:49:18,600 --> 00:49:22,640 Speaker 1: talk to lots of incredible people who are using technology 669 00:49:22,960 --> 00:49:26,640 Speaker 1: in really interesting ways, so make sure you go back 670 00:49:26,719 --> 00:49:30,080 Speaker 1: and listen to those episodes as well. Thank you so much. 671 00:49:35,040 --> 00:49:38,080 Speaker 1: Text Stuff is an I Heart Radio production. For more 672 00:49:38,160 --> 00:49:41,520 Speaker 1: podcasts from My Heart Radio, visit the I heart Radio app, 673 00:49:41,680 --> 00:49:44,840 Speaker 1: Apple podcasts, or wherever you listen to your favorite shows.