1 00:00:15,076 --> 00:00:21,156 Speaker 1: Pushkin. I'm Maybe Higgins and this is Solvable Interviews with 2 00:00:21,196 --> 00:00:25,276 Speaker 1: the world's most innovative thinkers working to solve the world's 3 00:00:25,316 --> 00:00:29,676 Speaker 1: biggest problems. My name is Nevine Rao. I'm the senior 4 00:00:29,716 --> 00:00:34,036 Speaker 1: vice president for Health and Rockefeller Foundation, and I believe 5 00:00:34,516 --> 00:00:38,716 Speaker 1: the crisis of maternal mortality is solvable. This episode, we're 6 00:00:38,756 --> 00:00:41,676 Speaker 1: hearing from doctor Nevine Rau. You just heard him there. 7 00:00:41,796 --> 00:00:45,356 Speaker 1: He's from the Rockefeller Foundation and he is a renowned 8 00:00:45,476 --> 00:00:51,396 Speaker 1: expert in safe pregnancies and healthy deliveries around the world. Now, 9 00:00:51,436 --> 00:00:53,876 Speaker 1: if you had to guess how many babies would you 10 00:00:53,916 --> 00:00:56,796 Speaker 1: say are born every day, I'll give you a second. 11 00:00:57,596 --> 00:01:01,756 Speaker 1: Now I cheated. I looked it up and UNISF estimates 12 00:01:01,796 --> 00:01:05,356 Speaker 1: that an average of wait for it, three hundred and 13 00:01:05,476 --> 00:01:10,276 Speaker 1: fifty three thousand babies are born each day around the world. 14 00:01:10,916 --> 00:01:14,716 Speaker 1: Is not incredible. It's more than four births every second, 15 00:01:15,356 --> 00:01:18,156 Speaker 1: and most of those they're safe for both the mother 16 00:01:18,316 --> 00:01:21,716 Speaker 1: and the baby. But many of those births are not. 17 00:01:22,236 --> 00:01:26,476 Speaker 1: In fact, nearly eight hundred and thirty women die every 18 00:01:26,596 --> 00:01:32,396 Speaker 1: day due to complications during pregnancy and childbirth. Now, most 19 00:01:32,436 --> 00:01:36,236 Speaker 1: of these deaths can be prevented through skilled care at 20 00:01:36,316 --> 00:01:41,716 Speaker 1: childbirth and just having access to emergency obstretric care. But 21 00:01:41,876 --> 00:01:45,916 Speaker 1: in Sub Saharan Africa, where maternal mortality ratios are the highest, 22 00:01:46,356 --> 00:01:49,516 Speaker 1: fewer than half of women are attended to by a 23 00:01:49,556 --> 00:01:53,156 Speaker 1: trained midwife or a nurse or a doctor during childbirth. 24 00:01:53,996 --> 00:01:57,636 Speaker 1: So you can probably guess that maternal deaths mirror the 25 00:01:57,676 --> 00:02:00,596 Speaker 1: gap between the rich and the poor. Less than one 26 00:02:00,676 --> 00:02:05,236 Speaker 1: percent of maternal deaths happen in wealthy countries. But I 27 00:02:05,276 --> 00:02:08,636 Speaker 1: wonder if you knew that America has the highest maternal 28 00:02:08,756 --> 00:02:13,276 Speaker 1: mortality rate of all industrial countries, in fact, by several 29 00:02:13,316 --> 00:02:17,916 Speaker 1: times over. Maternal and tile survival are the hallmarks of 30 00:02:17,956 --> 00:02:22,356 Speaker 1: healthy communities, and doctor Rown knows that. But he also 31 00:02:22,476 --> 00:02:26,876 Speaker 1: understands that although major advances in digital technology and data 32 00:02:26,916 --> 00:02:32,436 Speaker 1: science are definitely improving health intervention effectiveness, the global health 33 00:02:32,476 --> 00:02:36,956 Speaker 1: divide persists. All of these wonderful innovations, well, they're just 34 00:02:36,996 --> 00:02:41,596 Speaker 1: not reaching the poorest and most vulnerable communities. Doctor Rao 35 00:02:41,876 --> 00:02:46,036 Speaker 1: envisions a world where the data gap, and therefore the 36 00:02:46,076 --> 00:02:49,836 Speaker 1: health gap, can be bridged, and we'll hear more about 37 00:02:49,876 --> 00:02:53,476 Speaker 1: how he reached this thinking and also his daily work 38 00:02:53,596 --> 00:02:57,196 Speaker 1: towards this much better future. Let's listen to him now 39 00:02:57,196 --> 00:03:00,996 Speaker 1: with an apple bound What in your background led you 40 00:03:01,036 --> 00:03:04,076 Speaker 1: to this problem? How did you identify this as a 41 00:03:04,276 --> 00:03:07,556 Speaker 1: concrete problem that can be solved? And how must have 42 00:03:07,596 --> 00:03:11,276 Speaker 1: been about twenty five part of my training as a 43 00:03:11,316 --> 00:03:16,276 Speaker 1: medical student in rural India, and I remember this sixteen 44 00:03:16,396 --> 00:03:20,636 Speaker 1: year old girl being brought in. She had twins, and 45 00:03:20,676 --> 00:03:23,316 Speaker 1: these were the days when we didn't know she had twins. 46 00:03:23,316 --> 00:03:26,676 Speaker 1: There was no echo cardiogram, and apparently she delivered one 47 00:03:26,716 --> 00:03:28,756 Speaker 1: of the twins at home. It was a prolonged labor. 48 00:03:29,236 --> 00:03:31,916 Speaker 1: And now they brought her in to the hospital because 49 00:03:32,156 --> 00:03:35,876 Speaker 1: she had a second baby that was also obstructed. And 50 00:03:35,956 --> 00:03:39,156 Speaker 1: I remember there and helping with that, and as part 51 00:03:39,156 --> 00:03:44,476 Speaker 1: of that second delivery, she started bleeding and then literally 52 00:03:44,516 --> 00:03:47,996 Speaker 1: bled out, and I remember trying to stem the blood 53 00:03:47,996 --> 00:03:51,396 Speaker 1: and it's so horrific when you see blood gushing out 54 00:03:51,396 --> 00:03:54,196 Speaker 1: of a woman's Wigiane's just and you could see that 55 00:03:54,236 --> 00:03:56,956 Speaker 1: she was dying, and she knew she was dying. But 56 00:03:57,076 --> 00:04:00,436 Speaker 1: I never forget that, but that stayed with me. And 57 00:04:00,516 --> 00:04:06,316 Speaker 1: that was almost forty five years ago. And when I 58 00:04:06,356 --> 00:04:09,276 Speaker 1: got to America and I finished my training and was 59 00:04:09,276 --> 00:04:13,156 Speaker 1: a practicing physician, I was horrified to hear that this 60 00:04:13,236 --> 00:04:16,916 Speaker 1: problem still exists and in fact is getting worse in 61 00:04:16,956 --> 00:04:21,916 Speaker 1: some countries, and that even today woman died during pregnancy 62 00:04:21,916 --> 00:04:26,276 Speaker 1: and childbirth. The tragedy is that we know how to 63 00:04:26,316 --> 00:04:28,956 Speaker 1: save them. Most of the drugs and most of the 64 00:04:29,076 --> 00:04:33,636 Speaker 1: procedures have been in place since the nineteen forties, and 65 00:04:33,716 --> 00:04:37,116 Speaker 1: in some countries there is no materal mortality so to 66 00:04:37,156 --> 00:04:41,196 Speaker 1: speak of, and so it is solvable. It has been 67 00:04:41,196 --> 00:04:43,796 Speaker 1: solved in this day and age. There's some Scandinavian countries 68 00:04:43,836 --> 00:04:47,476 Speaker 1: that have solved it. So it is truly solvable, and 69 00:04:47,596 --> 00:04:51,116 Speaker 1: it has been solved. The fact that we still have 70 00:04:51,356 --> 00:04:54,676 Speaker 1: eight hundred women dying every day. Literally that's two jumber 71 00:04:54,756 --> 00:04:58,876 Speaker 1: jets crashing every day. I realized that we as a 72 00:04:59,476 --> 00:05:03,156 Speaker 1: human race are not going to progress unless we say 73 00:05:03,196 --> 00:05:06,396 Speaker 1: no to these unnecessary debts. Walk me through the nature 74 00:05:06,436 --> 00:05:08,916 Speaker 1: of the problem. So you say, the medical profession has 75 00:05:09,156 --> 00:05:12,796 Speaker 1: come up with solutions, we have ways to prevent women 76 00:05:12,836 --> 00:05:15,916 Speaker 1: from dying in childbirth. What is stopping people from getting 77 00:05:15,916 --> 00:05:19,316 Speaker 1: the healthcare that they need. I'll break it down. It's 78 00:05:19,436 --> 00:05:25,156 Speaker 1: very traditionally broken down into three segments. They're called three delays. 79 00:05:25,316 --> 00:05:28,276 Speaker 1: And this is very well researched and written about The 80 00:05:28,316 --> 00:05:32,276 Speaker 1: first delay is delay in seeking care. So this is 81 00:05:32,276 --> 00:05:36,596 Speaker 1: a delay in the woman herself going to the hospital 82 00:05:36,756 --> 00:05:41,156 Speaker 1: getting prenatal checkups, understanding that this needs and should be 83 00:05:41,196 --> 00:05:43,876 Speaker 1: a medical care and take care of her body and 84 00:05:44,036 --> 00:05:47,796 Speaker 1: her health, or the family also understanding that this should 85 00:05:47,836 --> 00:05:51,196 Speaker 1: be a delivery in the facility and that usually the 86 00:05:51,276 --> 00:05:55,076 Speaker 1: feeling in these villagers is the mother in law saying, look, 87 00:05:55,116 --> 00:05:58,196 Speaker 1: I delivered your husband in that back room. You go 88 00:05:58,236 --> 00:06:00,076 Speaker 1: and do it. We're not going to spend money on 89 00:06:00,156 --> 00:06:03,036 Speaker 1: hospitals doctors, and by the way, you still have to 90 00:06:03,036 --> 00:06:06,716 Speaker 1: sweep the barn and make the cows. So the first 91 00:06:06,716 --> 00:06:10,396 Speaker 1: delay is in seeking care even is a huge delay. 92 00:06:10,436 --> 00:06:13,916 Speaker 1: And the second delay is getting to care. So they 93 00:06:13,956 --> 00:06:16,396 Speaker 1: have realized, okay, they have done that, they've gone and 94 00:06:16,516 --> 00:06:19,476 Speaker 1: seen and had some prenatal checkups, but they have not 95 00:06:19,676 --> 00:06:23,036 Speaker 1: planned for how they're going to get to care. Either 96 00:06:23,116 --> 00:06:25,276 Speaker 1: they have not don't have the money at the last 97 00:06:25,316 --> 00:06:28,596 Speaker 1: minute to pay for the automobile the taxi, or they're 98 00:06:28,636 --> 00:06:32,316 Speaker 1: no ambulances, or even in certain parts such as Zambia, 99 00:06:32,516 --> 00:06:34,756 Speaker 1: if the flash floods have come and the road is 100 00:06:34,796 --> 00:06:37,036 Speaker 1: washed out, there's no way to get to care. So 101 00:06:37,076 --> 00:06:40,956 Speaker 1: the second delay is in getting to care. And the 102 00:06:40,956 --> 00:06:44,636 Speaker 1: third delay is receiving care. So there sometimes they do 103 00:06:44,676 --> 00:06:47,676 Speaker 1: that and they come and at the hospital there is 104 00:06:47,716 --> 00:06:51,036 Speaker 1: either no alexity, there's no medication, there's no train doctor, 105 00:06:51,076 --> 00:06:56,036 Speaker 1: there's no anesthesia, there no facilities, And so the third 106 00:06:56,076 --> 00:07:00,076 Speaker 1: delay isn't receiving care. And so it's not just enough 107 00:07:00,076 --> 00:07:02,916 Speaker 1: for us to say, oh, okay, we'll make sure they're ambulances, 108 00:07:02,956 --> 00:07:06,396 Speaker 1: because if they don't get into the ambulance, it's meaningless. 109 00:07:06,436 --> 00:07:08,916 Speaker 1: And or if they say, we say, we'll just put 110 00:07:09,156 --> 00:07:12,836 Speaker 1: dication and we'll train doctors, but if you haven't done 111 00:07:11,956 --> 00:07:17,156 Speaker 1: the community outreach to make them want to come, it's meaningless. 112 00:07:17,196 --> 00:07:20,116 Speaker 1: So really all three delays need to be addressed together. 113 00:07:20,596 --> 00:07:23,996 Speaker 1: And usually most of these women die a combination of 114 00:07:24,036 --> 00:07:27,876 Speaker 1: the delays. Most often it's all three. So maybe can 115 00:07:27,916 --> 00:07:30,236 Speaker 1: you give me some idea of what we're talking about 116 00:07:30,276 --> 00:07:35,716 Speaker 1: in terms of numbers how many women die annually, but 117 00:07:35,836 --> 00:07:38,956 Speaker 1: also how have those numbers been reduced in recent years, 118 00:07:39,556 --> 00:07:42,476 Speaker 1: and how do you foresee them being reduced further in 119 00:07:42,516 --> 00:07:45,876 Speaker 1: the next ten or twenty or thirty years. The goal 120 00:07:46,156 --> 00:07:50,516 Speaker 1: is to reach preventable maternal mortality, to reduce it down 121 00:07:50,516 --> 00:07:55,476 Speaker 1: to seventy by twenty thirty. I mean, no death is acceptable, 122 00:07:55,476 --> 00:07:57,996 Speaker 1: but seventy is a number that the world has put 123 00:07:58,036 --> 00:08:00,716 Speaker 1: us taken the ground saying, if we can make sure 124 00:08:00,876 --> 00:08:04,756 Speaker 1: every country comes down to seventy, that would be achievable. 125 00:08:04,956 --> 00:08:08,116 Speaker 1: Some countries today that number is five and in some 126 00:08:08,156 --> 00:08:11,636 Speaker 1: countries that number five thousand, and so we have made 127 00:08:11,756 --> 00:08:15,756 Speaker 1: huge progress in the last ten years. We've halfd metal 128 00:08:15,796 --> 00:08:19,676 Speaker 1: mortality as a world, but we're still very far away 129 00:08:19,756 --> 00:08:23,676 Speaker 1: from the seventy number. And the business is usual as 130 00:08:23,796 --> 00:08:27,076 Speaker 1: the rates of reduction as we see it now will 131 00:08:27,116 --> 00:08:30,716 Speaker 1: not get us to that number of seventy metal mortality. 132 00:08:30,876 --> 00:08:33,756 Speaker 1: So there has been a huge progress, but the rate 133 00:08:33,796 --> 00:08:35,916 Speaker 1: of reduction is not enough to get us where we 134 00:08:35,956 --> 00:08:39,356 Speaker 1: want to go. Those are the numbers. And currently, as 135 00:08:39,356 --> 00:08:43,236 Speaker 1: I said, eight hundred women die every day in the world, 136 00:08:43,596 --> 00:08:46,396 Speaker 1: and by the way, seven hundred women die every year 137 00:08:46,756 --> 00:08:50,636 Speaker 1: here in the US. It's almost two deaths a day. Wow. 138 00:08:50,716 --> 00:08:53,396 Speaker 1: So how do you overcome this first barrier? How do 139 00:08:53,436 --> 00:08:57,156 Speaker 1: you convince people to come to appointments, to come to hospitals. 140 00:08:57,196 --> 00:09:00,076 Speaker 1: How do you get them used to the idea that 141 00:09:00,636 --> 00:09:03,556 Speaker 1: birth is not something that takes place at home. So 142 00:09:03,636 --> 00:09:06,716 Speaker 1: if you take India as an example. They have done 143 00:09:06,756 --> 00:09:12,676 Speaker 1: a huge outreach to including conditional cash transfers to community 144 00:09:12,716 --> 00:09:15,756 Speaker 1: health workers to bring these pregnant women into the facilities, 145 00:09:16,596 --> 00:09:18,996 Speaker 1: and there was a push and there's almost been an 146 00:09:19,036 --> 00:09:22,836 Speaker 1: eighty percent increase in facility birth rates in India, so 147 00:09:22,956 --> 00:09:27,556 Speaker 1: it can be done, and behavioral change communications they've been 148 00:09:27,676 --> 00:09:33,876 Speaker 1: they've used local storytelling, they've used the power of pure experience, 149 00:09:34,396 --> 00:09:36,996 Speaker 1: so all that has worked, and in fact there's been 150 00:09:37,036 --> 00:09:40,876 Speaker 1: a huge increase in facility births rather than birthing at home. 151 00:09:41,276 --> 00:09:46,836 Speaker 1: But unfortunately that eighty percent increase in facility births has 152 00:09:46,916 --> 00:09:51,876 Speaker 1: not resulted in an equalent eighty percent decrease in maternal mortality. 153 00:09:52,356 --> 00:09:55,156 Speaker 1: The facilities were not ready for this onslaught and the 154 00:09:55,276 --> 00:09:58,276 Speaker 1: quality of care they were receiving or the protocols that 155 00:09:58,316 --> 00:10:01,716 Speaker 1: they had in place were not suffice and so they 156 00:10:02,156 --> 00:10:06,036 Speaker 1: did initially see the eighty percent decrease. But the way 157 00:10:06,036 --> 00:10:08,916 Speaker 1: India went about it is first is raising the demand, 158 00:10:09,156 --> 00:10:13,356 Speaker 1: using the awareness and incentivizing women to give birth in facilities, 159 00:10:13,396 --> 00:10:18,836 Speaker 1: including making the whole experience free, including the transportation, and 160 00:10:18,996 --> 00:10:22,996 Speaker 1: now are very much focused on the quality that the 161 00:10:23,036 --> 00:10:26,396 Speaker 1: woman will receive during that childbirth process. If you add 162 00:10:26,436 --> 00:10:30,236 Speaker 1: to that data analysis and data predictability and predictive analytics 163 00:10:30,236 --> 00:10:33,236 Speaker 1: to see which woman is a high risk and once 164 00:10:33,276 --> 00:10:35,956 Speaker 1: they come into the hospital and to triage them, and 165 00:10:36,036 --> 00:10:39,236 Speaker 1: to be able to use the latest and the best 166 00:10:39,236 --> 00:10:42,596 Speaker 1: in data and technology is again leads us to believe 167 00:10:42,636 --> 00:10:45,756 Speaker 1: this is solvable and hence is something we should be doing. 168 00:10:46,196 --> 00:10:48,556 Speaker 1: Tell me a little bit more about data. You know, 169 00:10:48,596 --> 00:10:52,076 Speaker 1: we're talking about remote communities. What kind of difference can 170 00:10:52,196 --> 00:10:55,556 Speaker 1: data make? How does that help doctors in rural India. 171 00:10:56,836 --> 00:11:00,196 Speaker 1: I have been in communities and it's amazing how the 172 00:11:00,236 --> 00:11:04,596 Speaker 1: advent of mobile phone technology has so penetrated even the 173 00:11:04,676 --> 00:11:07,636 Speaker 1: rural areas in a lighter way. They say, they probably 174 00:11:08,116 --> 00:11:10,796 Speaker 1: more telephones than bathrooms in India, and so the people 175 00:11:10,796 --> 00:11:14,436 Speaker 1: who have access to a phone more easier than electricity 176 00:11:14,556 --> 00:11:19,116 Speaker 1: with that kind of penetration, I have seen, say in 177 00:11:19,156 --> 00:11:22,996 Speaker 1: that village, in these communities, in a house, the husband 178 00:11:23,556 --> 00:11:28,116 Speaker 1: who's usually the farmer, the male, the man has a 179 00:11:28,196 --> 00:11:31,916 Speaker 1: phone and today on his phone, the farmer has a 180 00:11:31,916 --> 00:11:35,956 Speaker 1: weather forecasting app that tells him went to plant and 181 00:11:35,996 --> 00:11:39,076 Speaker 1: went to harvest. He's got an app that tells him 182 00:11:39,116 --> 00:11:42,196 Speaker 1: the prices of his harvest and the produce in the 183 00:11:42,236 --> 00:11:44,876 Speaker 1: market that day, so he knows when to sell. He 184 00:11:44,956 --> 00:11:48,676 Speaker 1: also has on an app transportation like the equordent of 185 00:11:48,676 --> 00:11:51,076 Speaker 1: the ubers, to be able to move his produce and 186 00:11:51,476 --> 00:11:55,276 Speaker 1: his harvest to the cities for a better price. This 187 00:11:55,356 --> 00:11:58,956 Speaker 1: exists today, We've seen it. And in that same house 188 00:11:59,996 --> 00:12:04,116 Speaker 1: is the wife who's the community health worker, and she 189 00:12:04,316 --> 00:12:08,956 Speaker 1: carries around six registers, does not have access to the phone, 190 00:12:09,556 --> 00:12:12,956 Speaker 1: has twenty families that she's seeing, has no idea how 191 00:12:12,996 --> 00:12:16,516 Speaker 1: to optimize her day, which household is at risk, which 192 00:12:16,596 --> 00:12:20,276 Speaker 1: child in her community of who she's responsible is at 193 00:12:20,396 --> 00:12:24,596 Speaker 1: risk for my nutrition? Why couldn't she have similar predictive 194 00:12:24,596 --> 00:12:28,556 Speaker 1: analytic tools like weather forecasting that would help her do 195 00:12:28,676 --> 00:12:31,436 Speaker 1: her job better. So it is not just the doctors 196 00:12:31,516 --> 00:12:34,356 Speaker 1: having access to data, It is how can the community 197 00:12:34,396 --> 00:12:38,356 Speaker 1: health workers, the frontline healthcare workers have predictive analytics tools 198 00:12:38,396 --> 00:12:41,796 Speaker 1: that will optimize their work process but also in real 199 00:12:41,836 --> 00:12:44,276 Speaker 1: time can give them insights and inputs on how to 200 00:12:44,276 --> 00:12:46,356 Speaker 1: take care of these patients, of what tests to do, 201 00:12:46,636 --> 00:12:49,876 Speaker 1: which ones are the triage, which ones are the ones 202 00:12:49,916 --> 00:12:53,156 Speaker 1: at high risk. So this could be something as simple 203 00:12:53,196 --> 00:12:56,716 Speaker 1: as community health workers having a kind of app on 204 00:12:56,756 --> 00:12:59,076 Speaker 1: their phone that could help them give advice to pregnant 205 00:12:59,116 --> 00:13:01,596 Speaker 1: women or help them make decisions about who needs what 206 00:13:01,716 --> 00:13:05,556 Speaker 1: kind of care. That would be exactly the start. From there, 207 00:13:05,876 --> 00:13:08,276 Speaker 1: you can envision where she could have the story of 208 00:13:08,316 --> 00:13:11,156 Speaker 1: her village to know if there's a huge absence of 209 00:13:11,636 --> 00:13:15,116 Speaker 1: children in one school in her community, she should now 210 00:13:15,276 --> 00:13:18,036 Speaker 1: go there to see is there a diary outbreak, what's happening? 211 00:13:18,036 --> 00:13:20,116 Speaker 1: Why are the children are coming to school? There are 212 00:13:20,196 --> 00:13:22,836 Speaker 1: so many ways we can then build on it. What 213 00:13:22,956 --> 00:13:26,956 Speaker 1: about doctors in these communities, how can they access data 214 00:13:26,956 --> 00:13:28,836 Speaker 1: and how can that make a difference to what they do? 215 00:13:29,476 --> 00:13:34,796 Speaker 1: So take supply chain. Most doctors in these villages, if 216 00:13:34,796 --> 00:13:38,996 Speaker 1: there's a primary secondary health center, the doctor in charge 217 00:13:39,236 --> 00:13:42,316 Speaker 1: is the superintendent of the hospital, and he or she 218 00:13:42,436 --> 00:13:46,516 Speaker 1: has never been trained on stock forecasting, has never been 219 00:13:46,516 --> 00:13:51,236 Speaker 1: trained on human resource distribution and how to supply and demand. 220 00:13:52,276 --> 00:13:55,956 Speaker 1: If these apps can actually in real time keep track 221 00:13:56,036 --> 00:14:01,076 Speaker 1: of stockouts demands, is there any way that the data 222 00:14:01,396 --> 00:14:03,796 Speaker 1: can give a better insight to these doctors to be 223 00:14:03,836 --> 00:14:07,836 Speaker 1: able to do a better supply management, better access to 224 00:14:08,636 --> 00:14:11,916 Speaker 1: where the crisis and they can they have an access 225 00:14:11,916 --> 00:14:15,956 Speaker 1: that tells them based on social media and other data inputs. 226 00:14:15,956 --> 00:14:18,996 Speaker 1: Where are the migrants coming from, what's happening across borders, 227 00:14:18,996 --> 00:14:21,556 Speaker 1: where is the water on area, what's happening, is there 228 00:14:21,596 --> 00:14:25,836 Speaker 1: another ebola brewing? Data can help identify hot spots and 229 00:14:25,956 --> 00:14:29,116 Speaker 1: cold spots. Cold spots could be a whole region where 230 00:14:29,156 --> 00:14:32,156 Speaker 1: children have not being immunized and nobody's kept tracked and 231 00:14:32,236 --> 00:14:34,676 Speaker 1: we don't know because they are in the blind spots. 232 00:14:35,196 --> 00:14:39,196 Speaker 1: Hot spots could be where this flash pandemics or something 233 00:14:39,236 --> 00:14:42,156 Speaker 1: brewing that we could get earlier warning. But what about 234 00:14:42,156 --> 00:14:46,716 Speaker 1: specifically to deal with the issue of maternal mortality. Is 235 00:14:46,756 --> 00:14:50,116 Speaker 1: there you know, are there particular kinds of programs or apps, 236 00:14:50,196 --> 00:14:51,956 Speaker 1: or is there a kind of data that doctors can 237 00:14:51,996 --> 00:14:55,836 Speaker 1: find particularly useful. So if the frontline healthcare worker can 238 00:14:55,916 --> 00:14:58,756 Speaker 1: find out if there is a region where women are 239 00:14:58,796 --> 00:15:03,476 Speaker 1: not coming to anti natal care for visits and could 240 00:15:03,476 --> 00:15:07,196 Speaker 1: be very easily tracked based on whether the woman has 241 00:15:07,436 --> 00:15:10,396 Speaker 1: made an anti natal visit and if she asn't, they 242 00:15:10,396 --> 00:15:13,236 Speaker 1: could even make home visits or they could encourage the 243 00:15:13,276 --> 00:15:16,276 Speaker 1: woman to come in and we know, for example, simple 244 00:15:16,636 --> 00:15:20,316 Speaker 1: antenatal visit to check for protein in the urine, blood pressure, 245 00:15:20,916 --> 00:15:24,956 Speaker 1: sugar levels make a huge difference. I've also seen an 246 00:15:24,956 --> 00:15:29,796 Speaker 1: app it's in formulation stage. It's actually the camera can 247 00:15:29,876 --> 00:15:34,316 Speaker 1: take a video. So I've seen where in India, the 248 00:15:34,476 --> 00:15:38,796 Speaker 1: healthcare worker waves this camera her cell phone over the 249 00:15:38,836 --> 00:15:42,076 Speaker 1: belly of the pregnant woman. An inside, there is an 250 00:15:42,116 --> 00:15:46,036 Speaker 1: algorithm that based on that image and that picture that's taken, 251 00:15:46,996 --> 00:15:51,876 Speaker 1: the woman's size of the pelvis is measured, and the 252 00:15:51,996 --> 00:15:55,956 Speaker 1: baby's head is measured, and an algorithm predicts whether this 253 00:15:55,996 --> 00:15:58,636 Speaker 1: will be an obstructed labor, whether the child's head is 254 00:15:58,636 --> 00:16:01,356 Speaker 1: too big for the woman's pelvis. Wow, And that can 255 00:16:01,356 --> 00:16:03,596 Speaker 1: be put in a cell phone. Yes, I've seen it. 256 00:16:03,596 --> 00:16:07,476 Speaker 1: It already exists. Inside obviously has to be finalized and commercialized, 257 00:16:07,516 --> 00:16:09,596 Speaker 1: but people are thinking that way. So if you think 258 00:16:09,636 --> 00:16:14,796 Speaker 1: about how data and applications are changing in our lives today, 259 00:16:14,876 --> 00:16:17,556 Speaker 1: there's so many people with the Apple Watch that has 260 00:16:17,596 --> 00:16:20,716 Speaker 1: the health monitor on it. What can we do if 261 00:16:20,756 --> 00:16:23,196 Speaker 1: we take that kind of mindset and those kind of 262 00:16:23,236 --> 00:16:26,836 Speaker 1: assets to the developing world to improve public health, community 263 00:16:26,876 --> 00:16:30,676 Speaker 1: health And to me, I'm using maternal mortality as a 264 00:16:30,756 --> 00:16:34,356 Speaker 1: sentinel indicator the Canadian the coal mine, so to speak, 265 00:16:34,636 --> 00:16:38,116 Speaker 1: where it tells me the status and the health of 266 00:16:38,156 --> 00:16:42,236 Speaker 1: the community, because the first ones to die, the most vulnerable, 267 00:16:42,356 --> 00:16:45,836 Speaker 1: are the pregnant woman, and if we can save them, 268 00:16:45,876 --> 00:16:48,676 Speaker 1: it means very likely we have a system in place 269 00:16:49,276 --> 00:16:53,396 Speaker 1: that is saving many people. And so these data, these 270 00:16:53,436 --> 00:16:56,676 Speaker 1: tools are needed, are needed today. They exist. Is just 271 00:16:56,756 --> 00:16:58,796 Speaker 1: that somebody has to put it together, and that's where 272 00:16:58,796 --> 00:17:02,196 Speaker 1: we are. Do you get any opposition to the use 273 00:17:02,236 --> 00:17:05,196 Speaker 1: of technology and data? Do you find that people distrusted? 274 00:17:05,236 --> 00:17:09,876 Speaker 1: Do you have people rejecting it? In the countries that 275 00:17:09,996 --> 00:17:12,756 Speaker 1: I am working on right now, I can presume it 276 00:17:12,756 --> 00:17:16,916 Speaker 1: will happen. India is putting in place draconian and much 277 00:17:16,996 --> 00:17:21,436 Speaker 1: needed and many aspects health data, privacy and security laws. 278 00:17:21,876 --> 00:17:24,756 Speaker 1: So it is coming. But right now, when we show 279 00:17:24,836 --> 00:17:27,436 Speaker 1: up and we talk about how we are helping women 280 00:17:27,516 --> 00:17:32,036 Speaker 1: survive childbirth, there is open arms and even in even 281 00:17:31,796 --> 00:17:36,596 Speaker 1: in communities. Here in the US, it's the only developed 282 00:17:36,596 --> 00:17:39,996 Speaker 1: country in the world where metal mortality is rising. And 283 00:17:40,036 --> 00:17:42,916 Speaker 1: that is really an absolute shame, considering that we spend 284 00:17:42,956 --> 00:17:46,036 Speaker 1: more than any other country on healthcare. And is that 285 00:17:46,476 --> 00:17:48,956 Speaker 1: is that for similar reasons you have these same kinds 286 00:17:48,996 --> 00:17:50,836 Speaker 1: of obstacles in the US that you have here. Yes, 287 00:17:50,916 --> 00:17:52,556 Speaker 1: they are the same three delays, but they have a 288 00:17:52,596 --> 00:17:55,876 Speaker 1: different connotation. So the second delay is not that there's 289 00:17:55,876 --> 00:17:57,836 Speaker 1: a flash fard and they can't get to The second 290 00:17:57,836 --> 00:18:01,356 Speaker 1: delays she's in a housing project and taxis won't come there. 291 00:18:01,436 --> 00:18:03,956 Speaker 1: She doesn't have money for a taxi, and she can 292 00:18:03,956 --> 00:18:06,076 Speaker 1: see the hospital, but she can't cross it because there's 293 00:18:06,076 --> 00:18:09,956 Speaker 1: a huge highway in between. So, yes, you can envision 294 00:18:09,996 --> 00:18:13,196 Speaker 1: the delays. The concepts are the same, the details are different. 295 00:18:13,276 --> 00:18:17,676 Speaker 1: Also here in this country we have slightly different causes. 296 00:18:17,716 --> 00:18:21,556 Speaker 1: In the developing world. That three big causes are woman bleeding, 297 00:18:21,596 --> 00:18:24,356 Speaker 1: which is postpartum hemorrhage, pre acclamps here, which is when 298 00:18:24,356 --> 00:18:27,356 Speaker 1: the blood pressure shoots up and you get seizures and 299 00:18:27,436 --> 00:18:32,156 Speaker 1: brain damage. The third is sepsis, which is infection. Here 300 00:18:32,156 --> 00:18:35,276 Speaker 1: in the US it is coiegulation disorders, it is how 301 00:18:35,316 --> 00:18:40,796 Speaker 1: do you askular disease, It's comordabilities, it's older woman, it's obesity. 302 00:18:40,876 --> 00:18:44,596 Speaker 1: It's also general lack of health and women not engaging 303 00:18:44,636 --> 00:18:48,756 Speaker 1: with the healthcare system. So there are similarities, there are 304 00:18:48,796 --> 00:18:51,716 Speaker 1: some nuance differences, but the bottom line is the same. 305 00:18:52,156 --> 00:18:56,156 Speaker 1: Women are dying from preventable causes, and to think that 306 00:18:56,196 --> 00:18:59,916 Speaker 1: the rate is going up in this country is just unacceptable. 307 00:18:59,996 --> 00:19:03,316 Speaker 1: I agree, it's very shocking. It's all very well talking 308 00:19:03,356 --> 00:19:07,676 Speaker 1: about technology and apps and cell phones, but how can 309 00:19:07,716 --> 00:19:10,836 Speaker 1: you use this technology parts of the world where power 310 00:19:10,916 --> 00:19:14,996 Speaker 1: is unreliable and internet connections are unreliable? Do you have 311 00:19:15,156 --> 00:19:19,996 Speaker 1: solutions for that as well? So any and all attempts 312 00:19:20,116 --> 00:19:25,556 Speaker 1: at improving health will also have to buy nature address 313 00:19:26,116 --> 00:19:32,316 Speaker 1: the data inequity gap. Yes, electricity, internet connectivity, all these 314 00:19:32,916 --> 00:19:37,196 Speaker 1: are current barriers, but these have been bridged. There are 315 00:19:37,276 --> 00:19:41,436 Speaker 1: solutions for this. They are off grade solutions. They are 316 00:19:41,556 --> 00:19:45,396 Speaker 1: offline solutions. And in fact, most of the apps that 317 00:19:45,596 --> 00:19:47,756 Speaker 1: exist in most of the ones that are working right 318 00:19:47,796 --> 00:19:51,876 Speaker 1: now in parts of Africa by large part work offline 319 00:19:52,236 --> 00:19:55,516 Speaker 1: and then when the internet connection is there, they do 320 00:19:55,556 --> 00:20:00,676 Speaker 1: the upgrading. So these are solvable by technology. But it's 321 00:20:00,716 --> 00:20:04,476 Speaker 1: that feeling that we can and should do it that 322 00:20:04,636 --> 00:20:06,356 Speaker 1: is the piece that we need to cross. And once 323 00:20:06,396 --> 00:20:08,316 Speaker 1: we've crossed that, I have a feeling we can get 324 00:20:08,316 --> 00:20:12,036 Speaker 1: to all these current barriers. Even if you would have 325 00:20:12,236 --> 00:20:16,076 Speaker 1: asked me twenty years ago if I was in charge 326 00:20:16,076 --> 00:20:20,356 Speaker 1: of all health for a coastal village that was that 327 00:20:20,476 --> 00:20:23,756 Speaker 1: was routinely hit by hurricanes, and they asked me, what 328 00:20:23,796 --> 00:20:26,276 Speaker 1: would I have to do to make to save people 329 00:20:26,716 --> 00:20:29,636 Speaker 1: when the hurricane comes. Based on what I knew then, 330 00:20:29,756 --> 00:20:31,996 Speaker 1: I would have said, Oh, we need to build more shelters, 331 00:20:32,036 --> 00:20:33,916 Speaker 1: we need to have more hospitals, we need to have 332 00:20:33,996 --> 00:20:36,716 Speaker 1: more collar of vaccines, we need to have more clean water. 333 00:20:37,476 --> 00:20:39,596 Speaker 1: How do I save the lives? Based on what I know? 334 00:20:40,236 --> 00:20:43,396 Speaker 1: But today probably the thing that saves more lives is 335 00:20:43,436 --> 00:20:46,876 Speaker 1: the weather predicting app forecast that tells me the storm 336 00:20:46,996 --> 00:20:50,876 Speaker 1: is coming and I can evacuate people. And I would 337 00:20:50,876 --> 00:20:53,156 Speaker 1: have never thought of that as saving more lives. Twenty 338 00:20:53,196 --> 00:20:55,996 Speaker 1: five years ago, I'd have built more hospitals, more shelters, 339 00:20:56,036 --> 00:21:00,316 Speaker 1: But today that single app is saving more lives than 340 00:21:00,556 --> 00:21:02,836 Speaker 1: all the things we could have done. Similarly, today, if 341 00:21:02,836 --> 00:21:05,436 Speaker 1: we were talking about how can we save these mothers 342 00:21:05,476 --> 00:21:09,036 Speaker 1: from dying, we're talking about internet connect community. We're talking 343 00:21:09,156 --> 00:21:12,516 Speaker 1: about more hospitals, better training, on and on and on. 344 00:21:12,636 --> 00:21:15,676 Speaker 1: Perhaps there's technology out there that will take us to 345 00:21:15,716 --> 00:21:17,956 Speaker 1: a completely different place. I just want to make sure 346 00:21:18,476 --> 00:21:20,716 Speaker 1: that the current barriers don't hold us back, and that 347 00:21:20,836 --> 00:21:23,596 Speaker 1: we do understand there's a data in equity and that 348 00:21:23,596 --> 00:21:26,556 Speaker 1: that is exacerbating health in equities, and what are the 349 00:21:26,636 --> 00:21:30,316 Speaker 1: obstacles to you, what's still standing in your way, what's 350 00:21:30,396 --> 00:21:34,636 Speaker 1: keeping you from bringing down the this mortality rate more quickly? 351 00:21:34,916 --> 00:21:38,636 Speaker 1: So I will I will start that by quoting doctor 352 00:21:38,716 --> 00:21:43,996 Speaker 1: Mohammad Mahmata was an obstitation is an obstacian who considered 353 00:21:44,036 --> 00:21:47,916 Speaker 1: the father of this whole concept. He very famously once said, 354 00:21:47,916 --> 00:21:52,476 Speaker 1: and I'm quoting him, women are dying not because we 355 00:21:52,516 --> 00:21:55,276 Speaker 1: don't know how to save them. They're dying because we 356 00:21:55,316 --> 00:21:58,396 Speaker 1: have yet to decide their worth saving and to live. 357 00:21:58,516 --> 00:22:02,356 Speaker 1: That it is very clear that for any of what 358 00:22:02,556 --> 00:22:06,556 Speaker 1: solutions we come up with to stick, sustain and scale 359 00:22:06,636 --> 00:22:10,956 Speaker 1: in country, first we need the country. We need a 360 00:22:10,996 --> 00:22:15,516 Speaker 1: political sustainability. We need political will. We need the policymakers, 361 00:22:15,516 --> 00:22:20,476 Speaker 1: the decision makers to decide that the woman are worth saving. Second, 362 00:22:20,676 --> 00:22:24,436 Speaker 1: we need social sustainability. We need the culture to be 363 00:22:24,796 --> 00:22:28,756 Speaker 1: where the woman is valued and where healthcare is considered 364 00:22:28,796 --> 00:22:32,676 Speaker 1: important for these women to get and to deliver in 365 00:22:32,676 --> 00:22:37,956 Speaker 1: a facility. And then we also need the commercials sustainability 366 00:22:37,996 --> 00:22:42,676 Speaker 1: that whatever systems are put in place have to benefit 367 00:22:42,956 --> 00:22:47,476 Speaker 1: society and that we do understand that these are not 368 00:22:47,796 --> 00:22:50,956 Speaker 1: just programs that we can go in and set up 369 00:22:51,076 --> 00:22:54,956 Speaker 1: as philanthropy and turn around and walk away, because we 370 00:22:54,996 --> 00:22:56,476 Speaker 1: need to teach them out of fish, and then we 371 00:22:56,556 --> 00:22:59,436 Speaker 1: need to make it commercially viable for them to fish 372 00:22:59,556 --> 00:23:01,356 Speaker 1: rather than just give them the fish. So we need 373 00:23:01,356 --> 00:23:04,716 Speaker 1: to set up systems where this is then sustained locally 374 00:23:04,716 --> 00:23:07,796 Speaker 1: within the community. So that is the barrier is how 375 00:23:07,796 --> 00:23:12,476 Speaker 1: do we sustain scale the solutions that we put in place. 376 00:23:12,756 --> 00:23:15,116 Speaker 1: But it also sounds like, you know, there are these 377 00:23:15,116 --> 00:23:19,036 Speaker 1: incredible pieces of technology available, but there's also a fundamental 378 00:23:19,476 --> 00:23:24,476 Speaker 1: emotional or psychological obstacle, which is that not everywhere do 379 00:23:24,556 --> 00:23:28,036 Speaker 1: people think that women's lives are important. Obviously that is 380 00:23:28,036 --> 00:23:30,716 Speaker 1: true here in the US too. They are countries that 381 00:23:30,756 --> 00:23:33,916 Speaker 1: have come together that have realized that saving the woman 382 00:23:34,036 --> 00:23:35,556 Speaker 1: is not just that I think to do, but it's 383 00:23:35,556 --> 00:23:37,956 Speaker 1: the smart thing to do. And there is equality and 384 00:23:38,476 --> 00:23:40,876 Speaker 1: there is no mental immortality so to speak of. So 385 00:23:41,916 --> 00:23:44,436 Speaker 1: it is just this that they are still communities and 386 00:23:44,796 --> 00:23:48,676 Speaker 1: their countries that wage political wars on women's bodies, and 387 00:23:48,796 --> 00:23:51,916 Speaker 1: even here in this country is no different. So we 388 00:23:51,996 --> 00:23:54,596 Speaker 1: need to be able to break those barriers. But just 389 00:23:54,636 --> 00:23:56,556 Speaker 1: breaking those barriers and not enough. We need to come 390 00:23:56,636 --> 00:24:00,036 Speaker 1: up with the medication technology training to then actually really 391 00:24:00,076 --> 00:24:05,516 Speaker 1: save them, because no amount of cultural training will help 392 00:24:05,556 --> 00:24:08,596 Speaker 1: the woman who needs associated section. A lot of people 393 00:24:08,636 --> 00:24:11,276 Speaker 1: listening to this might be inspired by some of the 394 00:24:11,356 --> 00:24:14,156 Speaker 1: things you've said and might like to want to try 395 00:24:14,156 --> 00:24:17,156 Speaker 1: and help solve this problem. Are there things that listeners 396 00:24:17,156 --> 00:24:19,476 Speaker 1: can do? Do you have any advice for people listening? 397 00:24:19,956 --> 00:24:23,236 Speaker 1: So the first thing, there are many organizations that are linked. 398 00:24:23,796 --> 00:24:26,916 Speaker 1: I would say, be aware, get to know it, and 399 00:24:26,956 --> 00:24:29,796 Speaker 1: if you depending on the sliding scale, whether you suggest 400 00:24:29,876 --> 00:24:32,556 Speaker 1: your pocket, whether you can give some money, whether you 401 00:24:32,556 --> 00:24:36,116 Speaker 1: can give time, whether you can give some volunteer hours work, 402 00:24:36,796 --> 00:24:39,036 Speaker 1: it's a sliding scale. I think it all depends on 403 00:24:39,076 --> 00:24:41,596 Speaker 1: where your heart is. But I think the journey should 404 00:24:41,596 --> 00:24:45,356 Speaker 1: start from educating oneself, finding out who are in this space, 405 00:24:45,516 --> 00:24:49,636 Speaker 1: and then reaching out. And obviously, the Rockefeller Foundation has 406 00:24:49,676 --> 00:24:52,636 Speaker 1: our website, and on that website there's a health section, 407 00:24:53,116 --> 00:24:55,476 Speaker 1: and then you can see how we are working towards 408 00:24:55,996 --> 00:25:00,316 Speaker 1: trying to solve this. Really powerful words from doctor Navine 409 00:25:00,356 --> 00:25:04,956 Speaker 1: Rao from the Rockefeller Foundation there about maternal mortality and 410 00:25:05,116 --> 00:25:08,756 Speaker 1: also talking about what it's really dealing with, which is 411 00:25:09,236 --> 00:25:13,356 Speaker 1: women and children's lives, and how things can change when 412 00:25:13,436 --> 00:25:19,636 Speaker 1: society decides that they are worth saving. Solvable is a 413 00:25:19,676 --> 00:25:24,196 Speaker 1: collaboration between Pushkin Industries and the Rockefella Foundation, with production 414 00:25:24,356 --> 00:25:28,316 Speaker 1: by Laura Hyde, Hester Kant, Laura Sheeter, and Ruth Barnes 415 00:25:28,396 --> 00:25:32,236 Speaker 1: from Chalk and Blade. Pushkin's executive producer is Neia LaBelle, 416 00:25:32,596 --> 00:25:36,796 Speaker 1: Research by Sheer, Vincent, engineering by Jason Gambrel and the 417 00:25:36,876 --> 00:25:41,636 Speaker 1: great folks at GSI Studios. Original music composed by Pascal 418 00:25:41,716 --> 00:25:46,156 Speaker 1: Wise and special thanks to Maggie Taylor, Heather Fine, Julia Barton, 419 00:25:46,516 --> 00:25:50,716 Speaker 1: Carly Mgliori, Jacob Weisberg, and Malcolm Gladwell. You can learn 420 00:25:50,756 --> 00:25:55,716 Speaker 1: more about solving today's biggest problems at Rockefella Foundation dot org, 421 00:25:55,916 --> 00:26:12,716 Speaker 1: slash Solvable. I'm Mave Higgins now got solvus then