1 00:00:00,809 --> 00:00:02,270 I'm going to talk to you today 2 00:00:02,294 --> 00:00:05,777 about the design of medical technology for low-resource settings. 3 00:00:05,801 --> 00:00:08,154 I study health systems in these countries. 4 00:00:08,178 --> 00:00:10,093 And one of the major gaps in care, 5 00:00:10,117 --> 00:00:11,776 almost across the board, 6 00:00:11,800 --> 00:00:13,967 is access to safe surgery. 7 00:00:13,991 --> 00:00:16,650 Now one of the major bottlenecks that we've found 8 00:00:16,674 --> 00:00:19,909 that's sort of preventing both the access in the first place, 9 00:00:19,933 --> 00:00:23,369 and the safety of those surgeries that do happen, is anesthesia. 10 00:00:23,846 --> 00:00:26,238 And actually, it's the model that we expect to work 11 00:00:26,262 --> 00:00:28,961 for delivering anesthesia in these environments. 12 00:00:29,620 --> 00:00:33,473 Here, we have a scene that you would find in any operating room across the US, 13 00:00:33,497 --> 00:00:34,974 or any other developed country. 14 00:00:34,998 --> 00:00:36,185 In the background there 15 00:00:36,209 --> 00:00:38,780 is a very sophisticated anesthesia machine. 16 00:00:38,804 --> 00:00:42,653 And this machine is able to enable surgery and save lives 17 00:00:42,677 --> 00:00:45,976 because it was designed with this environment in mind. 18 00:00:46,329 --> 00:00:49,261 In order to operate, this machine needs a number of things 19 00:00:49,285 --> 00:00:51,185 that this hospital has to offer. 20 00:00:51,209 --> 00:00:54,577 It needs an extremely well-trained anesthesiologist 21 00:00:54,601 --> 00:00:57,052 with years of training with complex machines 22 00:00:57,076 --> 00:00:59,497 to help her monitor the flows of the gas 23 00:00:59,521 --> 00:01:02,084 and keep her patients safe and anesthetized 24 00:01:02,108 --> 00:01:03,559 throughout the surgery. 25 00:01:03,583 --> 00:01:06,350 It's a delicate machine running on computer algorithms, 26 00:01:06,374 --> 00:01:09,592 and it needs special care, TLC, to keep it up and running, 27 00:01:09,616 --> 00:01:11,474 and it's going to break pretty easily. 28 00:01:11,498 --> 00:01:14,410 And when it does, it needs a team of biomedical engineers 29 00:01:14,434 --> 00:01:17,743 who understand its complexities, can fix it, can source the parts 30 00:01:17,767 --> 00:01:19,999 and keep it saving lives. 31 00:01:20,688 --> 00:01:22,222 It's a pretty expensive machine. 32 00:01:22,246 --> 00:01:24,887 It needs a hospital whose budget can allow it 33 00:01:24,911 --> 00:01:29,805 to support one machine costing upwards of 50 or $100,000. 34 00:01:30,296 --> 00:01:32,195 And perhaps most obviously, 35 00:01:32,219 --> 00:01:33,733 but also most importantly -- 36 00:01:33,757 --> 00:01:36,593 and the path to concepts that we've heard about 37 00:01:36,617 --> 00:01:37,903 kind of illustrates this -- 38 00:01:37,927 --> 00:01:43,073 it needs infrastructure that can supply an uninterrupted source of electricity, 39 00:01:43,097 --> 00:01:46,109 of compressed oxygen, and other medical supplies 40 00:01:46,133 --> 00:01:50,411 that are so critical to the functioning of this machine. 41 00:01:50,435 --> 00:01:53,734 In other words, this machine requires a lot of stuff 42 00:01:53,758 --> 00:01:55,731 that this hospital cannot offer. 43 00:01:56,332 --> 00:01:59,352 This is the electrical supply for a hospital in rural Malawi. 44 00:02:00,153 --> 00:02:01,314 In this hospital, 45 00:02:01,338 --> 00:02:04,009 there is one person qualified to deliver anesthesia, 46 00:02:04,033 --> 00:02:05,185 and she's qualified 47 00:02:05,209 --> 00:02:09,785 because she has 12, maybe 18 months of training in anesthesia. 48 00:02:09,809 --> 00:02:12,056 In the hospital and in the entire region 49 00:02:12,080 --> 00:02:14,087 there's not a single biomedical engineer. 50 00:02:14,111 --> 00:02:15,680 So when this machine breaks, 51 00:02:15,704 --> 00:02:17,913 the machines that they have to work with break, 52 00:02:17,937 --> 00:02:19,706 they've got to try and figure it out, 53 00:02:19,730 --> 00:02:22,022 but most of the time, that's the end of the road. 54 00:02:22,046 --> 00:02:24,263 Those machines go the proverbial junkyard. 55 00:02:24,287 --> 00:02:26,616 And the price tag of the machine that I mentioned 56 00:02:26,640 --> 00:02:28,701 could represent maybe a quarter or a third 57 00:02:28,725 --> 00:02:31,537 of the annual operating budget for this hospital. 58 00:02:32,580 --> 00:02:35,964 And finally, I think you can see that infrastructure is not very strong. 59 00:02:35,988 --> 00:02:38,662 This hospital is connected to a very weak power grid, 60 00:02:38,686 --> 00:02:40,468 one that goes down frequently. 61 00:02:40,492 --> 00:02:43,038 So it runs frequently, the entire hospital, 62 00:02:43,062 --> 00:02:44,485 just on a generator. 63 00:02:44,509 --> 00:02:46,665 And you can imagine, the generator breaks down 64 00:02:46,689 --> 00:02:48,001 or runs out of fuel. 65 00:02:48,361 --> 00:02:50,388 And the World Bank sees this 66 00:02:50,412 --> 00:02:53,943 and estimates that a hospital in this setting in a low-income country 67 00:02:53,967 --> 00:02:57,043 can expect up to 18 power outages per month. 68 00:02:58,658 --> 00:03:01,851 Similarly, compressed oxygen and other medical supplies 69 00:03:01,875 --> 00:03:03,057 are really a luxury, 70 00:03:03,081 --> 00:03:06,363 and can often be out of stock for months or even a year. 71 00:03:06,387 --> 00:03:09,551 So it seems crazy, but the model that we have right now 72 00:03:09,575 --> 00:03:11,780 is taking those machines that were designed 73 00:03:11,804 --> 00:03:14,087 for that first environment that I showed you 74 00:03:14,111 --> 00:03:17,975 and donating or selling them to hospitals in this environment. 75 00:03:18,861 --> 00:03:20,437 It's not just inappropriate, 76 00:03:20,461 --> 00:03:22,999 it becomes really unsafe. 77 00:03:23,516 --> 00:03:25,438 One of our partners at Johns Hopkins 78 00:03:25,462 --> 00:03:29,641 was observing surgeries in Sierra Leone about a year ago. 79 00:03:30,225 --> 00:03:33,558 And the first surgery of the day happened to be an obstetrical case. 80 00:03:33,582 --> 00:03:36,565 A woman came in, she needed an emergency C-section 81 00:03:36,589 --> 00:03:39,318 to save her life and the life of her baby. 82 00:03:39,805 --> 00:03:41,759 And everything began pretty auspiciously. 83 00:03:41,783 --> 00:03:44,036 The surgeon was on call and scrubbed in. 84 00:03:44,060 --> 00:03:45,348 The nurse was there. 85 00:03:45,372 --> 00:03:48,233 She was able to anesthetize her quickly, and it was important 86 00:03:48,257 --> 00:03:50,593 because of the emergency nature of the situation. 87 00:03:50,617 --> 00:03:52,334 And everything began well 88 00:03:52,358 --> 00:03:54,430 until the power went out. 89 00:03:55,773 --> 00:03:57,597 And now in the middle of this surgery, 90 00:03:57,621 --> 00:04:00,690 the surgeon is racing against the clock to finish his case, 91 00:04:00,714 --> 00:04:03,066 which he can do -- he's got a headlamp. 92 00:04:03,090 --> 00:04:07,151 But the nurse is literally running around a darkened operating theater 93 00:04:07,175 --> 00:04:10,130 trying to find anything she can use to anesthetize her patient, 94 00:04:10,154 --> 00:04:11,843 to keep her patient asleep. 95 00:04:11,867 --> 00:04:14,750 Because her machine doesn't work when there's no power. 96 00:04:15,437 --> 00:04:18,491 This routine surgery that many of you have probably experienced, 97 00:04:18,515 --> 00:04:22,989 and others are probably the product of, has now become a tragedy. 98 00:04:23,655 --> 00:04:26,391 And what's so frustrating is this is not a singular event; 99 00:04:26,415 --> 00:04:28,914 this happens across the developing world. 100 00:04:28,938 --> 00:04:31,891 35 million surgeries are attempted every year 101 00:04:31,915 --> 00:04:33,511 without safe anesthesia. 102 00:04:34,210 --> 00:04:37,122 My colleague, Dr. Paul Fenton, was living this reality. 103 00:04:37,146 --> 00:04:38,976 He was the chief of anesthesiology 104 00:04:39,000 --> 00:04:41,500 in a hospital in Malawi, a teaching hospital. 105 00:04:42,028 --> 00:04:43,522 He went to work every day 106 00:04:43,546 --> 00:04:45,514 in an operating theater like this one, 107 00:04:45,538 --> 00:04:48,677 trying to deliver anesthesia and teach others how to do so 108 00:04:48,701 --> 00:04:49,976 using that same equipment 109 00:04:50,000 --> 00:04:54,311 that became so unreliable, and frankly unsafe, in his hospital. 110 00:04:55,039 --> 00:04:56,819 And after umpteen surgeries 111 00:04:56,843 --> 00:04:59,411 and, you can imagine, really unspeakable tragedy, 112 00:04:59,435 --> 00:05:01,777 he just said, "That's it. I'm done. That's enough. 113 00:05:01,801 --> 00:05:03,871 There has to be something better." 114 00:05:04,229 --> 00:05:05,776 He took a walk down the hall 115 00:05:05,800 --> 00:05:09,231 to where they threw all those machines that had just crapped out on them, 116 00:05:09,255 --> 00:05:10,932 I think that's the scientific term, 117 00:05:10,956 --> 00:05:12,298 and he started tinkering. 118 00:05:12,322 --> 00:05:14,675 He took one part from here and another from there, 119 00:05:14,699 --> 00:05:17,281 and he tried to come up with a machine that would work 120 00:05:17,305 --> 00:05:18,933 in the reality that he was facing. 121 00:05:18,957 --> 00:05:20,171 And what he came up with: 122 00:05:20,195 --> 00:05:21,518 was this guy. 123 00:05:21,542 --> 00:05:25,079 The prototype for the Universal Anesthesia Machine -- 124 00:05:25,103 --> 00:05:28,400 a machine that would work and anesthetize his patients 125 00:05:28,424 --> 00:05:31,999 no matter the circumstances that his hospital had to offer. 126 00:05:32,519 --> 00:05:34,000 Here it is, back at home 127 00:05:34,024 --> 00:05:37,453 at that same hospital, developed a little further, 12 years later, 128 00:05:37,477 --> 00:05:40,657 working on patients from pediatrics to geriatrics. 129 00:05:41,137 --> 00:05:43,907 Let me show you a little bit about how this machine works. 130 00:05:43,931 --> 00:05:45,809 Voila! 131 00:05:46,452 --> 00:05:47,659 Here she is. 132 00:05:47,683 --> 00:05:48,927 When you have electricity, 133 00:05:48,951 --> 00:05:51,969 everything in this machine begins in the base. 134 00:05:51,993 --> 00:05:54,590 There's a built-in oxygen concentrator down there. 135 00:05:54,614 --> 00:05:57,743 Now you've heard me mention oxygen a few times at this point. 136 00:05:57,767 --> 00:06:01,555 Essentially, to deliver anesthesia, you want as pure oxygen as possible, 137 00:06:01,579 --> 00:06:05,002 because eventually you're going to dilute it, essentially, with the gas. 138 00:06:05,026 --> 00:06:07,462 And the mixture that the patient inhales 139 00:06:07,486 --> 00:06:09,797 needs to be at least a certain percentage oxygen 140 00:06:09,821 --> 00:06:11,507 or else it can become dangerous. 141 00:06:11,531 --> 00:06:13,449 But so in here when there's electricity, 142 00:06:13,473 --> 00:06:16,348 the oxygen concentrator takes in room air. 143 00:06:16,372 --> 00:06:19,651 Now we know room air is gloriously free, 144 00:06:19,675 --> 00:06:20,976 it is abundant, 145 00:06:21,000 --> 00:06:22,999 and it's already 21 percent oxygen. 146 00:06:23,436 --> 00:06:27,113 So all this concentrator does is take that room air in, filter it 147 00:06:27,137 --> 00:06:30,957 and send 95 percent pure oxygen up and across here, 148 00:06:30,981 --> 00:06:33,187 where it mixes with the anesthetic agent. 149 00:06:33,743 --> 00:06:37,367 Now before that mixture hits the patient's lungs, 150 00:06:37,391 --> 00:06:39,605 it's going to pass by here -- you can't see it, 151 00:06:39,629 --> 00:06:41,411 but there's an oxygen sensor here -- 152 00:06:41,435 --> 00:06:45,640 that's going to read out on this screen the percentage of oxygen being delivered. 153 00:06:46,284 --> 00:06:48,258 Now if you don't have power, 154 00:06:48,282 --> 00:06:51,888 or, God forbid, the power cuts out in the middle of a surgery, 155 00:06:51,912 --> 00:06:54,256 this machine transitions automatically, 156 00:06:54,280 --> 00:06:56,029 without even having to touch it, 157 00:06:56,053 --> 00:06:58,384 to drawing in room air from this inlet. 158 00:06:58,805 --> 00:07:00,141 Everything else is the same. 159 00:07:00,165 --> 00:07:01,761 The only difference is that now 160 00:07:01,785 --> 00:07:04,999 you're only working with 21 percent oxygen. 161 00:07:05,406 --> 00:07:08,227 Now that used to be a dangerous guessing game, 162 00:07:08,251 --> 00:07:10,637 because you only knew if you gave too little oxygen 163 00:07:10,661 --> 00:07:12,037 once something bad happened. 164 00:07:12,061 --> 00:07:14,774 But we've put a long-life battery backup on here. 165 00:07:14,798 --> 00:07:16,992 This is the only part that's battery backed up. 166 00:07:17,016 --> 00:07:20,421 But this gives control to the provider, whether there's power or not, 167 00:07:20,445 --> 00:07:22,038 because they can adjust the flows 168 00:07:22,062 --> 00:07:26,030 based on the percentage of oxygen they see that they're giving the patient. 169 00:07:26,054 --> 00:07:29,399 In both cases, whether you have power or not, 170 00:07:29,423 --> 00:07:31,463 sometimes the patient needs help breathing. 171 00:07:31,487 --> 00:07:34,688 It's just a reality of anesthesia, the lungs can be paralyzed. 172 00:07:34,712 --> 00:07:36,796 And so we've just added this manual bellows. 173 00:07:36,820 --> 00:07:39,811 We've seen surgeries for three or four hours 174 00:07:39,835 --> 00:07:41,693 to ventilate the patient on this. 175 00:07:42,424 --> 00:07:45,412 So it's a straightforward machine. 176 00:07:45,436 --> 00:07:48,341 I shudder to say simple; it's straightforward. 177 00:07:48,936 --> 00:07:50,837 And it's by design. 178 00:07:50,861 --> 00:07:56,061 You do not need to be a highly trained, specialized anesthesiologist 179 00:07:56,085 --> 00:07:57,256 to use this machine, 180 00:07:57,280 --> 00:08:00,032 which is good because, in these rural district hospitals, 181 00:08:00,056 --> 00:08:02,651 you're not going to get that level of training. 182 00:08:02,675 --> 00:08:05,803 It's also designed for the environment that it will be used in. 183 00:08:05,827 --> 00:08:07,604 This is an incredibly rugged machine. 184 00:08:07,628 --> 00:08:11,083 It has to stand up to the heat and the wear and tear 185 00:08:11,107 --> 00:08:14,225 that happens in hospitals in these rural districts. 186 00:08:14,249 --> 00:08:16,644 And so it's not going to break very easily, 187 00:08:16,668 --> 00:08:19,942 but if it does, virtually every piece in this machine 188 00:08:19,966 --> 00:08:21,976 can be swapped out and replaced 189 00:08:22,000 --> 00:08:23,994 with a hex wrench and a screwdriver. 190 00:08:25,750 --> 00:08:28,010 And finally, it's affordable. 191 00:08:28,034 --> 00:08:31,687 This machine comes in at an eighth of the cost 192 00:08:31,711 --> 00:08:34,789 of the conventional machine that I showed you earlier. 193 00:08:34,813 --> 00:08:39,301 So in other words, what we have here is a machine that can enable surgery 194 00:08:39,325 --> 00:08:40,497 and save lives, 195 00:08:40,521 --> 00:08:43,511 because it was designed for its environment, 196 00:08:43,535 --> 00:08:45,953 just like the first machine I showed you. 197 00:08:45,977 --> 00:08:47,981 But we're not content to stop there. 198 00:08:48,005 --> 00:08:49,180 Is it working? 199 00:08:49,204 --> 00:08:51,786 Is this the design that's going to work in place? 200 00:08:51,810 --> 00:08:53,577 Well, we've seen good results so far. 201 00:08:53,601 --> 00:08:56,986 This is in 13 hospitals in four countries, 202 00:08:57,010 --> 00:09:00,943 and since 2010, we've done well over 2,000 surgeries 203 00:09:00,967 --> 00:09:03,200 with no clinically adverse events. 204 00:09:03,540 --> 00:09:04,707 So we're thrilled. 205 00:09:04,731 --> 00:09:09,153 This really seems like a cost-effective, scalable solution 206 00:09:09,177 --> 00:09:11,210 to a problem that's really pervasive. 207 00:09:11,698 --> 00:09:13,298 But we still want to be sure 208 00:09:13,322 --> 00:09:15,739 that this is the most effective and safe device 209 00:09:15,763 --> 00:09:17,820 that we can be putting into hospitals. 210 00:09:17,844 --> 00:09:20,368 So to do that, we've launched a number of partnerships 211 00:09:20,392 --> 00:09:21,878 with NGOs and universities, 212 00:09:21,902 --> 00:09:24,551 to gather data on the user interface, 213 00:09:24,575 --> 00:09:26,767 on the types of surgeries it's appropriate for, 214 00:09:26,791 --> 00:09:29,178 and ways we can enhance the device itself. 215 00:09:29,202 --> 00:09:31,856 One of those partnerships is with Johns Hopkins 216 00:09:31,880 --> 00:09:33,253 just here in Baltimore. 217 00:09:33,277 --> 00:09:37,555 They have a really cool anesthesia simulation lab out in Baltimore. 218 00:09:37,579 --> 00:09:39,460 So we're taking this machine 219 00:09:39,484 --> 00:09:43,058 and recreating some of the operating theater crises 220 00:09:43,082 --> 00:09:44,589 that this machine might face 221 00:09:44,613 --> 00:09:46,976 in one of the hospitals that it's intended for, 222 00:09:47,000 --> 00:09:49,124 and in a contained, safe environment, 223 00:09:49,148 --> 00:09:50,865 evaluating its effectiveness. 224 00:09:51,347 --> 00:09:54,979 We're then able to compare the results from that study 225 00:09:55,003 --> 00:09:56,423 with real-world experience, 226 00:09:56,447 --> 00:09:58,644 because we're putting two of these in hospitals 227 00:09:58,668 --> 00:10:00,819 that Johns Hopkins works with in Sierra Leone, 228 00:10:00,843 --> 00:10:03,957 including the hospital where that emergency C-section happened. 229 00:10:05,385 --> 00:10:08,513 So I've talked a lot about anesthesia, and I tend to do that. 230 00:10:08,537 --> 00:10:12,629 I think it is incredibly fascinating and an important component of health. 231 00:10:12,653 --> 00:10:15,284 And it really seems peripheral, we never think about it, 232 00:10:15,308 --> 00:10:17,512 until we don't have access to it, 233 00:10:17,536 --> 00:10:19,498 and then it becomes a gatekeeper. 234 00:10:19,522 --> 00:10:21,491 Who gets surgery and who doesn't? 235 00:10:21,824 --> 00:10:24,554 Who gets safe surgery and who doesn't? 236 00:10:25,216 --> 00:10:27,679 But you know, it's just one of so many ways 237 00:10:27,703 --> 00:10:30,810 that design, appropriate design, 238 00:10:30,834 --> 00:10:32,810 can have an impact on health outcomes. 239 00:10:33,318 --> 00:10:35,437 If more people in the health-delivery space 240 00:10:35,461 --> 00:10:39,030 really working on some of these challenges in low-income countries 241 00:10:39,054 --> 00:10:42,707 could start their design process, their solution search, 242 00:10:42,731 --> 00:10:44,669 from outside of that proverbial box 243 00:10:44,693 --> 00:10:46,631 and inside of the hospital -- 244 00:10:46,655 --> 00:10:48,486 In other words, if we could design 245 00:10:48,510 --> 00:10:51,701 for the environment that exists in so many parts of the world, 246 00:10:51,725 --> 00:10:54,134 rather than the one that we wished existed -- 247 00:10:54,158 --> 00:10:56,324 we might just save a lot of lives. 248 00:10:56,787 --> 00:10:58,255 Thank you very much. 249 00:10:58,279 --> 00:11:02,714 (Applause)