1
00:00:00,000 --> 00:00:02,000
I'm going to talk to you today

2
00:00:02,000 --> 00:00:05,000
about the design of medical technology for low resource settings.

3
00:00:05,000 --> 00:00:07,000
I study health systems in these countries.

4
00:00:07,000 --> 00:00:09,000
And one of the major gaps in care,

5
00:00:09,000 --> 00:00:11,000
almost across the board,

6
00:00:11,000 --> 00:00:13,000
is access to safe surgery.

7
00:00:13,000 --> 00:00:16,000
Now one of the major bottlenecks that we've found

8
00:00:16,000 --> 00:00:19,000
that's sort of preventing both the access in the first place

9
00:00:19,000 --> 00:00:21,000
and the safety of those surgeries that do happen

10
00:00:21,000 --> 00:00:23,000
is anesthesia.

11
00:00:23,000 --> 00:00:25,000
And actually, it's the model that we expect to work

12
00:00:25,000 --> 00:00:27,000
for delivering anesthesia

13
00:00:27,000 --> 00:00:29,000
in these environments.

14
00:00:29,000 --> 00:00:31,000
Here we have a scene that you would find

15
00:00:31,000 --> 00:00:34,000
in any operating room across the U.S. or any other developed country.

16
00:00:34,000 --> 00:00:36,000
In the background there

17
00:00:36,000 --> 00:00:38,000
is a very sophisticated anesthesia machine.

18
00:00:38,000 --> 00:00:40,000
And this machine is able

19
00:00:40,000 --> 00:00:42,000
to enable surgery and save lives

20
00:00:42,000 --> 00:00:44,000
because it was designed

21
00:00:44,000 --> 00:00:46,000
with this environment in mind.

22
00:00:46,000 --> 00:00:49,000
In order to operate, this machine needs a number of things

23
00:00:49,000 --> 00:00:51,000
that this hospital has to offer.

24
00:00:51,000 --> 00:00:54,000
It needs an extremely well-trained anesthesiologist

25
00:00:54,000 --> 00:00:56,000
with years of training with complex machines

26
00:00:56,000 --> 00:00:59,000
to help her monitor the flows of the gas

27
00:00:59,000 --> 00:01:01,000
and keep her patients safe and anesthetized

28
00:01:01,000 --> 00:01:03,000
throughout the surgery.

29
00:01:03,000 --> 00:01:06,000
It's a delicate machine running on computer algorithms,

30
00:01:06,000 --> 00:01:09,000
and it needs special care, TLC, to keep it up and running,

31
00:01:09,000 --> 00:01:11,000
and it's going to break pretty easily.

32
00:01:11,000 --> 00:01:14,000
And when it does, it needs a team of biomedical engineers

33
00:01:14,000 --> 00:01:16,000
who understand its complexities,

34
00:01:16,000 --> 00:01:18,000
can fix it, can source the parts

35
00:01:18,000 --> 00:01:20,000
and keep it saving lives.

36
00:01:20,000 --> 00:01:22,000
It's a pretty expensive machine.

37
00:01:22,000 --> 00:01:24,000
It needs a hospital

38
00:01:24,000 --> 00:01:26,000
whose budget can allow it to support one machine

39
00:01:26,000 --> 00:01:29,000
costing upwards of 50 or $100,000.

40
00:01:29,000 --> 00:01:31,000
And perhaps most obviously,

41
00:01:31,000 --> 00:01:33,000
and perhaps most importantly --

42
00:01:33,000 --> 00:01:35,000
and the path to concepts that we've heard about

43
00:01:35,000 --> 00:01:37,000
kind of illustrate this --

44
00:01:37,000 --> 00:01:39,000
it needs infrastructure

45
00:01:39,000 --> 00:01:41,000
that can supply an uninterrupted source

46
00:01:41,000 --> 00:01:44,000
of electricity, of compressed oxygen

47
00:01:44,000 --> 00:01:46,000
and other medical supplies

48
00:01:46,000 --> 00:01:48,000
that are so critical to the functioning

49
00:01:48,000 --> 00:01:50,000
of this machine.

50
00:01:50,000 --> 00:01:53,000
In other words, this machine requires a lot of stuff

51
00:01:53,000 --> 00:01:55,000
that this hospital cannot offer.

52
00:01:55,000 --> 00:01:57,000
This is the electrical supply

53
00:01:57,000 --> 00:01:59,000
for a hospital in rural Malawi.

54
00:01:59,000 --> 00:02:01,000
In this hospital,

55
00:02:01,000 --> 00:02:03,000
there is one person qualified to deliver anesthesia,

56
00:02:03,000 --> 00:02:05,000
and she's qualified

57
00:02:05,000 --> 00:02:07,000
because she has 12, maybe 18 months

58
00:02:07,000 --> 00:02:09,000
of training in anesthesia.

59
00:02:09,000 --> 00:02:11,000
In the hospital and in the entire region

60
00:02:11,000 --> 00:02:13,000
there's not a single biomedical engineer.

61
00:02:13,000 --> 00:02:15,000
So when this machine breaks,

62
00:02:15,000 --> 00:02:17,000
the machines they have to work with break,

63
00:02:17,000 --> 00:02:20,000
they've got to try and figure it out, but most of the time, that's the end of the road.

64
00:02:20,000 --> 00:02:23,000
Those machines go the proverbial junkyard.

65
00:02:23,000 --> 00:02:26,000
And the price tag of the machine that I mentioned

66
00:02:26,000 --> 00:02:28,000
could represent maybe a quarter or a third

67
00:02:28,000 --> 00:02:30,000
of the annual operating budget

68
00:02:30,000 --> 00:02:32,000
for this hospital.

69
00:02:32,000 --> 00:02:35,000
And finally, I think you can see that infrastructure is not very strong.

70
00:02:35,000 --> 00:02:38,000
This hospital is connected to a very weak power grid,

71
00:02:38,000 --> 00:02:40,000
one that goes down frequently.

72
00:02:40,000 --> 00:02:42,000
So it runs frequently, the entire hospital,

73
00:02:42,000 --> 00:02:44,000
just on a generator.

74
00:02:44,000 --> 00:02:46,000
And you can imagine, the generator breaks down

75
00:02:46,000 --> 00:02:48,000
or runs out of fuel.

76
00:02:48,000 --> 00:02:50,000
And the World Bank sees this

77
00:02:50,000 --> 00:02:53,000
and estimates that a hospital in this setting in a low-income country

78
00:02:53,000 --> 00:02:56,000
can expect up to 18 power outages

79
00:02:56,000 --> 00:02:58,000
per month.

80
00:02:58,000 --> 00:03:00,000
Similarly compressed oxygen and other medical supplies

81
00:03:00,000 --> 00:03:02,000
are really a luxury

82
00:03:02,000 --> 00:03:04,000
and can often be out of stock

83
00:03:04,000 --> 00:03:06,000
for months or even a year.

84
00:03:06,000 --> 00:03:09,000
So it seems crazy, but the model that we have right now

85
00:03:09,000 --> 00:03:11,000
is taking those machines

86
00:03:11,000 --> 00:03:13,000
that were designed for that first environment that I showed you

87
00:03:13,000 --> 00:03:15,000
and donating or selling them

88
00:03:15,000 --> 00:03:18,000
to hospitals in this environment.

89
00:03:18,000 --> 00:03:20,000
It's not just inappropriate,

90
00:03:20,000 --> 00:03:23,000
it becomes really unsafe.

91
00:03:23,000 --> 00:03:25,000
One of our partners at Johns Hopkins

92
00:03:25,000 --> 00:03:28,000
was observing surgeries in Sierra Leone

93
00:03:28,000 --> 00:03:30,000
about a year ago.

94
00:03:30,000 --> 00:03:33,000
And the first surgery of the day happened to be an obstetrical case.

95
00:03:33,000 --> 00:03:36,000
A woman came in, she needed an emergency C-section

96
00:03:36,000 --> 00:03:39,000
to save her life and the life of her baby.

97
00:03:39,000 --> 00:03:41,000
And everything began pretty auspiciously.

98
00:03:41,000 --> 00:03:43,000
The surgeon was on call and scrubbed in.

99
00:03:43,000 --> 00:03:45,000
The nurse was there.

100
00:03:45,000 --> 00:03:47,000
She was able to anesthetize her quickly,

101
00:03:47,000 --> 00:03:50,000
and it was important because of the emergency nature of the situation.

102
00:03:50,000 --> 00:03:52,000
And everything began well

103
00:03:52,000 --> 00:03:55,000
until the power went out.

104
00:03:55,000 --> 00:03:57,000
And now in the middle of this surgery,

105
00:03:57,000 --> 00:04:00,000
the surgeon is racing against the clock to finish his case,

106
00:04:00,000 --> 00:04:02,000
which he can do -- he's got a headlamp.

107
00:04:02,000 --> 00:04:04,000
But the nurse is literally

108
00:04:04,000 --> 00:04:07,000
running around a darkened operating theater

109
00:04:07,000 --> 00:04:09,000
trying to find anything she can use to anesthetize her patient,

110
00:04:09,000 --> 00:04:11,000
to keep her patient asleep.

111
00:04:11,000 --> 00:04:14,000
Because her machine doesn't work when there's no power.

112
00:04:15,000 --> 00:04:18,000
And now this routine surgery that many of you have probably experienced,

113
00:04:18,000 --> 00:04:20,000
and others are probably the product of,

114
00:04:20,000 --> 00:04:23,000
has now become a tragedy.

115
00:04:23,000 --> 00:04:26,000
And what's so frustrating is this is not a singular event;

116
00:04:26,000 --> 00:04:28,000
this happens across the developing world.

117
00:04:28,000 --> 00:04:31,000
35 million surgeries are attempted every year

118
00:04:31,000 --> 00:04:33,000
without safe anesthesia.

119
00:04:33,000 --> 00:04:35,000
My colleague, Dr. Paul Fenton,

120
00:04:35,000 --> 00:04:37,000
was living this reality.

121
00:04:37,000 --> 00:04:39,000
He was the chief of anesthesiology

122
00:04:39,000 --> 00:04:41,000
in a hospital in Malawi, a teaching hospital.

123
00:04:41,000 --> 00:04:43,000
He went to work every day

124
00:04:43,000 --> 00:04:45,000
in an operating theater like this one,

125
00:04:45,000 --> 00:04:48,000
trying to deliver anesthesia and teach others how to do so

126
00:04:48,000 --> 00:04:50,000
using that same equipment

127
00:04:50,000 --> 00:04:52,000
that became so unreliable, and frankly unsafe,

128
00:04:52,000 --> 00:04:54,000
in his hospital.

129
00:04:54,000 --> 00:04:56,000
And after umpteen surgeries

130
00:04:56,000 --> 00:04:59,000
and, you can imagine, really unspeakable tragedy,

131
00:04:59,000 --> 00:05:01,000
he just said, "That's it. I'm done. That's enough.

132
00:05:01,000 --> 00:05:03,000
There has to be something better."

133
00:05:03,000 --> 00:05:05,000
So he took a walk down the hall

134
00:05:05,000 --> 00:05:07,000
to where they threw all those machines that had just crapped out on them --

135
00:05:07,000 --> 00:05:09,000
I think that's the scientific term --

136
00:05:09,000 --> 00:05:11,000
and he just started tinkering.

137
00:05:11,000 --> 00:05:13,000
He took one part from here and another from there,

138
00:05:13,000 --> 00:05:15,000
and he tried to come up with a machine

139
00:05:15,000 --> 00:05:18,000
that would work in the reality that he was facing.

140
00:05:18,000 --> 00:05:21,000
And what he came up with was this guy,

141
00:05:21,000 --> 00:05:24,000
the prototype for the Universal Anesthesia Machine --

142
00:05:24,000 --> 00:05:26,000
a machine that would work

143
00:05:26,000 --> 00:05:28,000
and anesthetize his patients

144
00:05:28,000 --> 00:05:32,000
no matter the circumstances that his hospital had to offer.

145
00:05:32,000 --> 00:05:34,000
Here it is back at home

146
00:05:34,000 --> 00:05:37,000
at that same hospital, developed a little further, 12 years later,

147
00:05:37,000 --> 00:05:40,000
working on patients from pediatrics to geriatrics.

148
00:05:40,000 --> 00:05:43,000
Now let me show you a little bit about how this machine works.

149
00:05:43,000 --> 00:05:45,000
Voila!

150
00:05:45,000 --> 00:05:47,000
Here she is.

151
00:05:47,000 --> 00:05:49,000
When you have electricity,

152
00:05:49,000 --> 00:05:51,000
everything in this machine begins in the base.

153
00:05:51,000 --> 00:05:54,000
There's a built-in oxygen concentrator down there.

154
00:05:54,000 --> 00:05:57,000
Now you've heard me mention oxygen a few times at this point.

155
00:05:57,000 --> 00:05:59,000
Essentially, to deliver anesthesia,

156
00:05:59,000 --> 00:06:01,000
you want as pure oxygen as possible,

157
00:06:01,000 --> 00:06:03,000
because eventually you're going to dilute it essentially

158
00:06:03,000 --> 00:06:05,000
with the gas.

159
00:06:05,000 --> 00:06:07,000
And the mixture that the patient inhales

160
00:06:07,000 --> 00:06:09,000
needs to be at least a certain percentage oxygen

161
00:06:09,000 --> 00:06:11,000
or else it can become dangerous.

162
00:06:11,000 --> 00:06:13,000
But so in here when there's electricity,

163
00:06:13,000 --> 00:06:16,000
the oxygen concentrator takes in room air.

164
00:06:16,000 --> 00:06:19,000
Now we know room air is gloriously free,

165
00:06:19,000 --> 00:06:21,000
it is abundant,

166
00:06:21,000 --> 00:06:23,000
and it's already 21 percent oxygen.

167
00:06:23,000 --> 00:06:26,000
So all this concentrator does is take that room air in, filter it

168
00:06:26,000 --> 00:06:28,000
and send 95 percent pure oxygen

169
00:06:28,000 --> 00:06:30,000
up and across here

170
00:06:30,000 --> 00:06:33,000
where it mixes with the anesthetic agent.

171
00:06:33,000 --> 00:06:35,000
Now before that mixture

172
00:06:35,000 --> 00:06:37,000
hits the patient's lungs,

173
00:06:37,000 --> 00:06:39,000
it's going to pass by here --

174
00:06:39,000 --> 00:06:41,000
you can't see it, but there's an oxygen sensor here --

175
00:06:41,000 --> 00:06:43,000
that's going to read out on this screen

176
00:06:43,000 --> 00:06:46,000
the percentage of oxygen being delivered.

177
00:06:46,000 --> 00:06:48,000
Now if you don't have power,

178
00:06:48,000 --> 00:06:51,000
or, God forbid, the power cuts out in the middle of surgery,

179
00:06:51,000 --> 00:06:53,000
this machine transitions automatically,

180
00:06:53,000 --> 00:06:55,000
without even having to touch it,

181
00:06:55,000 --> 00:06:58,000
to drawing in room air from this inlet.

182
00:06:58,000 --> 00:07:00,000
Everything else is the same.

183
00:07:00,000 --> 00:07:02,000
The only difference is that now

184
00:07:02,000 --> 00:07:05,000
you're only working with 21 percent oxygen.

185
00:07:05,000 --> 00:07:08,000
Now that used to be a dangerous guessing game,

186
00:07:08,000 --> 00:07:11,000
because you only knew if you had given too little oxygen once something bad happened.

187
00:07:11,000 --> 00:07:14,000
But we've put a long-life battery backup on here.

188
00:07:14,000 --> 00:07:16,000
This is the only part that's battery backed up.

189
00:07:16,000 --> 00:07:18,000
But this gives control to the provider,

190
00:07:18,000 --> 00:07:20,000
whether there's power or not,

191
00:07:20,000 --> 00:07:22,000
because they can adjust the flow

192
00:07:22,000 --> 00:07:25,000
based on the percentage of oxygen they see that they're giving their patient.

193
00:07:25,000 --> 00:07:27,000
In both cases,

194
00:07:27,000 --> 00:07:29,000
whether you have power or not,

195
00:07:29,000 --> 00:07:31,000
sometimes the patient needs help breathing.

196
00:07:31,000 --> 00:07:34,000
It's just a reality of anesthesia. The lungs can be paralyzed.

197
00:07:34,000 --> 00:07:36,000
And so we've just added this manual bellows.

198
00:07:36,000 --> 00:07:39,000
We've seen surgeries for three or four hours

199
00:07:39,000 --> 00:07:42,000
to ventilate the patient on this.

200
00:07:42,000 --> 00:07:45,000
So it's a straightforward machine.

201
00:07:45,000 --> 00:07:47,000
I shudder to say simple;

202
00:07:47,000 --> 00:07:49,000
it's straightforward.

203
00:07:49,000 --> 00:07:51,000
And it's by design.

204
00:07:51,000 --> 00:07:53,000
And you do not need to be

205
00:07:53,000 --> 00:07:56,000
a highly trained, specialized anesthesiologist to use this machine,

206
00:07:56,000 --> 00:07:59,000
which is good because, in these rural district hospitals,

207
00:07:59,000 --> 00:08:02,000
you're not going to get that level of training.

208
00:08:02,000 --> 00:08:05,000
It's also designed for the environment that it will be used in.

209
00:08:05,000 --> 00:08:07,000
This is an incredibly rugged machine.

210
00:08:07,000 --> 00:08:09,000
It has to stand up

211
00:08:09,000 --> 00:08:11,000
to the heat and the wear and tear that happens

212
00:08:11,000 --> 00:08:14,000
in hospitals in these rural districts.

213
00:08:14,000 --> 00:08:16,000
And so it's not going to break very easily,

214
00:08:16,000 --> 00:08:19,000
but if it does, virtually every piece in this machine

215
00:08:19,000 --> 00:08:22,000
can be swapped out and replaced

216
00:08:22,000 --> 00:08:25,000
with a hex wrench and a screwdriver.

217
00:08:25,000 --> 00:08:27,000
And finally, it's affordable.

218
00:08:27,000 --> 00:08:29,000
This machine comes in

219
00:08:29,000 --> 00:08:31,000
at an eighth of the cost

220
00:08:31,000 --> 00:08:34,000
of the conventional machine that I showed you earlier.

221
00:08:34,000 --> 00:08:37,000
So in other words, what we have here

222
00:08:37,000 --> 00:08:40,000
is a machine that can enable surgery and save lives

223
00:08:40,000 --> 00:08:43,000
because it was designed for its environment,

224
00:08:43,000 --> 00:08:45,000
just like the first machine I showed you.

225
00:08:45,000 --> 00:08:47,000
But we're not content to stop there.

226
00:08:47,000 --> 00:08:49,000
Is it working?

227
00:08:49,000 --> 00:08:51,000
Is this the design that's going to work in place?

228
00:08:51,000 --> 00:08:53,000
Well we've seen good results so far.

229
00:08:53,000 --> 00:08:56,000
This is in 13 hospitals in four countries,

230
00:08:56,000 --> 00:08:58,000
and since 2010,

231
00:08:58,000 --> 00:09:00,000
we've done well over 2,000 surgeries

232
00:09:00,000 --> 00:09:02,000
with no clinically adverse events.

233
00:09:02,000 --> 00:09:04,000
So we're thrilled.

234
00:09:04,000 --> 00:09:08,000
This really seems like a cost-effective, scalable solution

235
00:09:08,000 --> 00:09:11,000
to a problem that's really pervasive.

236
00:09:11,000 --> 00:09:13,000
But we still want to be sure

237
00:09:13,000 --> 00:09:15,000
that this is the most effective and safe device

238
00:09:15,000 --> 00:09:17,000
that we can be putting into hospitals.

239
00:09:17,000 --> 00:09:19,000
So to do that we've launched a number of partnerships

240
00:09:19,000 --> 00:09:21,000
with NGOs and universities

241
00:09:21,000 --> 00:09:24,000
to gather data on the user interface,

242
00:09:24,000 --> 00:09:26,000
on the types of surgeries it's appropriate for

243
00:09:26,000 --> 00:09:28,000
and ways we can enhance the device itself.

244
00:09:28,000 --> 00:09:30,000
One of those partnerships

245
00:09:30,000 --> 00:09:33,000
is with Johns Hopkins just here in Baltimore.

246
00:09:33,000 --> 00:09:37,000
They have a really cool anesthesia simulation lab out in Baltimore.

247
00:09:37,000 --> 00:09:39,000
So we're taking this machine

248
00:09:39,000 --> 00:09:42,000
and recreating some of the operating theater crises

249
00:09:42,000 --> 00:09:44,000
that this machine might face

250
00:09:44,000 --> 00:09:46,000
in one of the hospitals that it's intended for,

251
00:09:46,000 --> 00:09:49,000
and in a contained, safe environment,

252
00:09:49,000 --> 00:09:51,000
evaluating its effectiveness.

253
00:09:51,000 --> 00:09:54,000
We're then able to compare the results from that study

254
00:09:54,000 --> 00:09:56,000
with real world experience,

255
00:09:56,000 --> 00:09:58,000
because we're putting two of these in hospitals

256
00:09:58,000 --> 00:10:00,000
that Johns Hopkins works with in Sierra Leone,

257
00:10:00,000 --> 00:10:03,000
including the hospital where that emergency C-section happened.

258
00:10:05,000 --> 00:10:08,000
So I've talked a lot about anesthesia, and I tend to do that.

259
00:10:08,000 --> 00:10:10,000
I think it is incredibly fascinating

260
00:10:10,000 --> 00:10:12,000
and an important component of health.

261
00:10:12,000 --> 00:10:15,000
And it really seems peripheral, we never think about it,

262
00:10:15,000 --> 00:10:17,000
until we don't have access to it,

263
00:10:17,000 --> 00:10:19,000
and then it becomes a gatekeeper.

264
00:10:19,000 --> 00:10:21,000
Who gets surgery and who doesn't?

265
00:10:21,000 --> 00:10:24,000
Who gets safe surgery and who doesn't?

266
00:10:24,000 --> 00:10:27,000
But you know, it's just one of so many ways

267
00:10:27,000 --> 00:10:30,000
that design, appropriate design,

268
00:10:30,000 --> 00:10:33,000
can have an impact on health outcomes.

269
00:10:33,000 --> 00:10:35,000
If more people in the health delivery space

270
00:10:35,000 --> 00:10:38,000
really working on some of these challenges in low-income countries

271
00:10:38,000 --> 00:10:40,000
could start their design process,

272
00:10:40,000 --> 00:10:42,000
their solution search,

273
00:10:42,000 --> 00:10:44,000
from outside of that proverbial box

274
00:10:44,000 --> 00:10:46,000
and inside of the hospital --

275
00:10:46,000 --> 00:10:48,000
in other words, if we could design

276
00:10:48,000 --> 00:10:51,000
for the environment that exists in so many parts of the world,

277
00:10:51,000 --> 00:10:53,000
rather than the one that we wished existed --

278
00:10:53,000 --> 00:10:56,000
we might just save a lot of lives.

279
00:10:56,000 --> 00:10:58,000
Thank you very much.

280
00:10:58,000 --> 00:11:02,000
(Applause)