1
00:00:00,400 --> 00:00:04,479
Speaker 1: Our health sector is in trouble, short staffed, under resourced,

2
00:00:04,600 --> 00:00:08,480
Speaker 1: overwhelmed with patients who have complex and expensive needs.

3
00:00:08,720 --> 00:00:12,479
Speaker 2: Our hospitals are among the least progressive in the Western

4
00:00:12,520 --> 00:00:16,759
Speaker 2: world when it comes to digital health. Just as artificial

5
00:00:16,800 --> 00:00:21,040
Speaker 2: intelligence shows potential to cut down ADMIN that sucks the

6
00:00:21,079 --> 00:00:22,880
Speaker 2: life out of our doctors and nurses.

7
00:00:23,120 --> 00:00:26,079
Speaker 3: So what can AI do for our health system.

8
00:00:25,880 --> 00:00:28,960
Speaker 2: And what are the barriers that are preventing its uptake?

9
00:00:29,200 --> 00:00:32,360
Speaker 1: On the Business Attack sponsored by two degrees Business this week,

10
00:00:32,640 --> 00:00:36,080
Speaker 1: AI and how it could enhance public health if we

11
00:00:36,120 --> 00:00:38,480
Speaker 1: can get our digital health house in order.

12
00:00:38,720 --> 00:00:41,360
Speaker 4: When you talk to healthcare organizations across New Zealand at

13
00:00:41,400 --> 00:00:43,440
Speaker 4: the moment and just ask the question of the board

14
00:00:43,520 --> 00:00:46,200
Speaker 4: or the teams or even their chief Data and Digital officer,

15
00:00:46,760 --> 00:00:50,400
Speaker 4: do you have an AI strategy? The answer is no.

16
00:00:50,800 --> 00:00:53,240
Speaker 2: Doctor Will Reedy is on the EXTENTSI of New Zealand

17
00:00:53,360 --> 00:00:58,000
Speaker 2: leadership team and part of the consulting firm's global health team. Now, Ben,

18
00:00:58,200 --> 00:01:00,560
Speaker 2: your interview with will really get me one of the

19
00:01:00,560 --> 00:01:03,520
Speaker 2: best overviews of where we're at with AI in health

20
00:01:03,560 --> 00:01:06,759
Speaker 2: space in New Zealand. So everyone stick around for that.

21
00:01:07,080 --> 00:01:10,160
Speaker 1: First though and keeping the health theme going. The Dyson

22
00:01:10,280 --> 00:01:14,640
Speaker 1: Awards for Design Excellence are underway now, annual global awards

23
00:01:14,680 --> 00:01:18,280
Speaker 1: where British inventor Sir James Dyson searches the world for

24
00:01:18,360 --> 00:01:21,440
Speaker 1: the best young designers. The New Zealand winner has just

25
00:01:21,480 --> 00:01:23,880
Speaker 1: been announced and Peter, I believe you were one of

26
00:01:23,920 --> 00:01:24,440
Speaker 1: the judges.

27
00:01:25,319 --> 00:01:28,639
Speaker 2: Yeah, they got me on just to run the ruler

28
00:01:28,680 --> 00:01:30,760
Speaker 2: over it in terms of is this something that's going

29
00:01:30,840 --> 00:01:34,520
Speaker 2: to connect with a big audience. We had health experts

30
00:01:34,560 --> 00:01:37,120
Speaker 2: and other design experts. One of the top designers from

31
00:01:37,200 --> 00:01:40,640
Speaker 2: Dyson was a judge as well. This was the first

32
00:01:40,680 --> 00:01:43,560
Speaker 2: time I judged it, so it was really good. But

33
00:01:43,600 --> 00:01:47,280
Speaker 2: the one that won was one of the most simple

34
00:01:47,319 --> 00:01:52,200
Speaker 2: ones really, but was a design that we thought actually

35
00:01:52,360 --> 00:01:55,440
Speaker 2: had a really addressable market. No one else was doing

36
00:01:55,480 --> 00:01:59,240
Speaker 2: this and it's called the snapcap and a very simple

37
00:01:59,520 --> 00:02:05,559
Speaker 2: device that really helps frontline health professionals deal with the

38
00:02:05,640 --> 00:02:11,000
Speaker 2: containers that medicines come in, glass containers that need to

39
00:02:11,040 --> 00:02:16,240
Speaker 2: be dismantled quickly on the frontline inwards and put in

40
00:02:16,280 --> 00:02:19,320
Speaker 2: a sharpy bin to get rid of this stuff. You

41
00:02:19,400 --> 00:02:22,720
Speaker 2: need a device to pull these things apart and break

42
00:02:22,760 --> 00:02:27,000
Speaker 2: them open, and amazingly there wasn't one in existence.

43
00:02:27,320 --> 00:02:29,320
Speaker 1: Yeah, it's a really cool looking device. It looks kind

44
00:02:29,360 --> 00:02:31,560
Speaker 1: of like our twenty first century bottle opener.

45
00:02:32,160 --> 00:02:33,359
Speaker 3: Very simple in.

46
00:02:33,440 --> 00:02:39,000
Speaker 1: Design, very esthetically pleasing in design, and obviously much needed

47
00:02:39,040 --> 00:02:41,320
Speaker 1: because I think in your interview you talk about the

48
00:02:41,360 --> 00:02:46,360
Speaker 1: fact that frontline health professionals actually do get injuries from

49
00:02:46,400 --> 00:02:48,520
Speaker 1: trying to open these little glass bottles.

50
00:02:49,160 --> 00:02:52,119
Speaker 2: Yeah, they do, and it's just a reminder of how

51
00:02:52,240 --> 00:02:57,480
Speaker 2: much our frontline health workforce have to do all sorts

52
00:02:57,520 --> 00:03:01,720
Speaker 2: of little jobs you don't even think of that is

53
00:03:01,800 --> 00:03:03,480
Speaker 2: up to them. So if we can make their lives

54
00:03:03,520 --> 00:03:07,400
Speaker 2: easier and hopefully do it at a cheap price, that's great.

55
00:03:08,160 --> 00:03:08,320
Speaker 4: You know.

56
00:03:08,360 --> 00:03:11,520
Speaker 2: This was just one of about a dozen designs that

57
00:03:11,560 --> 00:03:13,720
Speaker 2: we looked at. Some of the other stuff that was

58
00:03:13,800 --> 00:03:17,840
Speaker 2: sort of like an exoskeleton that would replace a moon boot.

59
00:03:17,960 --> 00:03:22,120
Speaker 2: There was insuls that can be customizable and three D

60
00:03:22,240 --> 00:03:25,360
Speaker 2: printed for people who have problems with their feet if

61
00:03:25,360 --> 00:03:29,000
Speaker 2: they've got potentially diabetes or something like that. There was

62
00:03:29,000 --> 00:03:31,120
Speaker 2: a little sense so that you can put on the

63
00:03:31,160 --> 00:03:34,240
Speaker 2: back of your shoulder if you're out jogging on a street.

64
00:03:34,280 --> 00:03:36,840
Speaker 2: It will give you haptic feedback if it thinks that

65
00:03:36,880 --> 00:03:38,600
Speaker 2: a car is getting too close to you or is

66
00:03:38,640 --> 00:03:41,280
Speaker 2: potentially going to take you out, so great to see

67
00:03:41,280 --> 00:03:42,120
Speaker 2: that creativity.

68
00:03:42,480 --> 00:03:45,160
Speaker 1: I love the simplicity of the winning design, and listening

69
00:03:45,200 --> 00:03:49,320
Speaker 1: to your conversation with the designer, Jack Pew gave me

70
00:03:49,440 --> 00:03:52,400
Speaker 1: some really good insight into kind of how he got

71
00:03:52,400 --> 00:03:55,080
Speaker 1: there and what he was thinking and the balance of

72
00:03:55,120 --> 00:03:57,800
Speaker 1: talent and pragmatism that led him there. So let's have

73
00:03:57,840 --> 00:03:59,360
Speaker 1: a listen to that interview.

74
00:03:59,040 --> 00:04:06,080
Speaker 2: Now, Jack Pugh, Welcome to the Business of Tech, and

75
00:04:06,160 --> 00:04:11,200
Speaker 2: congratulations on winning the New Zealand Dyson Awards. You were

76
00:04:11,320 --> 00:04:15,080
Speaker 2: crowned the best design off about a dozen. I was

77
00:04:15,160 --> 00:04:18,280
Speaker 2: under the actual judging panel, so I saw all of

78
00:04:18,320 --> 00:04:22,160
Speaker 2: these dozen or so designs. Tell us a little bit

79
00:04:22,160 --> 00:04:24,400
Speaker 2: about yourself, Jack, Where are you from? How did you

80
00:04:24,440 --> 00:04:25,640
Speaker 2: get into design?

81
00:04:26,120 --> 00:04:29,200
Speaker 5: Thanks Peter. So, I'm from christ Church, originally going to

82
00:04:29,320 --> 00:04:32,159
Speaker 5: sort of come up to the Capital to study at Messy.

83
00:04:32,680 --> 00:04:35,440
Speaker 5: I've always been quite interested in design when I was little.

84
00:04:35,480 --> 00:04:38,600
Speaker 5: Any when you ask would tell you that I always

85
00:04:38,640 --> 00:04:40,400
Speaker 5: wanted to be an inventor. So it's really cool to

86
00:04:40,400 --> 00:04:42,960
Speaker 5: be able to sort of do this and have some

87
00:04:43,000 --> 00:04:43,880
Speaker 5: recognition for it.

88
00:04:44,400 --> 00:04:48,280
Speaker 2: You've won this award. Take us through your winning design.

89
00:04:48,360 --> 00:04:51,080
Speaker 2: It's called the cap Snap what did you set out

90
00:04:51,080 --> 00:04:53,280
Speaker 2: to achieve with the cap snap and what is it?

91
00:04:53,360 --> 00:04:56,240
Speaker 5: Well, it's a simple tool for a simple problem in

92
00:04:56,279 --> 00:05:00,679
Speaker 5: the theory, a medical ball opener for health professionals working

93
00:05:00,720 --> 00:05:04,120
Speaker 5: with medications where they'd look to recycle them by taking

94
00:05:04,120 --> 00:05:08,160
Speaker 5: off the aluminium crimpsy your caps, or to open these

95
00:05:08,200 --> 00:05:11,760
Speaker 5: little glass vials which old medication called ampules. There's some

96
00:05:11,800 --> 00:05:15,600
Speaker 5: safety risks associated with both, and so this tool kind

97
00:05:15,600 --> 00:05:17,320
Speaker 5: of lets them do it in a real quick and

98
00:05:17,360 --> 00:05:17,880
Speaker 5: easy way.

99
00:05:18,080 --> 00:05:21,960
Speaker 2: Yeah, it looks like a bottle opener, and essentially it

100
00:05:22,040 --> 00:05:25,640
Speaker 2: is a bottle opener. Yeah, functionally, because you know, just

101
00:05:25,680 --> 00:05:28,760
Speaker 2: to try and visualize it, and unfortunately, I've got a

102
00:05:28,800 --> 00:05:32,840
Speaker 2: lot of experience of this now visiting sick relatives in hospital.

103
00:05:32,880 --> 00:05:36,560
Speaker 2: You'll see a medicine bottle. It's got some in this

104
00:05:36,680 --> 00:05:41,400
Speaker 2: in the case, i know, immunotherapy liquid drug in it.

105
00:05:41,400 --> 00:05:45,680
Speaker 2: It's a glass bottle, it's got an aluminium cap which

106
00:05:45,760 --> 00:05:48,240
Speaker 2: is snug on it. You've got to separate those two

107
00:05:48,360 --> 00:05:53,120
Speaker 2: things to recycle these materials. You also have, as you say, amples,

108
00:05:53,160 --> 00:05:56,279
Speaker 2: these little sort of sealed glass containers that you have

109
00:05:56,360 --> 00:05:59,640
Speaker 2: to crack open literally to get the liquid out of them.

110
00:06:00,040 --> 00:06:03,039
Speaker 2: But they're really damn fiddly. And what I loved about

111
00:06:03,080 --> 00:06:07,240
Speaker 2: your design. You've got one little device that probably doesn't

112
00:06:07,279 --> 00:06:09,400
Speaker 2: cost that much to make, and it does both. It

113
00:06:09,480 --> 00:06:12,240
Speaker 2: removes that aluminum casing from the top of the glass

114
00:06:12,279 --> 00:06:15,919
Speaker 2: bottle and you can insert the ampule into the bottom

115
00:06:15,920 --> 00:06:19,239
Speaker 2: of it and crack it in half. So the big

116
00:06:19,320 --> 00:06:22,080
Speaker 2: benefit I saw from it is cutting down on the

117
00:06:22,120 --> 00:06:26,400
Speaker 2: potential for nurses and doctors to hurt themselves going through

118
00:06:26,400 --> 00:06:31,200
Speaker 2: that process cutting themselves on glass or on aluminium or

119
00:06:31,240 --> 00:06:33,560
Speaker 2: a tool trying to take that off. It does both

120
00:06:33,600 --> 00:06:34,400
Speaker 2: of those purposes.

121
00:06:34,600 --> 00:06:37,880
Speaker 5: Yeah, that's right. The ways that people are currently getting

122
00:06:37,920 --> 00:06:40,480
Speaker 5: around both those issues is they're using kind of makeshift

123
00:06:40,480 --> 00:06:44,920
Speaker 5: solutions alcohol pads to step ampules, so at least if

124
00:06:44,920 --> 00:06:47,760
Speaker 5: it does shedd in an unpredictable way, the glass will

125
00:06:47,800 --> 00:06:50,760
Speaker 5: go into the air instead. And with those aluminium's caps,

126
00:06:50,800 --> 00:06:54,040
Speaker 5: they're real fiddly. If you ever see someone tri tech

127
00:06:54,080 --> 00:06:57,240
Speaker 5: one off, they don't have good tools suited for it,

128
00:06:57,279 --> 00:06:59,880
Speaker 5: so they're using kind of four steps and kind of

129
00:07:00,160 --> 00:07:01,800
Speaker 5: straining their hands to be able to pull them off.

130
00:07:01,880 --> 00:07:04,839
Speaker 2: Yeah, it's dangerous. And you know what I love is

131
00:07:04,880 --> 00:07:08,640
Speaker 2: the simplicity of this. Take us through your your design

132
00:07:08,720 --> 00:07:12,600
Speaker 2: approach to this. This is a very specialized piece of equipment,

133
00:07:13,280 --> 00:07:17,960
Speaker 2: So what work did you go through sort of talking

134
00:07:18,000 --> 00:07:23,360
Speaker 2: to frontline health practitioners to inform the design of cap snap.

135
00:07:23,280 --> 00:07:24,920
Speaker 5: Kind of things started off just kind of as a

136
00:07:24,920 --> 00:07:27,920
Speaker 5: conversation on what are the issues, kind of what are

137
00:07:27,960 --> 00:07:32,040
Speaker 5: they doing at the moment, and then from there tried

138
00:07:32,080 --> 00:07:34,400
Speaker 5: to think of kind of a range of solutions. What

139
00:07:34,440 --> 00:07:37,000
Speaker 5: would something handheld and quick and easy look like. First,

140
00:07:37,000 --> 00:07:40,200
Speaker 5: maybe something mounted to the wall, or like a mix

141
00:07:40,240 --> 00:07:42,280
Speaker 5: of both, something that's kind of a bit of a hybrid.

142
00:07:42,440 --> 00:07:44,480
Speaker 5: And I kind of drew up some ideas and sat

143
00:07:44,520 --> 00:07:47,000
Speaker 5: down with a member of the team and we kind

144
00:07:47,000 --> 00:07:49,240
Speaker 5: of talked through some of the ideas and we ended

145
00:07:49,320 --> 00:07:53,200
Speaker 5: up deciding that something that was real quick and small

146
00:07:53,440 --> 00:07:56,400
Speaker 5: and portable to be able to be moved around if necessary,

147
00:07:56,880 --> 00:08:00,320
Speaker 5: would be the kind of the best way forward. And

148
00:08:00,360 --> 00:08:02,400
Speaker 5: then from there I kind of springboard into a bit

149
00:08:02,400 --> 00:08:07,560
Speaker 5: of antive process, found some studies overseas looking at similar things.

150
00:08:08,000 --> 00:08:11,560
Speaker 5: We kind of developed into well, it's a bottle with

151
00:08:11,640 --> 00:08:15,200
Speaker 5: a cap, how about a botopner. We'll keep it super

152
00:08:15,280 --> 00:08:17,800
Speaker 5: super easy. And then some of the issues that came

153
00:08:17,880 --> 00:08:20,440
Speaker 5: up with one size kind of we couldn't have a

154
00:08:20,440 --> 00:08:24,000
Speaker 5: one size fits all approach to have it be real functional,

155
00:08:24,240 --> 00:08:26,440
Speaker 5: So we looked at what if we had a bollopner

156
00:08:26,480 --> 00:08:28,600
Speaker 5: that could sort of adjust and through that kind of

157
00:08:28,600 --> 00:08:32,400
Speaker 5: linear motion, we could incorporate that really seamlessly into the

158
00:08:32,640 --> 00:08:33,720
Speaker 5: ampule snapping function.

159
00:08:34,080 --> 00:08:37,080
Speaker 2: It's interesting this was not, by any means the most

160
00:08:37,160 --> 00:08:41,800
Speaker 2: elaborate or sophisticated design that we came across the judging

161
00:08:41,840 --> 00:08:44,440
Speaker 2: panel for the dice and awards. There were all sorts

162
00:08:44,440 --> 00:08:50,680
Speaker 2: of quite elaborate designs that were probably required a lot

163
00:08:50,720 --> 00:08:55,920
Speaker 2: more design forinesse. But what I loved about this one

164
00:08:56,160 --> 00:08:58,400
Speaker 2: is that when I did research about it, no one

165
00:08:58,480 --> 00:09:00,360
Speaker 2: was really doing this. Why do you think no one

166
00:09:00,360 --> 00:09:02,960
Speaker 2: in the health profession has come up with something like this?

167
00:09:03,080 --> 00:09:06,640
Speaker 5: Yet you get these things like the ampules or the

168
00:09:07,280 --> 00:09:10,280
Speaker 5: crimseal caps, and then the job has to be done,

169
00:09:10,320 --> 00:09:13,160
Speaker 5: so you come up with a workaround and you kind

170
00:09:13,160 --> 00:09:15,440
Speaker 5: of that's just what you do. And then in a

171
00:09:15,440 --> 00:09:17,880
Speaker 5: good and bad way, people don't make a lot of

172
00:09:17,920 --> 00:09:21,359
Speaker 5: noise about it. And so we've got all this potential

173
00:09:21,520 --> 00:09:25,280
Speaker 5: for interesting design solutions or just simple little fixes for

174
00:09:25,360 --> 00:09:28,239
Speaker 5: these health staff we're having to do all these workarounds

175
00:09:28,280 --> 00:09:30,640
Speaker 5: on a kind of a daily basis that just sort

176
00:09:30,640 --> 00:09:32,719
Speaker 5: of someone needs to have a look at and try

177
00:09:32,760 --> 00:09:33,880
Speaker 5: to come up with something.

178
00:09:33,880 --> 00:09:37,240
Speaker 2: And that's exactly what you've done. You know, Sir James

179
00:09:37,280 --> 00:09:43,240
Speaker 2: Dyson is famous for being a real iterative designer. Did

180
00:09:43,240 --> 00:09:46,720
Speaker 2: you go through numerous iterations and either future obvious ones

181
00:09:46,840 --> 00:09:48,800
Speaker 2: you see for this device to make it even better?

182
00:09:48,960 --> 00:09:51,400
Speaker 5: Yeah, So a real fun part of the process is

183
00:09:51,440 --> 00:09:54,240
Speaker 5: going through the iterations. So we went through a few

184
00:09:54,280 --> 00:09:57,440
Speaker 5: different iterations looking at how I can keep it as

185
00:09:57,480 --> 00:10:00,840
Speaker 5: simple as possible and reduce kind of them the number

186
00:10:00,880 --> 00:10:03,920
Speaker 5: of mechanical parts in it. There are kind of two approaches.

187
00:10:04,000 --> 00:10:06,840
Speaker 5: One kind of looked like a claw, if that makes sense,

188
00:10:06,840 --> 00:10:08,719
Speaker 5: and that would allow it to be able to open

189
00:10:08,760 --> 00:10:11,480
Speaker 5: a bunch of different sizes without having to use that

190
00:10:11,559 --> 00:10:14,920
Speaker 5: slighter function. But the issue we kind of ran into

191
00:10:15,000 --> 00:10:18,680
Speaker 5: with some of those prototypes is that it wasn't sort

192
00:10:18,679 --> 00:10:21,199
Speaker 5: of snappy and intuitive. If you looked at it, you

193
00:10:21,240 --> 00:10:23,080
Speaker 5: wouldn't know what you're looking at, and that was a

194
00:10:23,120 --> 00:10:25,440
Speaker 5: real big part of the design is making it just

195
00:10:25,520 --> 00:10:27,640
Speaker 5: super straightforward that you can look at it and pretty

196
00:10:27,720 --> 00:10:30,360
Speaker 5: much figure out how to use it without any instruction.

197
00:10:30,720 --> 00:10:33,640
Speaker 5: There's so little time to teach people about new things

198
00:10:34,640 --> 00:10:37,280
Speaker 5: when a new tool comes in kind of in these busy,

199
00:10:37,320 --> 00:10:39,400
Speaker 5: bustling environments that that's kind of what you want from

200
00:10:39,400 --> 00:10:41,200
Speaker 5: a tool that's going to do just this quick and

201
00:10:41,240 --> 00:10:41,679
Speaker 5: simple job.

202
00:10:41,760 --> 00:10:43,840
Speaker 2: Yeah, Because it literally looks like a bottle opener, so

203
00:10:44,080 --> 00:10:46,719
Speaker 2: instantly you have that recognition. You go, oh, I've got

204
00:10:46,720 --> 00:10:49,400
Speaker 2: a bottle here, This must go around the neck. And

205
00:10:49,440 --> 00:10:51,320
Speaker 2: then you've got the sort of the hidden compartment on

206
00:10:51,360 --> 00:10:53,760
Speaker 2: the bottom of it, which is for breaking the ample

207
00:10:53,960 --> 00:10:55,560
Speaker 2: as well. So I guess it's a bit of education

208
00:10:55,640 --> 00:10:58,720
Speaker 2: required so that people know to use that. You're sitting

209
00:10:58,720 --> 00:11:01,480
Speaker 2: in your lab as we talk here, you've got a

210
00:11:01,480 --> 00:11:04,720
Speaker 2: three D printer behind you. Was that useful? Do you

211
00:11:04,760 --> 00:11:07,720
Speaker 2: do a lot of iterations and design work and prototyping

212
00:11:07,920 --> 00:11:08,920
Speaker 2: using three D printing?

213
00:11:09,120 --> 00:11:12,080
Speaker 5: Yeah? So three D printing was a huge enabler for

214
00:11:12,120 --> 00:11:17,200
Speaker 5: this project. That and water jet cutting was another super

215
00:11:17,360 --> 00:11:20,440
Speaker 5: useful part in sort of smashing out these prototypes. I

216
00:11:20,520 --> 00:11:23,360
Speaker 5: tried to kind of reduce the amount of plastic that

217
00:11:23,400 --> 00:11:25,280
Speaker 5: I used where I could, so I tried to make

218
00:11:25,320 --> 00:11:28,440
Speaker 5: my prototypes I can kind of hot swap between the

219
00:11:28,520 --> 00:11:32,480
Speaker 5: different inserts to try the different geometries to see what

220
00:11:32,600 --> 00:11:35,640
Speaker 5: was kind of the best and most effective fit when

221
00:11:35,800 --> 00:11:37,920
Speaker 5: kind of figuring out some of the measurements. So yeah,

222
00:11:38,000 --> 00:11:42,280
Speaker 5: definitely super pivotal and being able to quickly run through ideas.

223
00:11:42,400 --> 00:11:47,800
Speaker 2: So design was only sort of part of the criteria

224
00:11:47,880 --> 00:11:50,880
Speaker 2: for winning this award. A big part of it was

225
00:11:50,880 --> 00:11:53,680
Speaker 2: the real world application and the potential for this to

226
00:11:53,720 --> 00:11:56,800
Speaker 2: actually go on and be used. It's all well and

227
00:11:56,840 --> 00:11:59,959
Speaker 2: good to design something that just never leaves the labor

228
00:12:00,080 --> 00:12:02,320
Speaker 2: with the prototyping stage, and we did see some sort

229
00:12:02,360 --> 00:12:05,320
Speaker 2: of designs like that. What are the next steps for you?

230
00:12:05,440 --> 00:12:07,679
Speaker 2: Is this something that you'd like to pursue potentially as

231
00:12:07,679 --> 00:12:10,000
Speaker 2: a business that the Snapcap try and get it out

232
00:12:10,000 --> 00:12:12,520
Speaker 2: there into hospitals and clinics around the world.

233
00:12:12,559 --> 00:12:15,520
Speaker 5: I would like to see it in people's hands and

234
00:12:15,640 --> 00:12:18,120
Speaker 5: just making their lives a little bit easier for those tasks.

235
00:12:18,120 --> 00:12:19,960
Speaker 5: So I'm able to put it a little bit of

236
00:12:19,960 --> 00:12:22,480
Speaker 5: time in to be able to work up the design

237
00:12:22,600 --> 00:12:25,360
Speaker 5: a little bit further. I've got some help from some

238
00:12:25,400 --> 00:12:29,199
Speaker 5: awesome people at a couple of the other hospitals around

239
00:12:29,200 --> 00:12:32,439
Speaker 5: the country been able to kind of cast the net

240
00:12:32,480 --> 00:12:36,120
Speaker 5: a little bit wider see how the same issues are

241
00:12:36,200 --> 00:12:39,080
Speaker 5: sort of getting received across those different sites. We're hoping

242
00:12:39,120 --> 00:12:42,120
Speaker 5: to be able to work it into something that we

243
00:12:42,160 --> 00:12:43,760
Speaker 5: can get into people's hands.

244
00:12:43,960 --> 00:12:46,160
Speaker 2: The judges we're talking about this, we sort of thought

245
00:12:46,880 --> 00:12:49,240
Speaker 2: this is great, and you submitted a video literally off

246
00:12:49,880 --> 00:12:53,440
Speaker 2: a nurse who was I think snapping an ampool who

247
00:12:53,480 --> 00:12:55,440
Speaker 2: actually cut herself.

248
00:12:55,679 --> 00:12:58,320
Speaker 5: It wasn't scripted, but we had to use that tape.

249
00:12:58,440 --> 00:13:00,800
Speaker 2: Well, that illustrates it very well. But we were thinking,

250
00:13:01,440 --> 00:13:03,960
Speaker 2: why don't you just I mean, you need to break

251
00:13:04,000 --> 00:13:07,720
Speaker 2: the ampull, but in terms of taking the aluminum cap

252
00:13:07,880 --> 00:13:09,640
Speaker 2: off the bottle, why don't you just chuck it all

253
00:13:09,760 --> 00:13:12,120
Speaker 2: in a bin and automate that later.

254
00:13:12,440 --> 00:13:14,800
Speaker 5: That was an idea that we kind of tossed around

255
00:13:14,840 --> 00:13:18,880
Speaker 5: at the start. But the kind of reality of kind

256
00:13:18,920 --> 00:13:20,920
Speaker 5: of this product is that it'd be all well and

257
00:13:20,960 --> 00:13:23,800
Speaker 5: good to make something real, awesome and automated, and I

258
00:13:23,880 --> 00:13:26,880
Speaker 5: actually going into this project that's kind of what I

259
00:13:26,920 --> 00:13:29,079
Speaker 5: was going to look to do. But then talking with

260
00:13:29,600 --> 00:13:32,319
Speaker 5: the people and like hearing about the issues and then

261
00:13:33,040 --> 00:13:36,640
Speaker 5: learning about the ampuel side of things, we've sort of

262
00:13:36,640 --> 00:13:39,880
Speaker 5: determined that while technically it could be considered a band

263
00:13:39,920 --> 00:13:42,800
Speaker 5: aid solution, it would be on the fastest track to

264
00:13:42,840 --> 00:13:45,680
Speaker 5: be able to have this issue kind of be sold.

265
00:13:45,720 --> 00:13:48,120
Speaker 2: That's what we loved about it, the simplicity of it.

266
00:13:48,200 --> 00:13:51,280
Speaker 2: I mean, presumably this, if you get this into production,

267
00:13:51,320 --> 00:13:54,120
Speaker 2: it wouldn't be a super expensive device to create either.

268
00:13:54,360 --> 00:13:57,560
Speaker 5: Yeah, we're hoping to well, we're working to get the

269
00:13:57,600 --> 00:14:01,360
Speaker 5: part count down as much as possible. Engineers in christ

270
00:14:01,440 --> 00:14:04,120
Speaker 5: Church I've had some great input on on clever ways

271
00:14:04,120 --> 00:14:08,199
Speaker 5: to really simplify that mechanism to even just three or

272
00:14:08,200 --> 00:14:08,760
Speaker 5: four parts.

273
00:14:09,080 --> 00:14:09,440
Speaker 3: Tops.

274
00:14:09,840 --> 00:14:11,040
Speaker 2: Where are you working at the moment?

275
00:14:11,320 --> 00:14:15,440
Speaker 5: So I'm working out of Wellington Regional Hospital, part of

276
00:14:15,480 --> 00:14:19,360
Speaker 5: the Futtow Water Improvement team based out of here, so

277
00:14:19,680 --> 00:14:22,120
Speaker 5: day to day I'm sort of looking at other issues,

278
00:14:22,160 --> 00:14:24,800
Speaker 5: but it's still we're able to work on some innovative

279
00:14:24,800 --> 00:14:26,800
Speaker 5: solutions sort of within the hospital, which is a really

280
00:14:26,800 --> 00:14:28,120
Speaker 5: exciting sort of opportunity.

281
00:14:28,200 --> 00:14:30,480
Speaker 2: Well, brilliant, you're in exactly the right place and they're

282
00:14:30,520 --> 00:14:34,240
Speaker 2: lucky to have this design brain on the team there.

283
00:14:34,320 --> 00:14:38,000
Speaker 2: So good luck for the next phase. And he's hoping

284
00:14:38,040 --> 00:14:41,000
Speaker 2: we see snapcap in hospital wards before too long.

285
00:14:41,080 --> 00:14:48,160
Speaker 1: Well hat fingers crossed, great young talent, Jack Pugh. Great

286
00:14:48,200 --> 00:14:51,000
Speaker 1: to have those kinds of people coming through New Zealand,

287
00:14:51,080 --> 00:14:54,680
Speaker 1: so big well done to him and look forward to

288
00:14:54,680 --> 00:14:58,000
Speaker 1: seeing what he does next. So that's innovation and health hardware,

289
00:14:58,200 --> 00:15:01,440
Speaker 1: and our topic of focus this week is AI in healthcare.

290
00:15:02,120 --> 00:15:05,400
Speaker 1: Doctor Will Reedy is one of the country's leading experts

291
00:15:05,440 --> 00:15:08,480
Speaker 1: in digital health. He joined Accentua around a year ago

292
00:15:08,560 --> 00:15:11,040
Speaker 1: and is still a practicing doctor one day a week

293
00:15:11,240 --> 00:15:12,840
Speaker 1: in the county's Monaco area.

294
00:15:13,000 --> 00:15:15,840
Speaker 2: He's a lot of experience helping with the rollout of

295
00:15:15,960 --> 00:15:19,520
Speaker 2: digital health systems all over the world, so he's pretty

296
00:15:19,520 --> 00:15:24,200
Speaker 2: well placed to compare and contrast our preparedness and progress

297
00:15:24,280 --> 00:15:27,320
Speaker 2: in the digital health space compared to the likes of Europe,

298
00:15:27,320 --> 00:15:29,920
Speaker 2: the US and Australia, where he's also worked.

299
00:15:30,240 --> 00:15:33,520
Speaker 3: Will's also really interested in the potential to reduce health

300
00:15:33,560 --> 00:15:36,600
Speaker 3: inequities using digital health tech, that is, if we can

301
00:15:36,640 --> 00:15:39,720
Speaker 3: trust machine learning and large language models to get it right.

302
00:15:40,160 --> 00:15:44,920
Speaker 2: So here's Ben's interview with Accentures doctor Will Reedy.

303
00:15:50,880 --> 00:15:52,800
Speaker 3: Good, Hey, Will, how are you great? Thanks?

304
00:15:52,840 --> 00:15:55,000
Speaker 1: Ben good, Welcome to the Business of Tech podcast. Thanks

305
00:15:55,040 --> 00:15:57,440
Speaker 1: for joining us. As so, why don't we start with

306
00:15:57,440 --> 00:16:00,680
Speaker 1: if you could just give us a very quick summary of.

307
00:16:00,680 --> 00:16:02,320
Speaker 3: Who you are, what you do, and a little bit

308
00:16:02,320 --> 00:16:03,160
Speaker 3: about your background.

309
00:16:03,280 --> 00:16:05,280
Speaker 4: So yeah, look, it's pleasure to be chatting with you

310
00:16:05,320 --> 00:16:08,600
Speaker 4: this morning. So I guess my full time job is

311
00:16:08,640 --> 00:16:12,040
Speaker 4: working for Accenture, is their how order health and wellness

312
00:16:12,120 --> 00:16:15,200
Speaker 4: lead here in New Zealand with the goal to help

313
00:16:15,240 --> 00:16:17,880
Speaker 4: the health system transform given some of the real challenges

314
00:16:18,120 --> 00:16:19,720
Speaker 4: that many of us aware of at the moment in

315
00:16:19,760 --> 00:16:23,040
Speaker 4: the health system. And then one day a week I

316
00:16:23,120 --> 00:16:26,000
Speaker 4: do a shift in the surgical services at Middlemore so

317
00:16:26,040 --> 00:16:28,880
Speaker 4: that can be in the emergency department and the clinics,

318
00:16:28,880 --> 00:16:31,200
Speaker 4: in the wards and sometimes in theater. So I guess

319
00:16:31,200 --> 00:16:33,840
Speaker 4: that keeps it real in terms of understanding the pulse

320
00:16:33,880 --> 00:16:36,800
Speaker 4: of the health system at the clinical front line. But

321
00:16:36,880 --> 00:16:39,240
Speaker 4: I guess ultimately the passion is to try and transform

322
00:16:39,240 --> 00:16:41,960
Speaker 4: health systems with technology and things like AI.

323
00:16:42,400 --> 00:16:46,400
Speaker 1: It must be quite start going from in Accenture talking

324
00:16:46,440 --> 00:16:49,440
Speaker 1: about the latest and greatest worldwide AI this, and then

325
00:16:49,440 --> 00:16:53,440
Speaker 1: you go into a hospital and very different story.

326
00:16:53,440 --> 00:16:54,800
Speaker 3: I'd imagine, Yeah, it is.

327
00:16:54,960 --> 00:16:58,040
Speaker 4: It's really interesting and it's generally accepted that in our

328
00:16:58,120 --> 00:17:00,920
Speaker 4: hospital system, not so much in our GP systems or

329
00:17:00,960 --> 00:17:04,560
Speaker 4: primary care systems. We are the least digitized hospitals now

330
00:17:04,600 --> 00:17:06,959
Speaker 4: in the developed world. So it is a little bit

331
00:17:07,000 --> 00:17:09,399
Speaker 4: of a change in terms of the global work that

332
00:17:09,480 --> 00:17:12,879
Speaker 4: I do and seeing what's going on in terms of

333
00:17:12,960 --> 00:17:15,760
Speaker 4: transformation and how far some other countries are ahead of us.

334
00:17:16,200 --> 00:17:19,560
Speaker 4: Also being quite digital my day job at Accenture and

335
00:17:19,600 --> 00:17:22,080
Speaker 4: then you know, having to use a pen and paper

336
00:17:22,080 --> 00:17:23,160
Speaker 4: on Fridays pretty much.

337
00:17:23,240 --> 00:17:23,960
Speaker 3: Yeah.

338
00:17:24,240 --> 00:17:27,639
Speaker 1: But also I guess conversely, that also just helps you

339
00:17:27,680 --> 00:17:30,400
Speaker 1: to understand the potential for change, right, and that actually

340
00:17:30,400 --> 00:17:33,520
Speaker 1: how far we can go when we start to implement

341
00:17:33,520 --> 00:17:34,040
Speaker 1: modern tech.

342
00:17:34,200 --> 00:17:36,160
Speaker 4: Yeah. I think it's easy to kind of look at

343
00:17:36,160 --> 00:17:39,800
Speaker 4: the I guess, the lower level of digitization and tech

344
00:17:39,880 --> 00:17:44,000
Speaker 4: enable transformation in the New Zealand health system today. But

345
00:17:44,040 --> 00:17:47,240
Speaker 4: it's also an opportunity, and my words are an opportunity

346
00:17:47,280 --> 00:17:49,399
Speaker 4: to kind of leap frog some of the approaches and

347
00:17:49,440 --> 00:17:52,639
Speaker 4: the thinking. So I do I'm quite optimistic about some

348
00:17:52,680 --> 00:17:54,560
Speaker 4: of those opportunities for New Zealand just to go, hey,

349
00:17:54,560 --> 00:17:57,000
Speaker 4: where are we trying to get to? What would we

350
00:17:57,040 --> 00:17:59,080
Speaker 4: do differently? Could we leap frog ahead of some of

351
00:17:59,080 --> 00:18:00,000
Speaker 4: the other jurisdictions.

352
00:18:00,119 --> 00:18:01,960
Speaker 1: Yeah, it's interesting you use that term leap frog. That

353
00:18:01,960 --> 00:18:05,000
Speaker 1: seems to be kind of a relatively common New Zealand

354
00:18:05,000 --> 00:18:07,359
Speaker 1: experience where we kind of fall behind a little bit

355
00:18:07,359 --> 00:18:09,399
Speaker 1: and then we go, oh, let's catch up, and in

356
00:18:09,480 --> 00:18:13,160
Speaker 1: doing so we kind of go ahead and really hit

357
00:18:13,200 --> 00:18:15,280
Speaker 1: that cutting edge again. Is that kind of what you're

358
00:18:15,280 --> 00:18:16,560
Speaker 1: seeing happening at the moment?

359
00:18:17,480 --> 00:18:19,919
Speaker 4: I guess, I see the opportunity and it's really interesting

360
00:18:19,920 --> 00:18:23,479
Speaker 4: to share with you. It's funny how some of the

361
00:18:23,520 --> 00:18:28,399
Speaker 4: AI technologies are becoming quite pervasive in healthcare. And a

362
00:18:28,440 --> 00:18:31,440
Speaker 4: colleague of mine has been leading the way in gp

363
00:18:31,640 --> 00:18:34,880
Speaker 4: Land all it or primary care in driving the adoption

364
00:18:35,000 --> 00:18:37,800
Speaker 4: of a product called Nabler, which allows you, with the

365
00:18:37,840 --> 00:18:41,080
Speaker 4: permission of the patient, to record the voice around the

366
00:18:41,119 --> 00:18:44,199
Speaker 4: interaction with the patient and then convert that to text

367
00:18:44,320 --> 00:18:46,840
Speaker 4: and then put it into the GP system. And our

368
00:18:46,880 --> 00:18:49,280
Speaker 4: GPS I think about ranked about third in the world

369
00:18:49,359 --> 00:18:52,600
Speaker 4: in terms of their levels of digitization. And that's been

370
00:18:52,880 --> 00:18:55,040
Speaker 4: kind of the same kind of I guess measurement that's

371
00:18:55,040 --> 00:18:57,239
Speaker 4: been in place for about twenty years. So primary care

372
00:18:57,280 --> 00:18:59,640
Speaker 4: in New Zealand's actually been I guess at the forefront

373
00:18:59,640 --> 00:19:02,600
Speaker 4: of tech for some time. And then you know, what

374
00:19:02,760 --> 00:19:05,400
Speaker 4: he's seeing is first and foremost the patients and they're

375
00:19:05,560 --> 00:19:08,080
Speaker 4: farno who are in the room with him, are seeing

376
00:19:08,119 --> 00:19:11,320
Speaker 4: some benefits around more eye contact not turning around and

377
00:19:11,320 --> 00:19:13,480
Speaker 4: typing stuff in the computer and in terms of the

378
00:19:13,480 --> 00:19:17,800
Speaker 4: fifteen minute consult that is common in primary care. The

379
00:19:17,840 --> 00:19:20,840
Speaker 4: other side that he's finding really interesting is efficiencies within

380
00:19:20,880 --> 00:19:24,040
Speaker 4: the consult around typing everything down because we often paraphrase

381
00:19:24,119 --> 00:19:26,520
Speaker 4: what the family or the patients say to us given

382
00:19:26,680 --> 00:19:29,640
Speaker 4: the time limit. But what he's also finding, as our

383
00:19:29,720 --> 00:19:32,320
Speaker 4: colleagues across about one dred and one hundred and fifty

384
00:19:32,320 --> 00:19:35,719
Speaker 4: practices in New Zealand, is that the cognitive load, that

385
00:19:35,760 --> 00:19:38,520
Speaker 4: the mental load is a lot less because you're having

386
00:19:38,560 --> 00:19:41,840
Speaker 4: to recite and think things through. So piece of technology

387
00:19:41,880 --> 00:19:47,040
Speaker 4: came along relatively recently and it's been adopted in about

388
00:19:47,080 --> 00:19:49,720
Speaker 4: three hundred practices across New Zealand, which is about fifteen

389
00:19:49,760 --> 00:19:52,600
Speaker 4: percent of the GP practices. So it's really interesting around

390
00:19:52,640 --> 00:19:55,640
Speaker 4: that leap frog concept. Grab an idea and go with it. Yep.

391
00:19:56,080 --> 00:19:57,639
Speaker 1: It is an interesting idea and it is one that

392
00:19:57,720 --> 00:20:02,000
Speaker 1: has you know, it could have been earlier probably, but

393
00:20:02,160 --> 00:20:04,240
Speaker 1: the reality is is that the technology that we have

394
00:20:04,359 --> 00:20:07,280
Speaker 1: now around AI has just made it super accessible.

395
00:20:08,160 --> 00:20:08,960
Speaker 3: Are there other.

396
00:20:08,880 --> 00:20:11,800
Speaker 1: Areas that you're seeing that trend that the modern AI

397
00:20:12,119 --> 00:20:13,720
Speaker 1: tools that we have now in the last couple of

398
00:20:13,800 --> 00:20:17,760
Speaker 1: years have made things possible suddenly that would have seemed

399
00:20:17,760 --> 00:20:19,040
Speaker 1: really onerous in the past.

400
00:20:19,280 --> 00:20:22,280
Speaker 4: Yeah, look, it's interesting. I'll probably give some broader contexts.

401
00:20:22,320 --> 00:20:24,719
Speaker 4: So one of the challenges in the health system at

402
00:20:24,720 --> 00:20:26,840
Speaker 4: the moment is how do you adopt AI? And I

403
00:20:26,880 --> 00:20:30,720
Speaker 4: guess people translate AI to generative AI at the moment,

404
00:20:30,760 --> 00:20:33,520
Speaker 4: so it's just careful to be specific about that. And

405
00:20:33,560 --> 00:20:37,560
Speaker 4: so the adoption piece is and I'll get down to

406
00:20:37,600 --> 00:20:40,639
Speaker 4: some of the use cases shortly but effectively, when you

407
00:20:40,680 --> 00:20:43,320
Speaker 4: talk to healthcare organizations across New Zealand at the moment,

408
00:20:43,359 --> 00:20:45,360
Speaker 4: and just ask the question of the board or their

409
00:20:45,359 --> 00:20:47,800
Speaker 4: exec teams or even their chief Data and Digital officer,

410
00:20:48,600 --> 00:20:52,800
Speaker 4: do you have an AI strategy? And the answer is no,

411
00:20:53,200 --> 00:20:55,760
Speaker 4: which is really interesting. And then the second question you

412
00:20:55,840 --> 00:20:58,080
Speaker 4: ask in the context of adoption of AI, if you

413
00:20:58,119 --> 00:21:00,720
Speaker 4: went down that path, is do you have a policy

414
00:21:01,040 --> 00:21:05,600
Speaker 4: around how you'll adopt AI as an organization? And generally

415
00:21:05,640 --> 00:21:09,040
Speaker 4: speaking not many have that either. But then you go, okay,

416
00:21:09,040 --> 00:21:10,800
Speaker 4: put that to one size. You don't have a strategy,

417
00:21:10,800 --> 00:21:12,880
Speaker 4: you don't have a policy. What are the core use

418
00:21:12,920 --> 00:21:14,960
Speaker 4: cases that you've been thinking about that you'd like to

419
00:21:14,960 --> 00:21:17,960
Speaker 4: get into your organization? In the next twelve months and

420
00:21:18,320 --> 00:21:21,239
Speaker 4: again that's where it opens up interesting conversations. And so

421
00:21:21,600 --> 00:21:23,920
Speaker 4: when we ran a leadership summit earlier in the year

422
00:21:23,960 --> 00:21:26,159
Speaker 4: with the Chief Medical Officer for Health New Zealand and

423
00:21:26,160 --> 00:21:28,440
Speaker 4: the head of the AI Advisory Group, the most common

424
00:21:28,520 --> 00:21:31,840
Speaker 4: use case was surfacing genitive AI experiences to patients or

425
00:21:31,840 --> 00:21:35,640
Speaker 4: family or FANO, so things like education CHAP as an example,

426
00:21:35,680 --> 00:21:38,560
Speaker 4: I've just been diagnosed with diabetes. What can I expect

427
00:21:38,880 --> 00:21:43,159
Speaker 4: and how do you get repeatable advice to patients where

428
00:21:43,200 --> 00:21:45,680
Speaker 4: doctor or nurse isn't available as an example.

429
00:21:45,720 --> 00:21:48,560
Speaker 1: That's kind of very similar to the recent announcement around

430
00:21:48,920 --> 00:21:52,960
Speaker 1: GOVGBT right where it's the ingesting a bunch of government documents,

431
00:21:53,640 --> 00:21:58,200
Speaker 1: government pages and then being able to chatbot style ask

432
00:21:58,320 --> 00:22:00,720
Speaker 1: questions and get information about this governments.

433
00:22:00,920 --> 00:22:02,560
Speaker 4: And I think I think in New Zealand, you know,

434
00:22:02,960 --> 00:22:05,760
Speaker 4: the health systems around the world, particularly in socialized health

435
00:22:05,760 --> 00:22:09,400
Speaker 4: systems like New Zealand, Australia in the UK, is everything's

436
00:22:09,440 --> 00:22:13,879
Speaker 4: quite fragmented. So if you've got a consistent education piece

437
00:22:14,560 --> 00:22:17,240
Speaker 4: experience for patients where no matter what question they ask,

438
00:22:17,280 --> 00:22:20,680
Speaker 4: they are a consistent answer, it's actually a big benefit

439
00:22:20,720 --> 00:22:24,159
Speaker 4: in terms of patients being empowered to manage a chronic

440
00:22:24,960 --> 00:22:28,000
Speaker 4: I guess condition like diabetes. So yeah, we are quite

441
00:22:28,040 --> 00:22:30,480
Speaker 4: surprised that everybody is going, how do we surface stuff

442
00:22:30,520 --> 00:22:33,480
Speaker 4: to patients? Which is really interesting, So that's open up

443
00:22:33,480 --> 00:22:36,040
Speaker 4: a new world. The second kind of area, broadly was

444
00:22:36,240 --> 00:22:38,679
Speaker 4: what we call the clinical or the front line or

445
00:22:38,680 --> 00:22:41,280
Speaker 4: the front of office, and so lots of use cases

446
00:22:41,320 --> 00:22:45,360
Speaker 4: around voice detext and reducing the burden of me using

447
00:22:45,359 --> 00:22:50,920
Speaker 4: pen and paper as an example, some benefits around managing inboxes,

448
00:22:50,920 --> 00:22:53,800
Speaker 4: around lab results coming in because a lot of lab

449
00:22:53,840 --> 00:22:55,960
Speaker 4: tests that we order for patients, we're kind of trying

450
00:22:56,000 --> 00:22:57,919
Speaker 4: to rule something out and we want it to be

451
00:22:58,720 --> 00:23:00,720
Speaker 4: if it comes back normal, then we don't really need

452
00:23:00,800 --> 00:23:03,600
Speaker 4: to process that forget where I'm coming from. And then

453
00:23:03,600 --> 00:23:06,440
Speaker 4: the back of office piece around workforce management and finance,

454
00:23:06,520 --> 00:23:08,960
Speaker 4: procurement and supply chain. So those are the broad areas.

455
00:23:09,240 --> 00:23:11,480
Speaker 4: So the reason I explain those broad areas around use

456
00:23:11,520 --> 00:23:15,720
Speaker 4: cases is people are understanding there is potential to apply

457
00:23:16,320 --> 00:23:19,080
Speaker 4: in particular generative AI to those use cases. It's just

458
00:23:19,280 --> 00:23:21,280
Speaker 4: where are they going to get the biggest impact around

459
00:23:21,280 --> 00:23:24,200
Speaker 4: healthcare in New Zealand. So I guess your question was, Hey,

460
00:23:25,400 --> 00:23:28,479
Speaker 4: are people's eyes being opened up. Yes, they are because

461
00:23:28,520 --> 00:23:32,200
Speaker 4: they're learning around use cases against offshore at the moment

462
00:23:32,240 --> 00:23:34,560
Speaker 4: and going, hey, that could easily be applied here in

463
00:23:34,560 --> 00:23:35,520
Speaker 4: the New Zealand context.

464
00:23:35,680 --> 00:23:39,240
Speaker 1: Yeah, sticking with the generative AI theme, you know, I've

465
00:23:39,480 --> 00:23:42,080
Speaker 1: often looked at the health of Fire website as it's

466
00:23:42,119 --> 00:23:44,000
Speaker 1: now called, and just that that's such a huge corpus

467
00:23:44,280 --> 00:23:46,720
Speaker 1: of information and data, like it seems like a great

468
00:23:46,800 --> 00:23:51,720
Speaker 1: opportunity for something like an educational chatbot, But there is

469
00:23:51,760 --> 00:23:54,600
Speaker 1: a lot of risk with that. Where chatbots are known

470
00:23:54,640 --> 00:23:57,280
Speaker 1: to want to please people, they're known to kind of

471
00:23:57,880 --> 00:24:00,360
Speaker 1: sometimes make things up if they're relying heavily on those

472
00:24:00,520 --> 00:24:04,560
Speaker 1: LLAM models in the background. How are people in the

473
00:24:04,600 --> 00:24:08,400
Speaker 1: health sector thinking about those risks considering the sensitivity.

474
00:24:08,880 --> 00:24:12,600
Speaker 4: Yeah, it's a really good question. I think the interesting

475
00:24:12,640 --> 00:24:18,760
Speaker 4: context is do healthcare organizations need to adopt genai or not?

476
00:24:19,080 --> 00:24:22,399
Speaker 4: And the general trend overseas is they're all adopting it

477
00:24:22,760 --> 00:24:25,800
Speaker 4: to see what the potential is, but not necessarily to

478
00:24:25,920 --> 00:24:29,520
Speaker 4: roll it out at scale. And the reason they're doing

479
00:24:29,560 --> 00:24:32,119
Speaker 4: that is it a competitive advantage? Does it deliver a

480
00:24:32,200 --> 00:24:34,119
Speaker 4: better service? Those types of things. So one of the

481
00:24:34,119 --> 00:24:36,639
Speaker 4: things I've seen In New Zealand. We have six and

482
00:24:36,680 --> 00:24:39,880
Speaker 4: a half thousand healthcare organizations, of which one is Health

483
00:24:39,920 --> 00:24:43,080
Speaker 4: New Zealand. It is the biggest, but that's the wider context.

484
00:24:43,119 --> 00:24:48,600
Speaker 4: So organizations need to think about AI from a responsible perspective.

485
00:24:48,640 --> 00:24:51,600
Speaker 4: And the biggest concern slash barrier is exactly what you

486
00:24:51,680 --> 00:24:55,680
Speaker 4: just articulated, which is hallucinations. I think the soft words

487
00:24:55,680 --> 00:24:58,960
Speaker 4: are unreliable outputs, and so I think a lot of

488
00:24:58,960 --> 00:25:02,600
Speaker 4: that has to be un understood. Choosing our use case,

489
00:25:03,560 --> 00:25:06,480
Speaker 4: learning about the use case, having the governance and leadership

490
00:25:06,680 --> 00:25:09,040
Speaker 4: in place to go. Actually, we've tried this in a

491
00:25:09,440 --> 00:25:12,560
Speaker 4: small use case. We have actually looked at what's happening

492
00:25:12,560 --> 00:25:15,199
Speaker 4: overseas and these use cases do actually add value, but

493
00:25:15,240 --> 00:25:17,600
Speaker 4: you'd got to go on the journey around I guess

494
00:25:17,600 --> 00:25:20,560
Speaker 4: the hallucination side of things. The other thing that's interesting

495
00:25:20,560 --> 00:25:23,280
Speaker 4: in New Zealand, in Health New Zealand as an example

496
00:25:23,280 --> 00:25:24,920
Speaker 4: of starting to do it, is they need to get

497
00:25:24,960 --> 00:25:28,840
Speaker 4: all their data in one place to run the GENAI

498
00:25:29,160 --> 00:25:31,560
Speaker 4: lms across the top of it. And so there's definitely

499
00:25:32,240 --> 00:25:34,520
Speaker 4: one that's the strategy for Health New Zealand, and they've

500
00:25:34,520 --> 00:25:37,679
Speaker 4: started investing in something called National data platform, so they

501
00:25:37,720 --> 00:25:39,480
Speaker 4: can put their data all in one place, whether it's

502
00:25:39,480 --> 00:25:43,360
Speaker 4: clinical data, workforce data, finance data, and then they can

503
00:25:43,400 --> 00:25:47,320
Speaker 4: start training the GENAI tools on top of their own

504
00:25:47,400 --> 00:25:50,320
Speaker 4: data and then managing that piece. You still have hallucinations,

505
00:25:50,359 --> 00:25:52,680
Speaker 4: don't get me wrong around data quality, but at least

506
00:25:52,680 --> 00:25:55,639
Speaker 4: you're doing it on the data that you have governance

507
00:25:55,920 --> 00:25:58,800
Speaker 4: over a couple of things to share with you. In

508
00:25:58,880 --> 00:26:03,600
Speaker 4: terms of New Zealand's text for GENAI adoption, mass of

509
00:26:03,680 --> 00:26:08,320
Speaker 4: concerns around liability. So if I have run a GENAI

510
00:26:08,400 --> 00:26:10,159
Speaker 4: tool and I've surfaced it to a patient and it's

511
00:26:10,200 --> 00:26:12,920
Speaker 4: given them some advice and something doesn't go as well

512
00:26:12,920 --> 00:26:14,560
Speaker 4: as it would have liked, and there's more risk in

513
00:26:14,680 --> 00:26:18,560
Speaker 4: terms of patient care, who's liable Is it the clinician

514
00:26:18,640 --> 00:26:20,880
Speaker 4: or is it the GENAI And how does it actually work?

515
00:26:20,920 --> 00:26:23,560
Speaker 4: So he's a little bit of maturity around what I

516
00:26:23,600 --> 00:26:26,520
Speaker 4: would call regulation and compliance to think through, because at

517
00:26:26,560 --> 00:26:30,560
Speaker 4: the end of the day, most healthyic organizations have a

518
00:26:30,600 --> 00:26:35,560
Speaker 4: clinical obligation around the safety of the care that they provide.

519
00:26:35,600 --> 00:26:38,879
Speaker 4: And if you introduce AI alongside the clinician, how does

520
00:26:38,920 --> 00:26:41,520
Speaker 4: it actually work and what are the implications? And the

521
00:26:41,560 --> 00:26:44,040
Speaker 4: last year that's really interesting is privacy and security of

522
00:26:44,080 --> 00:26:46,000
Speaker 4: the data. So you might bring it all in, but

523
00:26:46,040 --> 00:26:48,320
Speaker 4: you know, how do you actually control it in the

524
00:26:48,320 --> 00:26:50,760
Speaker 4: world of more cyber attacks, particularly in health systems around

525
00:26:50,760 --> 00:26:52,879
Speaker 4: the world. So those are kind of the core barriers.

526
00:26:52,880 --> 00:26:55,320
Speaker 4: But the number one is obviously the hallucinations piece.

527
00:26:55,720 --> 00:26:58,240
Speaker 1: Yeah, so you know, I guess part of that is

528
00:26:58,280 --> 00:27:01,399
Speaker 1: having the confidence to experiment and trial and go at it,

529
00:27:01,440 --> 00:27:03,960
Speaker 1: but also having the confidence to say, actually, in this case,

530
00:27:04,080 --> 00:27:07,359
Speaker 1: the risks are too high, the technology is not there

531
00:27:07,520 --> 00:27:10,359
Speaker 1: yet or may not be, and so we're going to

532
00:27:10,440 --> 00:27:13,600
Speaker 1: choose to not make this a customer facing or patient

533
00:27:13,640 --> 00:27:17,439
Speaker 1: facing thing. Keep it for doctors or healthcare providers and

534
00:27:17,440 --> 00:27:21,480
Speaker 1: they can be the intermediary between the chatbot that's getting

535
00:27:21,480 --> 00:27:24,520
Speaker 1: a lot of information and the patient at the other end.

536
00:27:24,600 --> 00:27:27,320
Speaker 1: Is that kind of how thinking is going, Yeah.

537
00:27:27,119 --> 00:27:27,359
Speaker 5: It is.

538
00:27:27,400 --> 00:27:30,520
Speaker 4: It's an interesting piece because if you're wanting to experience

539
00:27:30,560 --> 00:27:33,280
Speaker 4: an experiment and dip your toes and genitive AI, I

540
00:27:33,280 --> 00:27:35,800
Speaker 4: think that the two pieces of conversation we're having in

541
00:27:35,840 --> 00:27:38,199
Speaker 4: New Zealand at the moment is so, what are the

542
00:27:38,280 --> 00:27:42,720
Speaker 4: use cases at a gathering momentum offshore and generally speaking

543
00:27:43,200 --> 00:27:48,680
Speaker 4: in the healthcare context using GENAI to re platform, recode

544
00:27:48,720 --> 00:27:50,720
Speaker 4: old applications, and New Zealand has a problem at the

545
00:27:50,720 --> 00:27:52,960
Speaker 4: moment around that we've got a lot of legacy applications

546
00:27:53,000 --> 00:27:56,000
Speaker 4: being used, particularly in the public health system. The second

547
00:27:56,040 --> 00:27:58,800
Speaker 4: area is around contact center experience, and the third one

548
00:27:58,840 --> 00:28:00,600
Speaker 4: is that voice to text that I talk about. So

549
00:28:01,119 --> 00:28:03,439
Speaker 4: those are the three broad areas. The other thing that

550
00:28:03,440 --> 00:28:06,280
Speaker 4: we've packed up offshore, which is interesting to share with you,

551
00:28:06,400 --> 00:28:10,520
Speaker 4: is generally speaking, most of the GENAI use cases have

552
00:28:10,600 --> 00:28:14,560
Speaker 4: been done on top of platforms. They're leveraging Salesforce, Microsoft,

553
00:28:15,600 --> 00:28:19,080
Speaker 4: our electronic medical record platforms where genitive AI is being

554
00:28:19,119 --> 00:28:22,320
Speaker 4: built in as a feature if you like, in these platforms.

555
00:28:22,560 --> 00:28:26,000
Speaker 4: So the hallucinations piece isn't as big a risk because

556
00:28:26,000 --> 00:28:28,680
Speaker 4: it's built into a platform. It's well tested, So I

557
00:28:28,720 --> 00:28:31,320
Speaker 4: think it's a fine balance. But it's just interesting to

558
00:28:31,359 --> 00:28:34,280
Speaker 4: see what the trends are in terms of the practical

559
00:28:34,480 --> 00:28:37,760
Speaker 4: elements of gen AI, the hype versus in reality what's

560
00:28:37,800 --> 00:28:38,200
Speaker 4: going on.

561
00:28:38,400 --> 00:28:41,720
Speaker 1: Yeah, absolutely, that's kind of a lot about primary care,

562
00:28:41,760 --> 00:28:45,000
Speaker 1: and we talked about how that's really advanced. What about

563
00:28:45,040 --> 00:28:47,240
Speaker 1: in the hospital world.

564
00:28:47,520 --> 00:28:51,920
Speaker 4: Yeah, Again, it's an interesting discussion around looking what happens

565
00:28:51,920 --> 00:28:54,560
Speaker 4: off sure, and I think there's a couple of contextual things.

566
00:28:54,600 --> 00:28:57,680
Speaker 4: So as a practicing clinician, when I think about technology

567
00:28:57,800 --> 00:29:01,480
Speaker 4: like genitive AI, I think of it as another tool

568
00:29:01,560 --> 00:29:04,320
Speaker 4: in my clinical practice, like over stethoscope around my neck.

569
00:29:04,520 --> 00:29:07,160
Speaker 4: Most of the younger generation clinicians, I'm I'm a I

570
00:29:07,160 --> 00:29:09,160
Speaker 4: guess I called a veteran these days because they've been

571
00:29:09,200 --> 00:29:11,080
Speaker 4: around the health system for twenty five years. But if

572
00:29:11,120 --> 00:29:14,960
Speaker 4: I look at some of the newly trained doctors and nurses,

573
00:29:15,120 --> 00:29:18,640
Speaker 4: they all expect if you like, generative AI to be

574
00:29:18,720 --> 00:29:22,480
Speaker 4: available for some of the use cases around again that

575
00:29:22,600 --> 00:29:26,120
Speaker 4: voice detext piece, and we haven't done anything in New

576
00:29:26,200 --> 00:29:29,680
Speaker 4: Zealand at the moment, but in Australia, the first use

577
00:29:29,720 --> 00:29:32,240
Speaker 4: case is using voice to text and busy theaters to

578
00:29:32,320 --> 00:29:35,760
Speaker 4: drive through throughput so you can hit health targets around,

579
00:29:35,840 --> 00:29:37,720
Speaker 4: you know, like the waiting lists for hypophens and things

580
00:29:37,760 --> 00:29:42,000
Speaker 4: like that. So there is a willingness for in that

581
00:29:42,360 --> 00:29:47,640
Speaker 4: surgical operating theater for surgeons, anetheists, theater nurses to use

582
00:29:47,680 --> 00:29:50,240
Speaker 4: those technologies to be more efficient in terms of through

583
00:29:50,240 --> 00:29:53,080
Speaker 4: put through the theater because they're not writing notes after

584
00:29:53,120 --> 00:29:56,080
Speaker 4: an operation. It's been done real time, so yes, there

585
00:29:56,120 --> 00:29:58,840
Speaker 4: is potential, just not quite happening at the moment, and

586
00:29:58,880 --> 00:30:02,760
Speaker 4: I guess that's the clinical frontline piece. Really interesting report

587
00:30:03,160 --> 00:30:06,400
Speaker 4: that we did with Microsoft recently for New Zealand looking

588
00:30:06,440 --> 00:30:09,719
Speaker 4: at how genitive AI could be applied to the nursing

589
00:30:10,240 --> 00:30:14,360
Speaker 4: workforce and based on using some of the Microsoft technologies

590
00:30:14,960 --> 00:30:18,800
Speaker 4: voice to texts predictive analytics around patients becoming unwell because

591
00:30:18,800 --> 00:30:21,120
Speaker 4: they have temperatures gone up or their blood pressures dropped.

592
00:30:22,280 --> 00:30:24,760
Speaker 4: They said, if you put on these common tools, you'd

593
00:30:24,800 --> 00:30:28,000
Speaker 4: get a productivity increase of around nine to ten percent

594
00:30:28,040 --> 00:30:31,560
Speaker 4: for each nurse across the country. So that's proven offshore,

595
00:30:31,920 --> 00:30:33,800
Speaker 4: done a little bit of analysis around how I guess

596
00:30:33,880 --> 00:30:36,680
Speaker 4: nursing workforce works the New Zealand today and our public hospitals.

597
00:30:36,800 --> 00:30:41,400
Speaker 4: You apply these technologies and process improvement augmentation around genitive

598
00:30:41,440 --> 00:30:44,960
Speaker 4: AI would actually lift productivity ten percent, which I guess

599
00:30:45,480 --> 00:30:47,720
Speaker 4: you know eight to twelve hour shifts is you know

600
00:30:47,760 --> 00:30:49,960
Speaker 4: one to one and a half hours, which does make

601
00:30:49,960 --> 00:30:50,600
Speaker 4: a big difference.

602
00:30:50,760 --> 00:30:52,680
Speaker 1: It does, especially if you're looking at you know, a

603
00:30:52,720 --> 00:30:56,400
Speaker 1: shortage and if ten percent is saying you have the

604
00:30:56,440 --> 00:30:58,680
Speaker 1: equivalent to ten nurses that have nine nurses on shift.

605
00:30:58,680 --> 00:31:00,520
Speaker 3: That actually does make a difference end of the day.

606
00:31:00,640 --> 00:31:02,240
Speaker 4: It does, and you know, we've got to you know,

607
00:31:02,520 --> 00:31:04,560
Speaker 4: as you've heard in the media, we've got to adopt

608
00:31:04,600 --> 00:31:07,600
Speaker 4: a shortage of nursing shortage and in some areas what

609
00:31:07,680 --> 00:31:10,560
Speaker 4: I would call an allied health professional shortage BUZZIO is

610
00:31:10,560 --> 00:31:13,400
Speaker 4: occupational therapists. The biggest year actually at the moment is

611
00:31:13,440 --> 00:31:17,520
Speaker 4: anesthetic technicians. So that's actually preventing some of the through

612
00:31:17,560 --> 00:31:20,800
Speaker 4: put in private and public hospitals where there's just enough

613
00:31:20,800 --> 00:31:23,080
Speaker 4: of anesthetic technicians while you're sleep to look after you.

614
00:31:23,480 --> 00:31:25,000
Speaker 4: It's really interesting at the moment.

615
00:31:25,160 --> 00:31:29,000
Speaker 1: What about the casting forward into the future. Do you

616
00:31:29,080 --> 00:31:32,880
Speaker 1: see it, as you know, potentially being a kind of

617
00:31:33,360 --> 00:31:36,280
Speaker 1: first point of contact for patients to be able to

618
00:31:36,320 --> 00:31:38,840
Speaker 1: say instead of just pushing a button waiting for a

619
00:31:38,920 --> 00:31:41,400
Speaker 1: nurse pushing a button and saying, oh, I think my

620
00:31:41,480 --> 00:31:43,479
Speaker 1: leg's really hurting and I'm not sure, and so then

621
00:31:43,520 --> 00:31:46,440
Speaker 1: that can automatically go into like a database and be

622
00:31:46,520 --> 00:31:48,520
Speaker 1: triaged and get the nurses to who needs to. Like,

623
00:31:49,120 --> 00:31:51,040
Speaker 1: it strikes me there is a fair bit of potential

624
00:31:51,080 --> 00:31:55,440
Speaker 1: in that first line of support for generative AI in

625
00:31:55,560 --> 00:31:56,320
Speaker 1: the future.

626
00:31:56,800 --> 00:31:58,680
Speaker 4: Yeah, look, I totally agree with you. So I think

627
00:31:59,000 --> 00:32:03,120
Speaker 4: technologies like unit of AI we often talk about in

628
00:32:03,120 --> 00:32:04,880
Speaker 4: the current health system of New Zealand, we've got to

629
00:32:04,880 --> 00:32:07,000
Speaker 4: transform it generally. That means you spend in the next

630
00:32:07,000 --> 00:32:10,680
Speaker 4: five years trying to change the reality is we need

631
00:32:10,720 --> 00:32:14,000
Speaker 4: to ask New Zealanders, you know, what's your experience like

632
00:32:14,040 --> 00:32:16,360
Speaker 4: in terms of their most recent interaction with a healthcare

633
00:32:16,360 --> 00:32:18,920
Speaker 4: professional or a period of care and hospital. So my

634
00:32:19,400 --> 00:32:23,320
Speaker 4: view is first and foremost is that's where things are going.

635
00:32:23,320 --> 00:32:25,800
Speaker 4: I guess it's probably called consumerism and what do people

636
00:32:25,840 --> 00:32:28,320
Speaker 4: expect in terms of their health experience and how do

637
00:32:28,360 --> 00:32:31,120
Speaker 4: they compare it to other experiences like banking just to

638
00:32:31,240 --> 00:32:33,920
Speaker 4: use it? And I guess at parallel so I do

639
00:32:34,080 --> 00:32:37,520
Speaker 4: think one our customers, patients like you and I, will

640
00:32:37,600 --> 00:32:42,120
Speaker 4: expect a better level of experience and driven by digital

641
00:32:42,160 --> 00:32:44,640
Speaker 4: touch points, of which GENAI will power some of them.

642
00:32:44,640 --> 00:32:45,320
Speaker 3: It's number one.

643
00:32:45,480 --> 00:32:47,920
Speaker 4: The next question is do New Zealanders want it? And

644
00:32:47,960 --> 00:32:50,440
Speaker 4: the other that is yes. So about seven or eight

645
00:32:50,520 --> 00:32:53,400
Speaker 4: years ago a survey was done that I was involved

646
00:32:53,400 --> 00:32:56,640
Speaker 4: and where we went around the country asking patients what

647
00:32:56,720 --> 00:33:01,920
Speaker 4: their expectations were around digital tools to manage their disease

648
00:33:01,920 --> 00:33:03,800
Speaker 4: and illness if they had something like diabetes, or their

649
00:33:03,840 --> 00:33:06,000
Speaker 4: health and wellness where they're trying to prevent themselves from

650
00:33:06,000 --> 00:33:09,920
Speaker 4: getting diabetes, and the overwhelming response was yes, we want

651
00:33:09,920 --> 00:33:12,680
Speaker 4: a digital experience. I think the last piece to share

652
00:33:12,680 --> 00:33:14,920
Speaker 4: with you, and it's a really interesting thing to think through,

653
00:33:15,040 --> 00:33:19,040
Speaker 4: is we've got these workforce shortage challenges, which is a

654
00:33:19,080 --> 00:33:21,760
Speaker 4: big problem to solve. But one of the things in

655
00:33:21,840 --> 00:33:24,960
Speaker 4: other health systems have reimagined what healthcare could look like

656
00:33:25,000 --> 00:33:27,800
Speaker 4: in terms of a journey for patients and their families

657
00:33:27,880 --> 00:33:31,600
Speaker 4: is it's an interesting concept where there's kind of they

658
00:33:32,800 --> 00:33:36,080
Speaker 4: have the capacity if you empower the right cohorts to

659
00:33:36,120 --> 00:33:38,200
Speaker 4: manage their health and wellness to take some of the

660
00:33:38,200 --> 00:33:40,680
Speaker 4: burden off doctors and nurses if they've got the right

661
00:33:41,080 --> 00:33:42,880
Speaker 4: tools in front of them, or they can manage their

662
00:33:42,920 --> 00:33:45,800
Speaker 4: diabetes themselves. So I think the other thing that's coming

663
00:33:45,840 --> 00:33:50,360
Speaker 4: is we enable more digital journeys, patients and their families

664
00:33:50,400 --> 00:33:52,960
Speaker 4: and farnes will take more control over their health and

665
00:33:53,000 --> 00:33:55,840
Speaker 4: wellness and that'll take some of the burden off the

666
00:33:55,880 --> 00:33:59,680
Speaker 4: health system and it waits up all the workforce shortage problems.

667
00:34:00,000 --> 00:34:03,200
Speaker 4: Will help in terms of at the moment, you probably

668
00:34:03,680 --> 00:34:06,240
Speaker 4: got demand, but they can meet that with their own capacity.

669
00:34:06,440 --> 00:34:08,640
Speaker 4: It's a funny way of thinking about things, but definitely

670
00:34:08,680 --> 00:34:10,560
Speaker 4: that's the general trained off shore at the moment.

671
00:34:17,280 --> 00:34:21,440
Speaker 1: The other thing that strikes me is as not generative

672
00:34:21,480 --> 00:34:25,880
Speaker 1: AI specifically, but machine learning based tools, the kind of

673
00:34:25,880 --> 00:34:27,640
Speaker 1: classic AI as I kind of refer to it in

674
00:34:27,640 --> 00:34:31,800
Speaker 1: my head. The application of that is becoming so much

675
00:34:32,120 --> 00:34:36,800
Speaker 1: more capable and so much more broadly applied that actually

676
00:34:37,280 --> 00:34:41,279
Speaker 1: some of these widgets may not necessarily be needed where

677
00:34:41,280 --> 00:34:42,400
Speaker 1: they definitely were before.

678
00:34:42,400 --> 00:34:43,480
Speaker 3: And I'm thinking of things.

679
00:34:43,280 --> 00:34:45,799
Speaker 1: Like taku eyes take a photo of your eye to

680
00:34:45,800 --> 00:34:49,360
Speaker 1: get certain diagnoses. That strikes me as a way that

681
00:34:49,400 --> 00:34:53,920
Speaker 1: AI is helping us directly move towards some level of

682
00:34:53,960 --> 00:34:58,440
Speaker 1: equity because you don't need to have as much equipment

683
00:34:58,680 --> 00:35:01,640
Speaker 1: in order to be able to actually address or monitor

684
00:35:01,880 --> 00:35:02,560
Speaker 1: in some ways.

685
00:35:03,440 --> 00:35:04,359
Speaker 3: Are you seeing that as well?

686
00:35:04,440 --> 00:35:06,440
Speaker 4: You see the channel you know, I probably call that

687
00:35:06,560 --> 00:35:09,560
Speaker 4: democratization of health and wellness, right, It's a really interesting story.

688
00:35:09,560 --> 00:35:13,080
Speaker 4: So yeah, I've been involved with Tokui since the start

689
00:35:13,120 --> 00:35:16,160
Speaker 4: Spark Health in my previous role provide U some innovation

690
00:35:16,280 --> 00:35:18,719
Speaker 4: funding to get them using aws on the cloud to

691
00:35:18,760 --> 00:35:21,840
Speaker 4: run their algorithms around the email that you just talked about.

692
00:35:21,880 --> 00:35:24,160
Speaker 4: So and again, that's a little bit of that of

693
00:35:24,560 --> 00:35:29,480
Speaker 4: a consumer customer experience piece where you gather a photo

694
00:35:29,520 --> 00:35:32,120
Speaker 4: of the retina. How you gather it can be multiple

695
00:35:32,160 --> 00:35:34,759
Speaker 4: different ways, and then you teaching this algorithm to kind

696
00:35:34,760 --> 00:35:38,040
Speaker 4: of go, have you got diabetic retinopathy or hypertensive retinopathy?

697
00:35:38,280 --> 00:35:38,440
Speaker 2: Yes?

698
00:35:38,560 --> 00:35:40,400
Speaker 4: Or no? No, don't worry about it. Come back in

699
00:35:40,440 --> 00:35:43,160
Speaker 4: a year and get screened. Oh yes, you do set

700
00:35:43,200 --> 00:35:47,239
Speaker 4: up the referral path. So and I guess that's that's

701
00:35:47,360 --> 00:35:52,640
Speaker 4: one people like tokuais going, hey, we need to unlock, democratize,

702
00:35:52,680 --> 00:35:56,560
Speaker 4: provide better X this provide better experience. I definitely see

703
00:35:56,920 --> 00:36:00,080
Speaker 4: that coming. It's an interesting area because, as you have alluded, so,

704
00:36:00,080 --> 00:36:06,880
Speaker 4: it's diagnostics, right, and again my views medicine traditionally is

705
00:36:07,520 --> 00:36:09,960
Speaker 4: you come and see me, I take a history from you,

706
00:36:10,000 --> 00:36:11,399
Speaker 4: I run a whole lot of tests, and we work

707
00:36:11,440 --> 00:36:15,319
Speaker 4: out what your diagnosis is. With the sophistication of diagnostics

708
00:36:15,360 --> 00:36:18,160
Speaker 4: these days, you almost do the diagnostic first to help

709
00:36:18,200 --> 00:36:20,919
Speaker 4: you get to the diagnosis, because sometimes it's the gold

710
00:36:21,000 --> 00:36:23,720
Speaker 4: standard and you don't necessarily need to take the history

711
00:36:23,760 --> 00:36:26,560
Speaker 4: of how you've been feeling. Those types of things so

712
00:36:28,239 --> 00:36:31,120
Speaker 4: from my perspective, I think diagnostics are going to get better.

713
00:36:31,239 --> 00:36:34,160
Speaker 4: One of the real challenges that's happened off shore is

714
00:36:34,600 --> 00:36:40,520
Speaker 4: when patients engage in those diagnostic type tools. If it's right, great,

715
00:36:40,520 --> 00:36:42,880
Speaker 4: if it's kind of a little bit on the fence

716
00:36:42,920 --> 00:36:44,799
Speaker 4: around what your diagnosis is and how do you kind

717
00:36:44,800 --> 00:36:47,160
Speaker 4: of enter into the system to get it clarified. Because

718
00:36:48,280 --> 00:36:50,480
Speaker 4: there's the science of medicine that I talked about before,

719
00:36:50,520 --> 00:36:52,600
Speaker 4: and there's the art of medicine. And sometimes what you

720
00:36:52,680 --> 00:36:55,399
Speaker 4: find and a diagnostic is that it can be one

721
00:36:55,440 --> 00:36:58,200
Speaker 4: diagnosis or sometimes it can mean three or four other

722
00:36:58,440 --> 00:37:01,680
Speaker 4: diagnoses and you need to do further diagnostic tests to

723
00:37:01,760 --> 00:37:04,160
Speaker 4: kind of rule bring out till you get to the one.

724
00:37:04,200 --> 00:37:05,520
Speaker 4: So it's a little bit a little bit to think

725
00:37:05,520 --> 00:37:07,920
Speaker 4: through there. But I agree with you in terms of

726
00:37:08,400 --> 00:37:11,960
Speaker 4: companies like toku Wai's kind of setting the standard around

727
00:37:12,480 --> 00:37:16,200
Speaker 4: machine learning and changing the access to those types of services.

728
00:37:17,160 --> 00:37:20,520
Speaker 1: I imagine, you know, maybe ten twenty years, You've got

729
00:37:20,520 --> 00:37:22,200
Speaker 1: somebody at home and it's like our time for my

730
00:37:22,239 --> 00:37:24,319
Speaker 1: medical checkup, and they get out their smartphone and they

731
00:37:24,560 --> 00:37:26,879
Speaker 1: take some photos of various things part of their body,

732
00:37:26,920 --> 00:37:29,680
Speaker 1: and they say some words and they you know, maybe

733
00:37:30,360 --> 00:37:33,200
Speaker 1: get a smart cheap smart watch and it takes some

734
00:37:33,880 --> 00:37:36,520
Speaker 1: stuff like that, and then that can feed into an

735
00:37:36,560 --> 00:37:38,640
Speaker 1: algorithm which can be sent to a GP to go,

736
00:37:39,200 --> 00:37:41,440
Speaker 1: oh yep, it all looks okay, Like we can give

737
00:37:41,480 --> 00:37:45,080
Speaker 1: you the medical tick really, like you said, democratizing it

738
00:37:45,120 --> 00:37:48,120
Speaker 1: but also taking it out of urban centers as well

739
00:37:48,200 --> 00:37:50,640
Speaker 1: and allowing people who may not have as much access

740
00:37:50,680 --> 00:37:56,799
Speaker 1: to primary healthcare to really receive the early intervention care

741
00:37:56,880 --> 00:37:58,360
Speaker 1: that can actually make a big difference.

742
00:37:58,440 --> 00:38:01,160
Speaker 4: Yeah, yeah, look, I totally agree with you. One of

743
00:38:01,200 --> 00:38:02,520
Speaker 4: the things I was going to share with you today

744
00:38:02,600 --> 00:38:05,239
Speaker 4: is being some more and I'm involved in Martin and

745
00:38:05,239 --> 00:38:08,480
Speaker 4: PACIFICA getting into digital health tech. And the reason I'm

746
00:38:08,480 --> 00:38:11,799
Speaker 4: bringing this up is there's an element of driving kind

747
00:38:11,800 --> 00:38:16,360
Speaker 4: of a national way of working rural or urban around healthcare.

748
00:38:16,880 --> 00:38:20,000
Speaker 4: But the other thing that's really interesting is sometimes communities

749
00:38:20,120 --> 00:38:22,840
Speaker 4: need to solve for themselves. So they understand the business

750
00:38:22,840 --> 00:38:25,200
Speaker 4: problem or the health problem. They've got some tech that

751
00:38:25,239 --> 00:38:27,640
Speaker 4: they could use, but they actually come together as a

752
00:38:27,680 --> 00:38:31,279
Speaker 4: community around how they solve some of these problems. So

753
00:38:31,320 --> 00:38:33,560
Speaker 4: I think what we'll see in the future in terms

754
00:38:33,600 --> 00:38:35,640
Speaker 4: of what I'm trying to cover here is you'll have

755
00:38:36,520 --> 00:38:39,040
Speaker 4: national ways of working around these health checks that might

756
00:38:39,120 --> 00:38:41,600
Speaker 4: work for eighty percent of New Zealanders, but it doesn't

757
00:38:41,640 --> 00:38:45,920
Speaker 4: work potentially behaviorally for twenty percent. And I'll follow this

758
00:38:46,000 --> 00:38:49,800
Speaker 4: through So in the PACIFICA communities, it's really really hard

759
00:38:49,840 --> 00:38:53,839
Speaker 4: to get Pacifica to engage with the health system, and

760
00:38:53,880 --> 00:38:56,560
Speaker 4: so how do you engage with them? And if you're

761
00:38:56,560 --> 00:39:00,879
Speaker 4: offering this tooling, how digitally enabled are my mum, she's

762
00:39:00,840 --> 00:39:04,360
Speaker 4: saw them on Chinese she's seventy five, not particularly digitally enabled.

763
00:39:04,360 --> 00:39:06,359
Speaker 4: But if you had the tool to do a health

764
00:39:06,440 --> 00:39:09,920
Speaker 4: check remotely, who would go and do that with her?

765
00:39:09,960 --> 00:39:11,439
Speaker 4: And it's probably been me or one of my three

766
00:39:11,440 --> 00:39:15,400
Speaker 4: younger brothers actually facilitate the processes. They've been independent enough

767
00:39:15,440 --> 00:39:17,440
Speaker 4: to do it. So there's a few but the technology

768
00:39:17,480 --> 00:39:19,719
Speaker 4: is enabled, but how do you practically get people to

769
00:39:19,800 --> 00:39:23,760
Speaker 4: use the tech to do the remote health and wellness

770
00:39:23,840 --> 00:39:26,319
Speaker 4: checks on an annual basis with the GP So there's

771
00:39:26,320 --> 00:39:27,560
Speaker 4: some of that. And the reason I'm showing that with

772
00:39:27,600 --> 00:39:31,200
Speaker 4: you is is once you understand the opportunity the technology,

773
00:39:31,239 --> 00:39:35,319
Speaker 4: it's really interesting to see how communities solve for how

774
00:39:35,360 --> 00:39:37,680
Speaker 4: they practically get the adoption of the tech to improve

775
00:39:37,719 --> 00:39:41,480
Speaker 4: health and wellness outcomes, and I think that's awesome. And

776
00:39:41,520 --> 00:39:46,319
Speaker 4: the other side of that is I think in my

777
00:39:46,400 --> 00:39:49,799
Speaker 4: experience at Accenture that the work we're doing in our

778
00:39:49,840 --> 00:39:52,640
Speaker 4: words around mary Indigenous people of New Zealand, some of

779
00:39:52,640 --> 00:39:54,319
Speaker 4: the problems you're trying to solve in New Zealand at

780
00:39:54,320 --> 00:39:57,200
Speaker 4: the moment, we're ahead of other parts of the world.

781
00:39:57,280 --> 00:40:00,719
Speaker 4: So I think about the Native Indian in America, I

782
00:40:00,719 --> 00:40:03,920
Speaker 4: think about Aboriginal and Tory Straight Islanders in Australia. Some

783
00:40:04,000 --> 00:40:06,880
Speaker 4: of the things we're doing in New Zealand already around

784
00:40:07,280 --> 00:40:10,920
Speaker 4: machine learning junior of AI sometimes through that EWI actually

785
00:40:11,520 --> 00:40:13,480
Speaker 4: is ahead of everybody else. So there's a little bit

786
00:40:13,480 --> 00:40:15,440
Speaker 4: around what we do in New Zealand. If we get

787
00:40:15,440 --> 00:40:17,359
Speaker 4: it right and get the adoption, we can kind of

788
00:40:17,560 --> 00:40:20,719
Speaker 4: show the world how you can improve equity for Formardi

789
00:40:20,760 --> 00:40:22,760
Speaker 4: pacifica as some examples.

790
00:40:23,560 --> 00:40:25,920
Speaker 1: Can you share a kind of an example of what

791
00:40:25,960 --> 00:40:27,960
Speaker 1: you mean by that? What are some of the interventions? Y?

792
00:40:28,080 --> 00:40:29,719
Speaker 4: Yeah, so no, it's really interesting to share with you.

793
00:40:29,800 --> 00:40:34,000
Speaker 4: So probably two places to start. One of the things

794
00:40:34,000 --> 00:40:36,000
Speaker 4: that I think, you know, what does the future of

795
00:40:36,000 --> 00:40:37,960
Speaker 4: health and wellness look like in New Zealand and it's

796
00:40:38,000 --> 00:40:40,080
Speaker 4: a really interesting to think thing to share with you

797
00:40:40,200 --> 00:40:44,600
Speaker 4: that there's this this concept called social determinants of health.

798
00:40:44,760 --> 00:40:48,000
Speaker 4: And effectively, if you digitize you every experience in the

799
00:40:48,000 --> 00:40:50,239
Speaker 4: health system, from a GP through to physio through to

800
00:40:50,280 --> 00:40:53,319
Speaker 4: beteen a hospital doctor, you'd only get one fifth of

801
00:40:53,320 --> 00:40:56,880
Speaker 4: the data that determines yours and my health and wellness outcomes.

802
00:40:57,160 --> 00:40:59,680
Speaker 4: Then the number one data items your post code and

803
00:40:59,680 --> 00:41:02,080
Speaker 4: where you live. But there's a whole lot of behavioral

804
00:41:02,120 --> 00:41:04,760
Speaker 4: stuff around do you exercise, do you smoke, do you drink?

805
00:41:05,320 --> 00:41:07,040
Speaker 4: What are your family what's your family history. I've got

806
00:41:07,040 --> 00:41:08,880
Speaker 4: a strong family history of a schemic heart disease in

807
00:41:08,920 --> 00:41:11,920
Speaker 4: my family. Where do you live, how educated are you?

808
00:41:12,040 --> 00:41:14,800
Speaker 4: As your house warm or cold? Those types of things.

809
00:41:14,840 --> 00:41:17,320
Speaker 4: So one of the opportunities in New Zealand is to

810
00:41:17,360 --> 00:41:21,399
Speaker 4: have that holistic approach to social atterminans of health. It's

811
00:41:21,400 --> 00:41:26,600
Speaker 4: called now in Martyrdom, there's another concept called tafade Tapafa

812
00:41:26,880 --> 00:41:30,600
Speaker 4: and that looks at your emotional health and wellness, your

813
00:41:30,600 --> 00:41:33,680
Speaker 4: physical you're spiritual and your mental health and wellness and

814
00:41:33,719 --> 00:41:35,239
Speaker 4: so some of the things that you're doing around how

815
00:41:35,239 --> 00:41:38,319
Speaker 4: they're applying the tech to that whole person. In the

816
00:41:38,400 --> 00:41:41,799
Speaker 4: community is actually having better health and wellness outcomes and

817
00:41:41,840 --> 00:41:44,400
Speaker 4: just doing a health system response. So coming back to

818
00:41:44,440 --> 00:41:47,400
Speaker 4: some of the use cases that happening at the moment,

819
00:41:48,480 --> 00:41:51,200
Speaker 4: So there's a lot of the communities in a number

820
00:41:51,200 --> 00:41:53,840
Speaker 4: of EEHE where they're collecting data around all those things

821
00:41:54,120 --> 00:41:57,120
Speaker 4: mental health, I guess, general physical health and well being,

822
00:41:57,239 --> 00:41:59,880
Speaker 4: emotional and spiritual health and wellbeing, and is starting to

823
00:42:00,160 --> 00:42:04,279
Speaker 4: algorithms across the top of that and so and that

824
00:42:04,400 --> 00:42:07,440
Speaker 4: helps them do care plans as an example for that

825
00:42:07,520 --> 00:42:09,760
Speaker 4: are not just about you know, take your high blood

826
00:42:09,760 --> 00:42:13,120
Speaker 4: pressure medication, will walk for thirty minutes today, it's around

827
00:42:13,160 --> 00:42:16,960
Speaker 4: thinking about the holistic person. So, and they're using genai

828
00:42:16,960 --> 00:42:20,399
Speaker 4: to link that outcomes and drive insights. And they're also

829
00:42:20,480 --> 00:42:22,960
Speaker 4: using a bit of email around predictive analytics. And it

830
00:42:23,400 --> 00:42:26,640
Speaker 4: comes back to innovation in New Zealand thinking about health

831
00:42:26,680 --> 00:42:30,239
Speaker 4: and wellness differently, and then those models of delivering health

832
00:42:30,280 --> 00:42:34,560
Speaker 4: and wellness I think where things are going to go globally.

833
00:42:35,360 --> 00:42:38,920
Speaker 1: That's really interesting because it's moving away from this concept

834
00:42:39,000 --> 00:42:45,120
Speaker 1: that population aggregated data is the best way of assessing

835
00:42:45,120 --> 00:42:47,720
Speaker 1: a population's health and saying well, if you can actually

836
00:42:47,760 --> 00:42:52,799
Speaker 1: take data from specific populations and you can really dynamically

837
00:42:52,840 --> 00:42:57,759
Speaker 1: split it out by post code, by you know, ethnic background,

838
00:42:57,840 --> 00:43:02,520
Speaker 1: and run analysis really quickly and easily over different areas.

839
00:43:02,920 --> 00:43:07,840
Speaker 1: And that is directly a result of modern digital data tools.

840
00:43:07,920 --> 00:43:08,080
Speaker 2: Right.

841
00:43:08,200 --> 00:43:10,640
Speaker 1: It wouldn't have been possible even twenty years ago because

842
00:43:10,680 --> 00:43:14,759
Speaker 1: everything was so slow and difficult to actually to do.

843
00:43:15,360 --> 00:43:18,640
Speaker 1: But the result of being able to do it dynamically

844
00:43:18,680 --> 00:43:22,480
Speaker 1: and quickly means that you can really look at areas

845
00:43:22,480 --> 00:43:26,120
Speaker 1: where it is most needed and make targeted interventions that

846
00:43:26,200 --> 00:43:30,280
Speaker 1: are taking consideration more than just you know, high blood pressure.

847
00:43:30,239 --> 00:43:32,799
Speaker 4: Here's your medicine, Yeah, exactly, And so you know, to

848
00:43:32,800 --> 00:43:36,440
Speaker 4: share with you, we've started a conversation with the Ministry

849
00:43:36,440 --> 00:43:40,479
Speaker 4: of Social Development and with and also with bai Kaha,

850
00:43:40,560 --> 00:43:44,120
Speaker 4: the ministry of disabled people that got broken off from

851
00:43:44,160 --> 00:43:46,120
Speaker 4: the health system as part of the reforms, and with

852
00:43:46,239 --> 00:43:50,879
Speaker 4: ACC and the context for that conversation is in communities

853
00:43:50,920 --> 00:43:55,040
Speaker 4: where health and wellness is a real challenge, how will

854
00:43:55,120 --> 00:44:00,239
Speaker 4: you bring insights health social disability in some case is

855
00:44:00,320 --> 00:44:05,440
Speaker 4: injury prevention perspective, focusing on an individual or the healthhold

856
00:44:05,440 --> 00:44:08,040
Speaker 4: they live in or the community they live in. And

857
00:44:08,120 --> 00:44:11,799
Speaker 4: so there's an enabling policy. I'll call it from the

858
00:44:11,840 --> 00:44:16,120
Speaker 4: DIA at the moment in Wellington around sharing identity across

859
00:44:16,120 --> 00:44:19,360
Speaker 4: those government agencies. So NHI in the hospital context or

860
00:44:19,360 --> 00:44:22,839
Speaker 4: health context and acc have a unique number for us

861
00:44:22,880 --> 00:44:24,920
Speaker 4: as well, so you know, so they can link my

862
00:44:25,040 --> 00:44:28,760
Speaker 4: record from the health system to I guess a shoulder

863
00:44:28,800 --> 00:44:30,560
Speaker 4: dislocation I had a few years ago and I was

864
00:44:30,560 --> 00:44:35,680
Speaker 4: playing rugby from an accident perspective. So there's enabling policy

865
00:44:35,680 --> 00:44:37,480
Speaker 4: to share identity so you can have a single view

866
00:44:37,520 --> 00:44:39,880
Speaker 4: of somebody. Then the second bit of that is what

867
00:44:40,000 --> 00:44:42,319
Speaker 4: data can be shared, and the third part of that

868
00:44:42,520 --> 00:44:44,719
Speaker 4: is what are some of the GENAI use cases you

869
00:44:44,719 --> 00:44:48,160
Speaker 4: can run across the top of health data, social services data,

870
00:44:48,320 --> 00:44:51,560
Speaker 4: disability data, and so there's real buy in across these

871
00:44:51,560 --> 00:44:54,880
Speaker 4: four agencies to kind of look at what the art

872
00:44:54,920 --> 00:44:58,239
Speaker 4: of the possible is in bringing together these agencies and

873
00:44:58,680 --> 00:45:02,200
Speaker 4: sharing data and a entity. Then once you've got that data,

874
00:45:02,280 --> 00:45:04,759
Speaker 4: how can you improve the experience for these some of

875
00:45:04,760 --> 00:45:08,040
Speaker 4: these communities that are affected in those contexts. So early

876
00:45:08,080 --> 00:45:11,160
Speaker 4: doors at the moment for us, but something that we

877
00:45:11,160 --> 00:45:13,680
Speaker 4: do offshore a lot, which is, hey, how do you

878
00:45:13,680 --> 00:45:16,760
Speaker 4: get these government agencies to think about the total picture

879
00:45:16,760 --> 00:45:19,399
Speaker 4: of social permanence of health and how do you bring

880
00:45:19,440 --> 00:45:22,279
Speaker 4: them together to offer new experiences and what opportunities does

881
00:45:22,320 --> 00:45:26,000
Speaker 4: genetve Ai bring. So it's a fascinating project that we've

882
00:45:26,000 --> 00:45:28,560
Speaker 4: started about three months ago, and then we're about to

883
00:45:28,560 --> 00:45:30,239
Speaker 4: get those agencies to look at the ard of the

884
00:45:30,239 --> 00:45:32,280
Speaker 4: possible of some of the things we're doing off sure again,

885
00:45:32,360 --> 00:45:35,640
Speaker 4: coming back to part of the conversations day, the Genai

886
00:45:35,800 --> 00:45:41,560
Speaker 4: conversation opens up these conversations around thinking about reimagining health

887
00:45:41,600 --> 00:45:42,120
Speaker 4: and wellness.

888
00:45:42,280 --> 00:45:44,520
Speaker 1: Is there anything that you would like to add, anything

889
00:45:44,640 --> 00:45:46,160
Speaker 1: that important you think we haven't covered.

890
00:45:46,320 --> 00:45:50,040
Speaker 4: So something I've been doing for some time is wearing

891
00:45:50,120 --> 00:45:53,520
Speaker 4: auring and it's really interesting. It started off as just

892
00:45:54,160 --> 00:45:56,920
Speaker 4: I Guess a tool to collect health and fitness data,

893
00:45:57,800 --> 00:46:00,880
Speaker 4: and with Genai coming along and now takes that data,

894
00:46:01,040 --> 00:46:08,280
Speaker 4: runs algorithms and goes measures mental resilience and it measures

895
00:46:09,120 --> 00:46:11,160
Speaker 4: whether I should not go and exercise today because I'm

896
00:46:11,200 --> 00:46:13,279
Speaker 4: not ready to do that. So I use that every

897
00:46:13,360 --> 00:46:16,439
Speaker 4: day and it also has a I Guess a health

898
00:46:16,480 --> 00:46:19,560
Speaker 4: coach based on the data. So just to share with

899
00:46:19,600 --> 00:46:22,560
Speaker 4: you right now, my level of daytime stress based on

900
00:46:22,560 --> 00:46:24,920
Speaker 4: the ring and the algorithms to me and what I've

901
00:46:24,920 --> 00:46:26,759
Speaker 4: been like for the last five years, I'm relaxed, So

902
00:46:26,800 --> 00:46:28,640
Speaker 4: that Ben, you must be running a good into you today.

903
00:46:29,600 --> 00:46:30,400
Speaker 3: Yeah.

904
00:46:30,560 --> 00:46:33,080
Speaker 4: Then it measures my heart health and tells me how

905
00:46:33,120 --> 00:46:37,640
Speaker 4: old my heart is relative to my age. So according

906
00:46:37,760 --> 00:46:40,239
Speaker 4: according to the app, I'm fifty one, and according to

907
00:46:40,280 --> 00:46:42,879
Speaker 4: the app, my heart's forty seven. And so I guess

908
00:46:42,880 --> 00:46:44,880
Speaker 4: what I'm sharing with you is coming back to some

909
00:46:44,920 --> 00:46:48,279
Speaker 4: of these experiences over time, and that consumerism trend I

910
00:46:48,320 --> 00:46:52,440
Speaker 4: see generally speaking Kiwi's you know, we'll get some of

911
00:46:52,480 --> 00:46:56,719
Speaker 4: these experiences where they manage their health and wellness leveraging

912
00:46:57,480 --> 00:47:01,359
Speaker 4: machine learning, generative AI, and then and then when they

913
00:47:01,400 --> 00:47:03,520
Speaker 4: think they need the intervention of the health system or

914
00:47:03,560 --> 00:47:05,800
Speaker 4: to navigate the health system, then they'll touch the health system.

915
00:47:05,840 --> 00:47:08,000
Speaker 4: So right now I wouldn't get where I'm coming from.

916
00:47:08,120 --> 00:47:09,840
Speaker 4: But if my heart health went the other way, or

917
00:47:09,880 --> 00:47:12,400
Speaker 4: my pulse is getting higher, or I was getting my

918
00:47:12,440 --> 00:47:15,200
Speaker 4: mental resilience wasn't so good, I'd consider potentially, you know,

919
00:47:15,239 --> 00:47:16,399
Speaker 4: going and seeing my family GP.

920
00:47:16,600 --> 00:47:19,320
Speaker 1: And I guess even if you couldn't put an awring

921
00:47:19,400 --> 00:47:21,719
Speaker 1: on every finger in New Zealand, you could have them

922
00:47:21,760 --> 00:47:24,920
Speaker 1: in digital check up spots and churches and might I

923
00:47:25,320 --> 00:47:28,080
Speaker 1: and you know, a GP clinic where you can just

924
00:47:28,080 --> 00:47:31,400
Speaker 1: pop in and get that done really easily and dynamically

925
00:47:31,440 --> 00:47:33,920
Speaker 1: and without and linking that back to your health data

926
00:47:34,000 --> 00:47:36,560
Speaker 1: so that it can directly feed and maybe flag something

927
00:47:36,600 --> 00:47:38,120
Speaker 1: at your GP if there's something wrong.

928
00:47:38,200 --> 00:47:40,400
Speaker 4: Yeah, and look being a really important thing you just

929
00:47:40,440 --> 00:47:42,160
Speaker 4: packed up upon that I should have talked about before,

930
00:47:42,200 --> 00:47:44,680
Speaker 4: which is the data in the future is going to

931
00:47:44,719 --> 00:47:46,080
Speaker 4: be a mixture of what I put in as a

932
00:47:46,080 --> 00:47:49,320
Speaker 4: provider about you, but it also should include the information

933
00:47:49,400 --> 00:47:51,880
Speaker 4: you put in about you. I see that trend coming,

934
00:47:51,880 --> 00:47:54,279
Speaker 4: but pragmatically, do people want it? And I think the

935
00:47:54,320 --> 00:47:57,840
Speaker 4: answers over time yes, if they start learning about some

936
00:47:57,920 --> 00:48:00,239
Speaker 4: of the capabilities, but they need to be empowered to

937
00:48:00,280 --> 00:48:01,040
Speaker 4: manage the health.

938
00:48:05,120 --> 00:48:07,400
Speaker 1: I have to say I thoroughly enjoyed that interview. I

939
00:48:07,440 --> 00:48:11,720
Speaker 1: thought Will was really great in the content and the

940
00:48:11,760 --> 00:48:15,200
Speaker 1: specificity that he went into around the use of AI

941
00:48:15,239 --> 00:48:18,920
Speaker 1: and healthcare, right from generative AI into kind of the

942
00:48:18,960 --> 00:48:21,880
Speaker 1: Toku Eyes style machine learning and everywhere in between.

943
00:48:21,920 --> 00:48:23,120
Speaker 3: I thought it was really interesting.

944
00:48:23,520 --> 00:48:27,680
Speaker 2: He was great, you know, and obviously Accentua is heavily

945
00:48:27,719 --> 00:48:32,239
Speaker 2: involved in what's going on in the health sector. They're

946
00:48:32,239 --> 00:48:36,680
Speaker 2: helping out the government and probably earning a lot of

947
00:48:37,040 --> 00:48:39,960
Speaker 2: good consulting fees. But it was a real sort of,

948
00:48:40,280 --> 00:48:45,239
Speaker 2: I think, frank and upfront sort of assessment of where

949
00:48:45,239 --> 00:48:47,960
Speaker 2: we're at, and a real takeaway for me is what

950
00:48:48,200 --> 00:48:51,920
Speaker 2: we're coming from behind. Not on the primary health I

951
00:48:51,960 --> 00:48:54,480
Speaker 2: was surprised, you know, Will suggested that we're maybe the

952
00:48:54,520 --> 00:48:57,239
Speaker 2: third best in the world when it comes to GP

953
00:48:57,400 --> 00:49:02,000
Speaker 2: clinics and that offering digital health service. So my experience

954
00:49:02,000 --> 00:49:06,040
Speaker 2: off it hasn't been particularly great with patient portals and

955
00:49:06,520 --> 00:49:10,560
Speaker 2: literally watching doctors punching stuff into their computer while I'm

956
00:49:10,640 --> 00:49:14,920
Speaker 2: paying for them to do that. But I think we

957
00:49:14,960 --> 00:49:19,279
Speaker 2: all know that in hospitals, our systems have not been

958
00:49:19,360 --> 00:49:24,680
Speaker 2: great at evolving to meet the modern needs of the population,

959
00:49:25,239 --> 00:49:29,920
Speaker 2: and he's definitely put his finger on that. So I

960
00:49:30,000 --> 00:49:33,160
Speaker 2: was surprised and pleasantly surprised to hear a lot of

961
00:49:33,160 --> 00:49:37,759
Speaker 2: the experimentation and innovation that's already going on with population

962
00:49:37,880 --> 00:49:41,520
Speaker 2: health data and that and applying machine learning to it.

963
00:49:41,680 --> 00:49:44,400
Speaker 2: So that's great. There's some really cool pilots and stuff

964
00:49:44,440 --> 00:49:48,600
Speaker 2: going on with medical devices and people's homes, and then

965
00:49:48,719 --> 00:49:52,680
Speaker 2: all of that data eventually could be used in conjunction

966
00:49:52,760 --> 00:49:56,239
Speaker 2: with AI for predictive health. So there's a lot of

967
00:49:56,360 --> 00:50:00,279
Speaker 2: good stuff going on, but some big barriers. There's well,

968
00:50:00,680 --> 00:50:05,680
Speaker 2: we're way behind, We're struggling with a funding crisis in

969
00:50:05,840 --> 00:50:09,960
Speaker 2: health so and the data is not yet in a

970
00:50:10,000 --> 00:50:13,120
Speaker 2: state where frankly it's going to be reliable enough to

971
00:50:13,160 --> 00:50:15,200
Speaker 2: feed into AI system. So a heck of a lot

972
00:50:15,200 --> 00:50:15,759
Speaker 2: of work to do.

973
00:50:16,080 --> 00:50:21,879
Speaker 1: Yeah, project here wants an embattled, difficult project is now dead.

974
00:50:22,000 --> 00:50:28,279
Speaker 1: Project another failed attempt to conglomerate this healthcare data into

975
00:50:28,400 --> 00:50:32,239
Speaker 1: something national and consistent. But we have to get there

976
00:50:32,280 --> 00:50:34,319
Speaker 1: at some point. I have to believe that at some

977
00:50:34,360 --> 00:50:35,960
Speaker 1: point we're going to need to figure it out. And

978
00:50:36,280 --> 00:50:38,600
Speaker 1: maybe trying to create something from the ground up is

979
00:50:38,680 --> 00:50:41,040
Speaker 1: just hubris. Maybe we need to figure out a better

980
00:50:41,480 --> 00:50:45,400
Speaker 1: kind of glue wear approach or something. But whatever it is,

981
00:50:46,440 --> 00:50:49,920
Speaker 1: we need to unlock this data and start enabling it

982
00:50:50,040 --> 00:50:54,120
Speaker 1: the sharing between ACC and hospitals and GPS and like

983
00:50:54,719 --> 00:50:57,560
Speaker 1: will said in the interview, figuring out how people can

984
00:50:57,600 --> 00:51:00,799
Speaker 1: actually input their own data through their wearable where that's

985
00:51:00,880 --> 00:51:04,880
Speaker 1: Aora ring and Apple watch or a glucose monitoring system

986
00:51:05,000 --> 00:51:09,240
Speaker 1: or whatever else, it will really take us a step

987
00:51:09,560 --> 00:51:12,239
Speaker 1: forward and ahead of a lot of other countries if

988
00:51:12,239 --> 00:51:13,520
Speaker 1: we can manage to get there.

989
00:51:13,960 --> 00:51:16,359
Speaker 2: Yeah, one of the barriers he mentioned there, which I

990
00:51:16,400 --> 00:51:21,080
Speaker 2: was a little bit surprised about, is massive concerns about liability.

991
00:51:22,160 --> 00:51:25,840
Speaker 2: So we might need some regulation and compliance tweaks to

992
00:51:26,360 --> 00:51:30,839
Speaker 2: give health providers the confidence to use AI and not

993
00:51:30,880 --> 00:51:32,800
Speaker 2: be worried about getting sued. I thought that would have

994
00:51:32,840 --> 00:51:36,000
Speaker 2: been a bigger deal in a place like the US.

995
00:51:36,040 --> 00:51:39,239
Speaker 2: And actually last week was a visiting expert in AI

996
00:51:39,320 --> 00:51:41,879
Speaker 2: came through New Zealand and she told me exactly that

997
00:51:41,960 --> 00:51:45,400
Speaker 2: she's working with doctors and radiographers and that using AI

998
00:51:45,480 --> 00:51:50,799
Speaker 2: and machine learning to try and improve testing and analyzing

999
00:51:51,000 --> 00:51:56,040
Speaker 2: test results. And she said the senior doctors in particular

1000
00:51:56,080 --> 00:51:59,960
Speaker 2: are really pushing back because they're worried about getting sued

1001
00:52:00,080 --> 00:52:04,239
Speaker 2: and getting their clinic or their hospital suit as well

1002
00:52:04,520 --> 00:52:08,400
Speaker 2: by making a wrong diagnosis. So that's clearly an issue

1003
00:52:08,520 --> 00:52:12,600
Speaker 2: here as well. So having that confidence to be able

1004
00:52:12,640 --> 00:52:16,160
Speaker 2: to put some trust in these systems is going to

1005
00:52:16,200 --> 00:52:21,360
Speaker 2: be key, and maybe we don't have quite the regulatory

1006
00:52:21,480 --> 00:52:22,520
Speaker 2: environment to allow that.

1007
00:52:22,840 --> 00:52:25,000
Speaker 1: Yeah, just putting those policies in place so that you

1008
00:52:25,000 --> 00:52:26,400
Speaker 1: know it's not going to be ramp and AI and

1009
00:52:26,480 --> 00:52:28,880
Speaker 1: making misdiagnoses all over the place. They are going to

1010
00:52:28,880 --> 00:52:34,120
Speaker 1: be in conjunction with good diagnosticians as well, so they're

1011
00:52:34,120 --> 00:52:37,239
Speaker 1: being checked and human in the chair and all that

1012
00:52:37,280 --> 00:52:37,840
Speaker 1: good stuff.

1013
00:52:37,960 --> 00:52:40,680
Speaker 2: Yeah. And the other interesting thing that will point it

1014
00:52:40,680 --> 00:52:44,319
Speaker 2: out when he goes out and talks to hospitals and

1015
00:52:44,360 --> 00:52:47,560
Speaker 2: clinicians and that sort of thing, a lot of them say, no,

1016
00:52:47,640 --> 00:52:49,920
Speaker 2: we don't actually have a policy or even a strategy

1017
00:52:50,280 --> 00:52:54,160
Speaker 2: around using AI. And this is I think we see

1018
00:52:54,200 --> 00:52:58,080
Speaker 2: just about in any area of industry and business at

1019
00:52:58,120 --> 00:53:01,440
Speaker 2: the moment, people immediately jumped to the use case. So

1020
00:53:01,440 --> 00:53:02,960
Speaker 2: I haven't got a strategy for it, but I know

1021
00:53:03,520 --> 00:53:06,279
Speaker 2: my customers want this. And when it comes to healthcare,

1022
00:53:06,880 --> 00:53:12,799
Speaker 2: it's using generative AI to surface experiences for patients. So

1023
00:53:13,080 --> 00:53:18,040
Speaker 2: giving them a chatbot that they can query about sensitive

1024
00:53:18,719 --> 00:53:22,719
Speaker 2: health related issues and they'll get reliable information. It's that

1025
00:53:22,840 --> 00:53:25,400
Speaker 2: sort of stuff that actually the health sector thinks is

1026
00:53:25,560 --> 00:53:27,720
Speaker 2: the frontline of the generative AI revolution.

1027
00:53:27,960 --> 00:53:28,160
Speaker 4: Yep.

1028
00:53:28,640 --> 00:53:31,040
Speaker 3: It's a big revolution and it's coming. We've just got

1029
00:53:31,040 --> 00:53:32,080
Speaker 3: to make sure we're ready for it.

1030
00:53:32,719 --> 00:53:36,480
Speaker 2: Absolutely. So thanks to doctor Will Reedy for coming on

1031
00:53:36,520 --> 00:53:39,560
Speaker 2: the Business of Tech, and we'll have more episodes coming

1032
00:53:39,600 --> 00:53:43,320
Speaker 2: in this ongoing series about how AI is impacting various

1033
00:53:43,360 --> 00:53:46,000
Speaker 2: sectors and industries across a tea.

1034
00:53:46,320 --> 00:53:49,000
Speaker 1: Show notes in the Tech section of the Business Desk

1035
00:53:49,120 --> 00:53:53,799
Speaker 1: website and the podcast is available on iHeartRadio, well your

1036
00:53:53,800 --> 00:53:55,600
Speaker 1: favorite podcast platform.

1037
00:53:55,680 --> 00:53:57,319
Speaker 2: Let us know what you think of the show and

1038
00:53:57,400 --> 00:54:00,600
Speaker 2: drop us a line with suggestions for future gare email

1039
00:54:00,760 --> 00:54:04,239
Speaker 2: Ben on benat Businessdesk dot co, dot MZ, or you

1040
00:54:04,280 --> 00:54:06,400
Speaker 2: can find both of us on LinkedIn and x.

1041
00:54:06,880 --> 00:54:10,120
Speaker 1: Next Thursday, we'll be talking mergers and acquisitions and the

1042
00:54:10,200 --> 00:54:14,040
Speaker 1: complex technical decisions relating to digital infrastructure that need to

1043
00:54:14,040 --> 00:54:16,040
Speaker 1: be made when businesses come together.

1044
00:54:16,480 --> 00:54:18,200
Speaker 2: Until then, have a great week.