WEBVTT - Could AI fix NZ's ailing health system?

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<v Speaker 1>Our health sector is in trouble, short staffed, under resourced,

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<v Speaker 1>overwhelmed with patients who have complex and expensive needs.

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<v Speaker 2>Our hospitals are among the least progressive in the Western

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<v Speaker 2>world when it comes to digital health. Just as artificial

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<v Speaker 2>intelligence shows potential to cut down ADMIN that sucks the

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<v Speaker 2>life out of our doctors and nurses.

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<v Speaker 3>So what can AI do for our health system.

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<v Speaker 2>And what are the barriers that are preventing its uptake?

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<v Speaker 1>On the Business Attack sponsored by two degrees Business this week,

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<v Speaker 1>AI and how it could enhance public health if we

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<v Speaker 1>can get our digital health house in order.

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<v Speaker 4>When you talk to healthcare organizations across New Zealand at

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<v Speaker 4>the moment and just ask the question of the board

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<v Speaker 4>or the teams or even their chief Data and Digital officer,

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<v Speaker 4>do you have an AI strategy? The answer is no.

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<v Speaker 2>Doctor Will Reedy is on the EXTENTSI of New Zealand

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<v Speaker 2>leadership team and part of the consulting firm's global health team. Now, Ben,

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<v Speaker 2>your interview with will really get me one of the

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<v Speaker 2>best overviews of where we're at with AI in health

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<v Speaker 2>space in New Zealand. So everyone stick around for that.

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<v Speaker 1>First though and keeping the health theme going. The Dyson

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<v Speaker 1>Awards for Design Excellence are underway now, annual global awards

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<v Speaker 1>where British inventor Sir James Dyson searches the world for

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<v Speaker 1>the best young designers. The New Zealand winner has just

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<v Speaker 1>been announced and Peter, I believe you were one of

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<v Speaker 1>the judges.

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<v Speaker 2>Yeah, they got me on just to run the ruler

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<v Speaker 2>over it in terms of is this something that's going

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<v Speaker 2>to connect with a big audience. We had health experts

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<v Speaker 2>and other design experts. One of the top designers from

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<v Speaker 2>Dyson was a judge as well. This was the first

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<v Speaker 2>time I judged it, so it was really good. But

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<v Speaker 2>the one that won was one of the most simple

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<v Speaker 2>ones really, but was a design that we thought actually

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<v Speaker 2>had a really addressable market. No one else was doing

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<v Speaker 2>this and it's called the snapcap and a very simple

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<v Speaker 2>device that really helps frontline health professionals deal with the

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<v Speaker 2>containers that medicines come in, glass containers that need to

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<v Speaker 2>be dismantled quickly on the frontline inwards and put in

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<v Speaker 2>a sharpy bin to get rid of this stuff. You

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<v Speaker 2>need a device to pull these things apart and break

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<v Speaker 2>them open, and amazingly there wasn't one in existence.

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<v Speaker 1>Yeah, it's a really cool looking device. It looks kind

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<v Speaker 1>of like our twenty first century bottle opener.

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<v Speaker 3>Very simple in.

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<v Speaker 1>Design, very esthetically pleasing in design, and obviously much needed

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<v Speaker 1>because I think in your interview you talk about the

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<v Speaker 1>fact that frontline health professionals actually do get injuries from

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<v Speaker 1>trying to open these little glass bottles.

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<v Speaker 2>Yeah, they do, and it's just a reminder of how

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<v Speaker 2>much our frontline health workforce have to do all sorts

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<v Speaker 2>of little jobs you don't even think of that is

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<v Speaker 2>up to them. So if we can make their lives

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<v Speaker 2>easier and hopefully do it at a cheap price, that's great.

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<v Speaker 4>You know.

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<v Speaker 2>This was just one of about a dozen designs that

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<v Speaker 2>we looked at. Some of the other stuff that was

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<v Speaker 2>sort of like an exoskeleton that would replace a moon boot.

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<v Speaker 2>There was insuls that can be customizable and three D

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<v Speaker 2>printed for people who have problems with their feet if

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<v Speaker 2>they've got potentially diabetes or something like that. There was

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<v Speaker 2>a little sense so that you can put on the

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<v Speaker 2>back of your shoulder if you're out jogging on a street.

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<v Speaker 2>It will give you haptic feedback if it thinks that

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<v Speaker 2>a car is getting too close to you or is

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<v Speaker 2>potentially going to take you out, so great to see

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<v Speaker 2>that creativity.

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<v Speaker 1>I love the simplicity of the winning design, and listening

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<v Speaker 1>to your conversation with the designer, Jack Pew gave me

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<v Speaker 1>some really good insight into kind of how he got

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<v Speaker 1>there and what he was thinking and the balance of

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<v Speaker 1>talent and pragmatism that led him there. So let's have

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<v Speaker 1>a listen to that interview.

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<v Speaker 2>Now, Jack Pugh, Welcome to the Business of Tech, and

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<v Speaker 2>congratulations on winning the New Zealand Dyson Awards. You were

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<v Speaker 2>crowned the best design off about a dozen. I was

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<v Speaker 2>under the actual judging panel, so I saw all of

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<v Speaker 2>these dozen or so designs. Tell us a little bit

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<v Speaker 2>about yourself, Jack, Where are you from? How did you

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<v Speaker 2>get into design?

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<v Speaker 5>Thanks Peter. So, I'm from christ Church, originally going to

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<v Speaker 5>sort of come up to the Capital to study at Messy.

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<v Speaker 5>I've always been quite interested in design when I was little.

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<v Speaker 5>Any when you ask would tell you that I always

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<v Speaker 5>wanted to be an inventor. So it's really cool to

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<v Speaker 5>be able to sort of do this and have some

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<v Speaker 5>recognition for it.

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<v Speaker 2>You've won this award. Take us through your winning design.

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<v Speaker 2>It's called the cap Snap what did you set out

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<v Speaker 2>to achieve with the cap snap and what is it?

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<v Speaker 5>Well, it's a simple tool for a simple problem in

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<v Speaker 5>the theory, a medical ball opener for health professionals working

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<v Speaker 5>with medications where they'd look to recycle them by taking

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<v Speaker 5>off the aluminium crimpsy your caps, or to open these

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<v Speaker 5>little glass vials which old medication called ampules. There's some

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<v Speaker 5>safety risks associated with both, and so this tool kind

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<v Speaker 5>of lets them do it in a real quick and

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<v Speaker 5>easy way.

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<v Speaker 2>Yeah, it looks like a bottle opener, and essentially it

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<v Speaker 2>is a bottle opener. Yeah, functionally, because you know, just

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<v Speaker 2>to try and visualize it, and unfortunately, I've got a

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<v Speaker 2>lot of experience of this now visiting sick relatives in hospital.

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<v Speaker 2>You'll see a medicine bottle. It's got some in this

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<v Speaker 2>in the case, i know, immunotherapy liquid drug in it.

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<v Speaker 2>It's a glass bottle, it's got an aluminium cap which

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<v Speaker 2>is snug on it. You've got to separate those two

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<v Speaker 2>things to recycle these materials. You also have, as you say, amples,

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<v Speaker 2>these little sort of sealed glass containers that you have

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<v Speaker 2>to crack open literally to get the liquid out of them.

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<v Speaker 2>But they're really damn fiddly. And what I loved about

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<v Speaker 2>your design. You've got one little device that probably doesn't

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<v Speaker 2>cost that much to make, and it does both. It

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<v Speaker 2>removes that aluminum casing from the top of the glass

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<v Speaker 2>bottle and you can insert the ampule into the bottom

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<v Speaker 2>of it and crack it in half. So the big

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<v Speaker 2>benefit I saw from it is cutting down on the

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<v Speaker 2>potential for nurses and doctors to hurt themselves going through

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<v Speaker 2>that process cutting themselves on glass or on aluminium or

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<v Speaker 2>a tool trying to take that off. It does both

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<v Speaker 2>of those purposes.

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<v Speaker 5>Yeah, that's right. The ways that people are currently getting

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<v Speaker 5>around both those issues is they're using kind of makeshift

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<v Speaker 5>solutions alcohol pads to step ampules, so at least if

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<v Speaker 5>it does shedd in an unpredictable way, the glass will

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<v Speaker 5>go into the air instead. And with those aluminium's caps,

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<v Speaker 5>they're real fiddly. If you ever see someone tri tech

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<v Speaker 5>one off, they don't have good tools suited for it,

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<v Speaker 5>so they're using kind of four steps and kind of

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<v Speaker 5>straining their hands to be able to pull them off.

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<v Speaker 2>Yeah, it's dangerous. And you know what I love is

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<v Speaker 2>the simplicity of this. Take us through your your design

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<v Speaker 2>approach to this. This is a very specialized piece of equipment,

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<v Speaker 2>So what work did you go through sort of talking

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<v Speaker 2>to frontline health practitioners to inform the design of cap snap.

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<v Speaker 5>Kind of things started off just kind of as a

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<v Speaker 5>conversation on what are the issues, kind of what are

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<v Speaker 5>they doing at the moment, and then from there tried

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<v Speaker 5>to think of kind of a range of solutions. What

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<v Speaker 5>would something handheld and quick and easy look like. First,

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<v Speaker 5>maybe something mounted to the wall, or like a mix

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<v Speaker 5>of both, something that's kind of a bit of a hybrid.

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<v Speaker 5>And I kind of drew up some ideas and sat

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<v Speaker 5>down with a member of the team and we kind

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<v Speaker 5>of talked through some of the ideas and we ended

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<v Speaker 5>up deciding that something that was real quick and small

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<v Speaker 5>and portable to be able to be moved around if necessary,

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<v Speaker 5>would be the kind of the best way forward. And

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<v Speaker 5>then from there I kind of springboard into a bit

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<v Speaker 5>of antive process, found some studies overseas looking at similar things.

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<v Speaker 5>We kind of developed into well, it's a bottle with

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<v Speaker 5>a cap, how about a botopner. We'll keep it super

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<v Speaker 5>super easy. And then some of the issues that came

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<v Speaker 5>up with one size kind of we couldn't have a

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<v Speaker 5>one size fits all approach to have it be real functional,

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<v Speaker 5>So we looked at what if we had a bollopner

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<v Speaker 5>that could sort of adjust and through that kind of

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<v Speaker 5>linear motion, we could incorporate that really seamlessly into the

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<v Speaker 5>ampule snapping function.

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<v Speaker 2>It's interesting this was not, by any means the most

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<v Speaker 2>elaborate or sophisticated design that we came across the judging

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<v Speaker 2>panel for the dice and awards. There were all sorts

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<v Speaker 2>of quite elaborate designs that were probably required a lot

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<v Speaker 2>more design forinesse. But what I loved about this one

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<v Speaker 2>is that when I did research about it, no one

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<v Speaker 2>was really doing this. Why do you think no one

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<v Speaker 2>in the health profession has come up with something like this?

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<v Speaker 5>Yet you get these things like the ampules or the

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<v Speaker 5>crimseal caps, and then the job has to be done,

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<v Speaker 5>so you come up with a workaround and you kind

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<v Speaker 5>of that's just what you do. And then in a

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<v Speaker 5>good and bad way, people don't make a lot of

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<v Speaker 5>noise about it. And so we've got all this potential

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<v Speaker 5>for interesting design solutions or just simple little fixes for

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<v Speaker 5>these health staff we're having to do all these workarounds

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<v Speaker 5>on a kind of a daily basis that just sort

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<v Speaker 5>of someone needs to have a look at and try

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<v Speaker 5>to come up with something.

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<v Speaker 2>And that's exactly what you've done. You know, Sir James

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<v Speaker 2>Dyson is famous for being a real iterative designer. Did

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<v Speaker 2>you go through numerous iterations and either future obvious ones

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<v Speaker 2>you see for this device to make it even better?

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<v Speaker 5>Yeah, So a real fun part of the process is

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<v Speaker 5>going through the iterations. So we went through a few

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<v Speaker 5>different iterations looking at how I can keep it as

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<v Speaker 5>simple as possible and reduce kind of them the number

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<v Speaker 5>of mechanical parts in it. There are kind of two approaches.

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<v Speaker 5>One kind of looked like a claw, if that makes sense,

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<v Speaker 5>and that would allow it to be able to open

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<v Speaker 5>a bunch of different sizes without having to use that

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<v Speaker 5>slighter function. But the issue we kind of ran into

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<v Speaker 5>with some of those prototypes is that it wasn't sort

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<v Speaker 5>of snappy and intuitive. If you looked at it, you

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<v Speaker 5>wouldn't know what you're looking at, and that was a

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<v Speaker 5>real big part of the design is making it just

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<v Speaker 5>super straightforward that you can look at it and pretty

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<v Speaker 5>much figure out how to use it without any instruction.

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<v Speaker 5>There's so little time to teach people about new things

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<v Speaker 5>when a new tool comes in kind of in these busy,

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<v Speaker 5>bustling environments that that's kind of what you want from

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<v Speaker 5>a tool that's going to do just this quick and

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<v Speaker 5>simple job.

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<v Speaker 2>Yeah, Because it literally looks like a bottle opener, so

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<v Speaker 2>instantly you have that recognition. You go, oh, I've got

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<v Speaker 2>a bottle here, This must go around the neck. And

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<v Speaker 2>then you've got the sort of the hidden compartment on

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<v Speaker 2>the bottom of it, which is for breaking the ample

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<v Speaker 2>as well. So I guess it's a bit of education

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<v Speaker 2>required so that people know to use that. You're sitting

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<v Speaker 2>in your lab as we talk here, you've got a

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<v Speaker 2>three D printer behind you. Was that useful? Do you

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<v Speaker 2>do a lot of iterations and design work and prototyping

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<v Speaker 2>using three D printing?

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<v Speaker 5>Yeah? So three D printing was a huge enabler for

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<v Speaker 5>this project. That and water jet cutting was another super

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<v Speaker 5>useful part in sort of smashing out these prototypes. I

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<v Speaker 5>tried to kind of reduce the amount of plastic that

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<v Speaker 5>I used where I could, so I tried to make

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<v Speaker 5>my prototypes I can kind of hot swap between the

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<v Speaker 5>different inserts to try the different geometries to see what

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<v Speaker 5>was kind of the best and most effective fit when

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<v Speaker 5>kind of figuring out some of the measurements. So yeah,

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<v Speaker 5>definitely super pivotal and being able to quickly run through ideas.

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<v Speaker 2>So design was only sort of part of the criteria

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<v Speaker 2>for winning this award. A big part of it was

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<v Speaker 2>the real world application and the potential for this to

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<v Speaker 2>actually go on and be used. It's all well and

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<v Speaker 2>good to design something that just never leaves the labor

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<v Speaker 2>with the prototyping stage, and we did see some sort

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<v Speaker 2>of designs like that. What are the next steps for you?

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<v Speaker 2>Is this something that you'd like to pursue potentially as

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<v Speaker 2>a business that the Snapcap try and get it out

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<v Speaker 2>there into hospitals and clinics around the world.

0:12:12.559 --> 0:12:15.520
<v Speaker 5>I would like to see it in people's hands and

0:12:15.640 --> 0:12:18.120
<v Speaker 5>just making their lives a little bit easier for those tasks.

0:12:18.120 --> 0:12:19.960
<v Speaker 5>So I'm able to put it a little bit of

0:12:19.960 --> 0:12:22.480
<v Speaker 5>time in to be able to work up the design

0:12:22.600 --> 0:12:25.360
<v Speaker 5>a little bit further. I've got some help from some

0:12:25.400 --> 0:12:29.199
<v Speaker 5>awesome people at a couple of the other hospitals around

0:12:29.200 --> 0:12:32.439
<v Speaker 5>the country been able to kind of cast the net

0:12:32.480 --> 0:12:36.120
<v Speaker 5>a little bit wider see how the same issues are

0:12:36.200 --> 0:12:39.080
<v Speaker 5>sort of getting received across those different sites. We're hoping

0:12:39.120 --> 0:12:42.120
<v Speaker 5>to be able to work it into something that we

0:12:42.160 --> 0:12:43.760
<v Speaker 5>can get into people's hands.

0:12:43.960 --> 0:12:46.160
<v Speaker 2>The judges we're talking about this, we sort of thought

0:12:46.880 --> 0:12:49.240
<v Speaker 2>this is great, and you submitted a video literally off

0:12:49.880 --> 0:12:53.440
<v Speaker 2>a nurse who was I think snapping an ampool who

0:12:53.480 --> 0:12:55.440
<v Speaker 2>actually cut herself.

0:12:55.679 --> 0:12:58.320
<v Speaker 5>It wasn't scripted, but we had to use that tape.

0:12:58.440 --> 0:13:00.800
<v Speaker 2>Well, that illustrates it very well. But we were thinking,

0:13:01.440 --> 0:13:03.960
<v Speaker 2>why don't you just I mean, you need to break

0:13:04.000 --> 0:13:07.720
<v Speaker 2>the ampull, but in terms of taking the aluminum cap

0:13:07.880 --> 0:13:09.640
<v Speaker 2>off the bottle, why don't you just chuck it all

0:13:09.760 --> 0:13:12.120
<v Speaker 2>in a bin and automate that later.

0:13:12.440 --> 0:13:14.800
<v Speaker 5>That was an idea that we kind of tossed around

0:13:14.840 --> 0:13:18.880
<v Speaker 5>at the start. But the kind of reality of kind

0:13:18.920 --> 0:13:20.920
<v Speaker 5>of this product is that it'd be all well and

0:13:20.960 --> 0:13:23.800
<v Speaker 5>good to make something real, awesome and automated, and I

0:13:23.880 --> 0:13:26.880
<v Speaker 5>actually going into this project that's kind of what I

0:13:26.920 --> 0:13:29.079
<v Speaker 5>was going to look to do. But then talking with

0:13:29.600 --> 0:13:32.319
<v Speaker 5>the people and like hearing about the issues and then

0:13:33.040 --> 0:13:36.640
<v Speaker 5>learning about the ampuel side of things, we've sort of

0:13:36.640 --> 0:13:39.880
<v Speaker 5>determined that while technically it could be considered a band

0:13:39.920 --> 0:13:42.800
<v Speaker 5>aid solution, it would be on the fastest track to

0:13:42.840 --> 0:13:45.680
<v Speaker 5>be able to have this issue kind of be sold.

0:13:45.720 --> 0:13:48.120
<v Speaker 2>That's what we loved about it, the simplicity of it.

0:13:48.200 --> 0:13:51.280
<v Speaker 2>I mean, presumably this, if you get this into production,

0:13:51.320 --> 0:13:54.120
<v Speaker 2>it wouldn't be a super expensive device to create either.

0:13:54.360 --> 0:13:57.560
<v Speaker 5>Yeah, we're hoping to well, we're working to get the

0:13:57.600 --> 0:14:01.360
<v Speaker 5>part count down as much as possible. Engineers in christ

0:14:01.440 --> 0:14:04.120
<v Speaker 5>Church I've had some great input on on clever ways

0:14:04.120 --> 0:14:08.199
<v Speaker 5>to really simplify that mechanism to even just three or

0:14:08.200 --> 0:14:08.760
<v Speaker 5>four parts.

0:14:09.080 --> 0:14:09.440
<v Speaker 3>Tops.

0:14:09.840 --> 0:14:11.040
<v Speaker 2>Where are you working at the moment?

0:14:11.320 --> 0:14:15.440
<v Speaker 5>So I'm working out of Wellington Regional Hospital, part of

0:14:15.480 --> 0:14:19.360
<v Speaker 5>the Futtow Water Improvement team based out of here, so

0:14:19.680 --> 0:14:22.120
<v Speaker 5>day to day I'm sort of looking at other issues,

0:14:22.160 --> 0:14:24.800
<v Speaker 5>but it's still we're able to work on some innovative

0:14:24.800 --> 0:14:26.800
<v Speaker 5>solutions sort of within the hospital, which is a really

0:14:26.800 --> 0:14:28.120
<v Speaker 5>exciting sort of opportunity.

0:14:28.200 --> 0:14:30.480
<v Speaker 2>Well, brilliant, you're in exactly the right place and they're

0:14:30.520 --> 0:14:34.240
<v Speaker 2>lucky to have this design brain on the team there.

0:14:34.320 --> 0:14:38.000
<v Speaker 2>So good luck for the next phase. And he's hoping

0:14:38.040 --> 0:14:41.000
<v Speaker 2>we see snapcap in hospital wards before too long.

0:14:41.080 --> 0:14:48.160
<v Speaker 1>Well hat fingers crossed, great young talent, Jack Pugh. Great

0:14:48.200 --> 0:14:51.000
<v Speaker 1>to have those kinds of people coming through New Zealand,

0:14:51.080 --> 0:14:54.680
<v Speaker 1>so big well done to him and look forward to

0:14:54.680 --> 0:14:58.000
<v Speaker 1>seeing what he does next. So that's innovation and health hardware,

0:14:58.200 --> 0:15:01.440
<v Speaker 1>and our topic of focus this week is AI in healthcare.

0:15:02.120 --> 0:15:05.400
<v Speaker 1>Doctor Will Reedy is one of the country's leading experts

0:15:05.440 --> 0:15:08.480
<v Speaker 1>in digital health. He joined Accentua around a year ago

0:15:08.560 --> 0:15:11.040
<v Speaker 1>and is still a practicing doctor one day a week

0:15:11.240 --> 0:15:12.840
<v Speaker 1>in the county's Monaco area.

0:15:13.000 --> 0:15:15.840
<v Speaker 2>He's a lot of experience helping with the rollout of

0:15:15.960 --> 0:15:19.520
<v Speaker 2>digital health systems all over the world, so he's pretty

0:15:19.520 --> 0:15:24.200
<v Speaker 2>well placed to compare and contrast our preparedness and progress

0:15:24.280 --> 0:15:27.320
<v Speaker 2>in the digital health space compared to the likes of Europe,

0:15:27.320 --> 0:15:29.920
<v Speaker 2>the US and Australia, where he's also worked.

0:15:30.240 --> 0:15:33.520
<v Speaker 3>Will's also really interested in the potential to reduce health

0:15:33.560 --> 0:15:36.600
<v Speaker 3>inequities using digital health tech, that is, if we can

0:15:36.640 --> 0:15:39.720
<v Speaker 3>trust machine learning and large language models to get it right.

0:15:40.160 --> 0:15:44.920
<v Speaker 2>So here's Ben's interview with Accentures doctor Will Reedy.

0:15:50.880 --> 0:15:52.800
<v Speaker 3>Good, Hey, Will, how are you great? Thanks?

0:15:52.840 --> 0:15:55.000
<v Speaker 1>Ben good, Welcome to the Business of Tech podcast. Thanks

0:15:55.040 --> 0:15:57.440
<v Speaker 1>for joining us. As so, why don't we start with

0:15:57.440 --> 0:16:00.680
<v Speaker 1>if you could just give us a very quick summary of.

0:16:00.680 --> 0:16:02.320
<v Speaker 3>Who you are, what you do, and a little bit

0:16:02.320 --> 0:16:03.160
<v Speaker 3>about your background.

0:16:03.280 --> 0:16:05.280
<v Speaker 4>So yeah, look, it's pleasure to be chatting with you

0:16:05.320 --> 0:16:08.600
<v Speaker 4>this morning. So I guess my full time job is

0:16:08.640 --> 0:16:12.040
<v Speaker 4>working for Accenture, is their how order health and wellness

0:16:12.120 --> 0:16:15.200
<v Speaker 4>lead here in New Zealand with the goal to help

0:16:15.240 --> 0:16:17.880
<v Speaker 4>the health system transform given some of the real challenges

0:16:18.120 --> 0:16:19.720
<v Speaker 4>that many of us aware of at the moment in

0:16:19.760 --> 0:16:23.040
<v Speaker 4>the health system. And then one day a week I

0:16:23.120 --> 0:16:26.000
<v Speaker 4>do a shift in the surgical services at Middlemore so

0:16:26.040 --> 0:16:28.880
<v Speaker 4>that can be in the emergency department and the clinics,

0:16:28.880 --> 0:16:31.200
<v Speaker 4>in the wards and sometimes in theater. So I guess

0:16:31.200 --> 0:16:33.840
<v Speaker 4>that keeps it real in terms of understanding the pulse

0:16:33.880 --> 0:16:36.800
<v Speaker 4>of the health system at the clinical front line. But

0:16:36.880 --> 0:16:39.240
<v Speaker 4>I guess ultimately the passion is to try and transform

0:16:39.240 --> 0:16:41.960
<v Speaker 4>health systems with technology and things like AI.

0:16:42.400 --> 0:16:46.400
<v Speaker 1>It must be quite start going from in Accenture talking

0:16:46.440 --> 0:16:49.440
<v Speaker 1>about the latest and greatest worldwide AI this, and then

0:16:49.440 --> 0:16:53.440
<v Speaker 1>you go into a hospital and very different story.

0:16:53.440 --> 0:16:54.800
<v Speaker 3>I'd imagine, Yeah, it is.

0:16:54.960 --> 0:16:58.040
<v Speaker 4>It's really interesting and it's generally accepted that in our

0:16:58.120 --> 0:17:00.920
<v Speaker 4>hospital system, not so much in our GP systems or

0:17:00.960 --> 0:17:04.560
<v Speaker 4>primary care systems. We are the least digitized hospitals now

0:17:04.600 --> 0:17:06.959
<v Speaker 4>in the developed world. So it is a little bit

0:17:07.000 --> 0:17:09.399
<v Speaker 4>of a change in terms of the global work that

0:17:09.480 --> 0:17:12.879
<v Speaker 4>I do and seeing what's going on in terms of

0:17:12.960 --> 0:17:15.760
<v Speaker 4>transformation and how far some other countries are ahead of us.

0:17:16.200 --> 0:17:19.560
<v Speaker 4>Also being quite digital my day job at Accenture and

0:17:19.600 --> 0:17:22.080
<v Speaker 4>then you know, having to use a pen and paper

0:17:22.080 --> 0:17:23.160
<v Speaker 4>on Fridays pretty much.

0:17:23.240 --> 0:17:23.960
<v Speaker 3>Yeah.

0:17:24.240 --> 0:17:27.639
<v Speaker 1>But also I guess conversely, that also just helps you

0:17:27.680 --> 0:17:30.400
<v Speaker 1>to understand the potential for change, right, and that actually

0:17:30.400 --> 0:17:33.520
<v Speaker 1>how far we can go when we start to implement

0:17:33.520 --> 0:17:34.040
<v Speaker 1>modern tech.

0:17:34.200 --> 0:17:36.160
<v Speaker 4>Yeah. I think it's easy to kind of look at

0:17:36.160 --> 0:17:39.800
<v Speaker 4>the I guess, the lower level of digitization and tech

0:17:39.880 --> 0:17:44.000
<v Speaker 4>enable transformation in the New Zealand health system today. But

0:17:44.040 --> 0:17:47.240
<v Speaker 4>it's also an opportunity, and my words are an opportunity

0:17:47.280 --> 0:17:49.399
<v Speaker 4>to kind of leap frog some of the approaches and

0:17:49.440 --> 0:17:52.639
<v Speaker 4>the thinking. So I do I'm quite optimistic about some

0:17:52.680 --> 0:17:54.560
<v Speaker 4>of those opportunities for New Zealand just to go, hey,

0:17:54.560 --> 0:17:57.000
<v Speaker 4>where are we trying to get to? What would we

0:17:57.040 --> 0:17:59.080
<v Speaker 4>do differently? Could we leap frog ahead of some of

0:17:59.080 --> 0:18:00.000
<v Speaker 4>the other jurisdictions.

0:18:00.119 --> 0:18:01.960
<v Speaker 1>Yeah, it's interesting you use that term leap frog. That

0:18:01.960 --> 0:18:05.000
<v Speaker 1>seems to be kind of a relatively common New Zealand

0:18:05.000 --> 0:18:07.359
<v Speaker 1>experience where we kind of fall behind a little bit

0:18:07.359 --> 0:18:09.399
<v Speaker 1>and then we go, oh, let's catch up, and in

0:18:09.480 --> 0:18:13.160
<v Speaker 1>doing so we kind of go ahead and really hit

0:18:13.200 --> 0:18:15.280
<v Speaker 1>that cutting edge again. Is that kind of what you're

0:18:15.280 --> 0:18:16.560
<v Speaker 1>seeing happening at the moment?

0:18:17.480 --> 0:18:19.919
<v Speaker 4>I guess, I see the opportunity and it's really interesting

0:18:19.920 --> 0:18:23.479
<v Speaker 4>to share with you. It's funny how some of the

0:18:23.520 --> 0:18:28.399
<v Speaker 4>AI technologies are becoming quite pervasive in healthcare. And a

0:18:28.440 --> 0:18:31.440
<v Speaker 4>colleague of mine has been leading the way in gp

0:18:31.640 --> 0:18:34.880
<v Speaker 4>Land all it or primary care in driving the adoption

0:18:35.000 --> 0:18:37.800
<v Speaker 4>of a product called Nabler, which allows you, with the

0:18:37.840 --> 0:18:41.080
<v Speaker 4>permission of the patient, to record the voice around the

0:18:41.119 --> 0:18:44.199
<v Speaker 4>interaction with the patient and then convert that to text

0:18:44.320 --> 0:18:46.840
<v Speaker 4>and then put it into the GP system. And our

0:18:46.880 --> 0:18:49.280
<v Speaker 4>GPS I think about ranked about third in the world

0:18:49.359 --> 0:18:52.600
<v Speaker 4>in terms of their levels of digitization. And that's been

0:18:52.880 --> 0:18:55.040
<v Speaker 4>kind of the same kind of I guess measurement that's

0:18:55.040 --> 0:18:57.239
<v Speaker 4>been in place for about twenty years. So primary care

0:18:57.280 --> 0:18:59.640
<v Speaker 4>in New Zealand's actually been I guess at the forefront

0:18:59.640 --> 0:19:02.600
<v Speaker 4>of tech for some time. And then you know, what

0:19:02.760 --> 0:19:05.400
<v Speaker 4>he's seeing is first and foremost the patients and they're

0:19:05.560 --> 0:19:08.080
<v Speaker 4>farno who are in the room with him, are seeing

0:19:08.119 --> 0:19:11.320
<v Speaker 4>some benefits around more eye contact not turning around and

0:19:11.320 --> 0:19:13.480
<v Speaker 4>typing stuff in the computer and in terms of the

0:19:13.480 --> 0:19:17.800
<v Speaker 4>fifteen minute consult that is common in primary care. The

0:19:17.840 --> 0:19:20.840
<v Speaker 4>other side that he's finding really interesting is efficiencies within

0:19:20.880 --> 0:19:24.040
<v Speaker 4>the consult around typing everything down because we often paraphrase

0:19:24.119 --> 0:19:26.520
<v Speaker 4>what the family or the patients say to us given

0:19:26.680 --> 0:19:29.640
<v Speaker 4>the time limit. But what he's also finding, as our

0:19:29.720 --> 0:19:32.320
<v Speaker 4>colleagues across about one dred and one hundred and fifty

0:19:32.320 --> 0:19:35.719
<v Speaker 4>practices in New Zealand, is that the cognitive load, that

0:19:35.760 --> 0:19:38.520
<v Speaker 4>the mental load is a lot less because you're having

0:19:38.560 --> 0:19:41.840
<v Speaker 4>to recite and think things through. So piece of technology

0:19:41.880 --> 0:19:47.040
<v Speaker 4>came along relatively recently and it's been adopted in about

0:19:47.080 --> 0:19:49.720
<v Speaker 4>three hundred practices across New Zealand, which is about fifteen

0:19:49.760 --> 0:19:52.600
<v Speaker 4>percent of the GP practices. So it's really interesting around

0:19:52.640 --> 0:19:55.640
<v Speaker 4>that leap frog concept. Grab an idea and go with it. Yep.

0:19:56.080 --> 0:19:57.639
<v Speaker 1>It is an interesting idea and it is one that

0:19:57.720 --> 0:20:02.000
<v Speaker 1>has you know, it could have been earlier probably, but

0:20:02.160 --> 0:20:04.240
<v Speaker 1>the reality is is that the technology that we have

0:20:04.359 --> 0:20:07.280
<v Speaker 1>now around AI has just made it super accessible.

0:20:08.160 --> 0:20:08.960
<v Speaker 3>Are there other.

0:20:08.880 --> 0:20:11.800
<v Speaker 1>Areas that you're seeing that trend that the modern AI

0:20:12.119 --> 0:20:13.720
<v Speaker 1>tools that we have now in the last couple of

0:20:13.800 --> 0:20:17.760
<v Speaker 1>years have made things possible suddenly that would have seemed

0:20:17.760 --> 0:20:19.040
<v Speaker 1>really onerous in the past.

0:20:19.280 --> 0:20:22.280
<v Speaker 4>Yeah, look, it's interesting. I'll probably give some broader contexts.

0:20:22.320 --> 0:20:24.719
<v Speaker 4>So one of the challenges in the health system at

0:20:24.720 --> 0:20:26.840
<v Speaker 4>the moment is how do you adopt AI? And I

0:20:26.880 --> 0:20:30.720
<v Speaker 4>guess people translate AI to generative AI at the moment,

0:20:30.760 --> 0:20:33.520
<v Speaker 4>so it's just careful to be specific about that. And

0:20:33.560 --> 0:20:37.560
<v Speaker 4>so the adoption piece is and I'll get down to

0:20:37.600 --> 0:20:40.639
<v Speaker 4>some of the use cases shortly but effectively, when you

0:20:40.680 --> 0:20:43.320
<v Speaker 4>talk to healthcare organizations across New Zealand at the moment,

0:20:43.359 --> 0:20:45.360
<v Speaker 4>and just ask the question of the board or their

0:20:45.359 --> 0:20:47.800
<v Speaker 4>exec teams or even their chief Data and Digital officer,

0:20:48.600 --> 0:20:52.800
<v Speaker 4>do you have an AI strategy? And the answer is no,

0:20:53.200 --> 0:20:55.760
<v Speaker 4>which is really interesting. And then the second question you

0:20:55.840 --> 0:20:58.080
<v Speaker 4>ask in the context of adoption of AI, if you

0:20:58.119 --> 0:21:00.720
<v Speaker 4>went down that path, is do you have a policy

0:21:01.040 --> 0:21:05.600
<v Speaker 4>around how you'll adopt AI as an organization? And generally

0:21:05.640 --> 0:21:09.040
<v Speaker 4>speaking not many have that either. But then you go, okay,

0:21:09.040 --> 0:21:10.800
<v Speaker 4>put that to one size. You don't have a strategy,

0:21:10.800 --> 0:21:12.880
<v Speaker 4>you don't have a policy. What are the core use

0:21:12.920 --> 0:21:14.960
<v Speaker 4>cases that you've been thinking about that you'd like to

0:21:14.960 --> 0:21:17.960
<v Speaker 4>get into your organization? In the next twelve months and

0:21:18.320 --> 0:21:21.239
<v Speaker 4>again that's where it opens up interesting conversations. And so

0:21:21.600 --> 0:21:23.920
<v Speaker 4>when we ran a leadership summit earlier in the year

0:21:23.960 --> 0:21:26.159
<v Speaker 4>with the Chief Medical Officer for Health New Zealand and

0:21:26.160 --> 0:21:28.440
<v Speaker 4>the head of the AI Advisory Group, the most common

0:21:28.520 --> 0:21:31.840
<v Speaker 4>use case was surfacing genitive AI experiences to patients or

0:21:31.840 --> 0:21:35.640
<v Speaker 4>family or FANO, so things like education CHAP as an example,

0:21:35.680 --> 0:21:38.560
<v Speaker 4>I've just been diagnosed with diabetes. What can I expect

0:21:38.880 --> 0:21:43.159
<v Speaker 4>and how do you get repeatable advice to patients where

0:21:43.200 --> 0:21:45.680
<v Speaker 4>doctor or nurse isn't available as an example.

0:21:45.720 --> 0:21:48.560
<v Speaker 1>That's kind of very similar to the recent announcement around

0:21:48.920 --> 0:21:52.960
<v Speaker 1>GOVGBT right where it's the ingesting a bunch of government documents,

0:21:53.640 --> 0:21:58.200
<v Speaker 1>government pages and then being able to chatbot style ask

0:21:58.320 --> 0:22:00.720
<v Speaker 1>questions and get information about this governments.

0:22:00.920 --> 0:22:02.560
<v Speaker 4>And I think I think in New Zealand, you know,

0:22:02.960 --> 0:22:05.760
<v Speaker 4>the health systems around the world, particularly in socialized health

0:22:05.760 --> 0:22:09.400
<v Speaker 4>systems like New Zealand, Australia in the UK, is everything's

0:22:09.440 --> 0:22:13.879
<v Speaker 4>quite fragmented. So if you've got a consistent education piece

0:22:14.560 --> 0:22:17.240
<v Speaker 4>experience for patients where no matter what question they ask,

0:22:17.280 --> 0:22:20.680
<v Speaker 4>they are a consistent answer, it's actually a big benefit

0:22:20.720 --> 0:22:24.159
<v Speaker 4>in terms of patients being empowered to manage a chronic

0:22:24.960 --> 0:22:28.000
<v Speaker 4>I guess condition like diabetes. So yeah, we are quite

0:22:28.040 --> 0:22:30.480
<v Speaker 4>surprised that everybody is going, how do we surface stuff

0:22:30.520 --> 0:22:33.480
<v Speaker 4>to patients? Which is really interesting, So that's open up

0:22:33.480 --> 0:22:36.040
<v Speaker 4>a new world. The second kind of area, broadly was

0:22:36.240 --> 0:22:38.679
<v Speaker 4>what we call the clinical or the front line or

0:22:38.680 --> 0:22:41.280
<v Speaker 4>the front of office, and so lots of use cases

0:22:41.320 --> 0:22:45.360
<v Speaker 4>around voice detext and reducing the burden of me using

0:22:45.359 --> 0:22:50.920
<v Speaker 4>pen and paper as an example, some benefits around managing inboxes,

0:22:50.920 --> 0:22:53.800
<v Speaker 4>around lab results coming in because a lot of lab

0:22:53.840 --> 0:22:55.960
<v Speaker 4>tests that we order for patients, we're kind of trying

0:22:56.000 --> 0:22:57.919
<v Speaker 4>to rule something out and we want it to be

0:22:58.720 --> 0:23:00.720
<v Speaker 4>if it comes back normal, then we don't really need

0:23:00.800 --> 0:23:03.600
<v Speaker 4>to process that forget where I'm coming from. And then

0:23:03.600 --> 0:23:06.440
<v Speaker 4>the back of office piece around workforce management and finance,

0:23:06.520 --> 0:23:08.960
<v Speaker 4>procurement and supply chain. So those are the broad areas.

0:23:09.240 --> 0:23:11.480
<v Speaker 4>So the reason I explain those broad areas around use

0:23:11.520 --> 0:23:15.720
<v Speaker 4>cases is people are understanding there is potential to apply

0:23:16.320 --> 0:23:19.080
<v Speaker 4>in particular generative AI to those use cases. It's just

0:23:19.280 --> 0:23:21.280
<v Speaker 4>where are they going to get the biggest impact around

0:23:21.280 --> 0:23:24.200
<v Speaker 4>healthcare in New Zealand. So I guess your question was, Hey,

0:23:25.400 --> 0:23:28.479
<v Speaker 4>are people's eyes being opened up. Yes, they are because

0:23:28.520 --> 0:23:32.200
<v Speaker 4>they're learning around use cases against offshore at the moment

0:23:32.240 --> 0:23:34.560
<v Speaker 4>and going, hey, that could easily be applied here in

0:23:34.560 --> 0:23:35.520
<v Speaker 4>the New Zealand context.

0:23:35.680 --> 0:23:39.240
<v Speaker 1>Yeah, sticking with the generative AI theme, you know, I've

0:23:39.480 --> 0:23:42.080
<v Speaker 1>often looked at the health of Fire website as it's

0:23:42.119 --> 0:23:44.000
<v Speaker 1>now called, and just that that's such a huge corpus

0:23:44.280 --> 0:23:46.720
<v Speaker 1>of information and data, like it seems like a great

0:23:46.800 --> 0:23:51.720
<v Speaker 1>opportunity for something like an educational chatbot, But there is

0:23:51.760 --> 0:23:54.600
<v Speaker 1>a lot of risk with that. Where chatbots are known

0:23:54.640 --> 0:23:57.280
<v Speaker 1>to want to please people, they're known to kind of

0:23:57.880 --> 0:24:00.360
<v Speaker 1>sometimes make things up if they're relying heavily on those

0:24:00.520 --> 0:24:04.560
<v Speaker 1>LLAM models in the background. How are people in the

0:24:04.600 --> 0:24:08.400
<v Speaker 1>health sector thinking about those risks considering the sensitivity.

0:24:08.880 --> 0:24:12.600
<v Speaker 4>Yeah, it's a really good question. I think the interesting

0:24:12.640 --> 0:24:18.760
<v Speaker 4>context is do healthcare organizations need to adopt genai or not?

0:24:19.080 --> 0:24:22.399
<v Speaker 4>And the general trend overseas is they're all adopting it

0:24:22.760 --> 0:24:25.800
<v Speaker 4>to see what the potential is, but not necessarily to

0:24:25.920 --> 0:24:29.520
<v Speaker 4>roll it out at scale. And the reason they're doing

0:24:29.560 --> 0:24:32.119
<v Speaker 4>that is it a competitive advantage? Does it deliver a

0:24:32.200 --> 0:24:34.119
<v Speaker 4>better service? Those types of things. So one of the

0:24:34.119 --> 0:24:36.639
<v Speaker 4>things I've seen In New Zealand. We have six and

0:24:36.680 --> 0:24:39.880
<v Speaker 4>a half thousand healthcare organizations, of which one is Health

0:24:39.920 --> 0:24:43.080
<v Speaker 4>New Zealand. It is the biggest, but that's the wider context.

0:24:43.119 --> 0:24:48.600
<v Speaker 4>So organizations need to think about AI from a responsible perspective.

0:24:48.640 --> 0:24:51.600
<v Speaker 4>And the biggest concern slash barrier is exactly what you

0:24:51.680 --> 0:24:55.680
<v Speaker 4>just articulated, which is hallucinations. I think the soft words

0:24:55.680 --> 0:24:58.960
<v Speaker 4>are unreliable outputs, and so I think a lot of

0:24:58.960 --> 0:25:02.600
<v Speaker 4>that has to be un understood. Choosing our use case,

0:25:03.560 --> 0:25:06.480
<v Speaker 4>learning about the use case, having the governance and leadership

0:25:06.680 --> 0:25:09.040
<v Speaker 4>in place to go. Actually, we've tried this in a

0:25:09.440 --> 0:25:12.560
<v Speaker 4>small use case. We have actually looked at what's happening

0:25:12.560 --> 0:25:15.199
<v Speaker 4>overseas and these use cases do actually add value, but

0:25:15.240 --> 0:25:17.600
<v Speaker 4>you'd got to go on the journey around I guess

0:25:17.600 --> 0:25:20.560
<v Speaker 4>the hallucination side of things. The other thing that's interesting

0:25:20.560 --> 0:25:23.280
<v Speaker 4>in New Zealand, in Health New Zealand as an example

0:25:23.280 --> 0:25:24.920
<v Speaker 4>of starting to do it, is they need to get

0:25:24.960 --> 0:25:28.840
<v Speaker 4>all their data in one place to run the GENAI

0:25:29.160 --> 0:25:31.560
<v Speaker 4>lms across the top of it. And so there's definitely

0:25:32.240 --> 0:25:34.520
<v Speaker 4>one that's the strategy for Health New Zealand, and they've

0:25:34.520 --> 0:25:37.679
<v Speaker 4>started investing in something called National data platform, so they

0:25:37.720 --> 0:25:39.480
<v Speaker 4>can put their data all in one place, whether it's

0:25:39.480 --> 0:25:43.360
<v Speaker 4>clinical data, workforce data, finance data, and then they can

0:25:43.400 --> 0:25:47.320
<v Speaker 4>start training the GENAI tools on top of their own

0:25:47.400 --> 0:25:50.320
<v Speaker 4>data and then managing that piece. You still have hallucinations,

0:25:50.359 --> 0:25:52.680
<v Speaker 4>don't get me wrong around data quality, but at least

0:25:52.680 --> 0:25:55.639
<v Speaker 4>you're doing it on the data that you have governance

0:25:55.920 --> 0:25:58.800
<v Speaker 4>over a couple of things to share with you. In

0:25:58.880 --> 0:26:03.600
<v Speaker 4>terms of New Zealand's text for GENAI adoption, mass of

0:26:03.680 --> 0:26:08.320
<v Speaker 4>concerns around liability. So if I have run a GENAI

0:26:08.400 --> 0:26:10.159
<v Speaker 4>tool and I've surfaced it to a patient and it's

0:26:10.200 --> 0:26:12.920
<v Speaker 4>given them some advice and something doesn't go as well

0:26:12.920 --> 0:26:14.560
<v Speaker 4>as it would have liked, and there's more risk in

0:26:14.680 --> 0:26:18.560
<v Speaker 4>terms of patient care, who's liable Is it the clinician

0:26:18.640 --> 0:26:20.880
<v Speaker 4>or is it the GENAI And how does it actually work?

0:26:20.920 --> 0:26:23.560
<v Speaker 4>So he's a little bit of maturity around what I

0:26:23.600 --> 0:26:26.520
<v Speaker 4>would call regulation and compliance to think through, because at

0:26:26.560 --> 0:26:30.560
<v Speaker 4>the end of the day, most healthyic organizations have a

0:26:30.600 --> 0:26:35.560
<v Speaker 4>clinical obligation around the safety of the care that they provide.

0:26:35.600 --> 0:26:38.879
<v Speaker 4>And if you introduce AI alongside the clinician, how does

0:26:38.920 --> 0:26:41.520
<v Speaker 4>it actually work and what are the implications? And the

0:26:41.560 --> 0:26:44.040
<v Speaker 4>last year that's really interesting is privacy and security of

0:26:44.080 --> 0:26:46.000
<v Speaker 4>the data. So you might bring it all in, but

0:26:46.040 --> 0:26:48.320
<v Speaker 4>you know, how do you actually control it in the

0:26:48.320 --> 0:26:50.760
<v Speaker 4>world of more cyber attacks, particularly in health systems around

0:26:50.760 --> 0:26:52.879
<v Speaker 4>the world. So those are kind of the core barriers.

0:26:52.880 --> 0:26:55.320
<v Speaker 4>But the number one is obviously the hallucinations piece.

0:26:55.720 --> 0:26:58.240
<v Speaker 1>Yeah, so you know, I guess part of that is

0:26:58.280 --> 0:27:01.399
<v Speaker 1>having the confidence to experiment and trial and go at it,

0:27:01.440 --> 0:27:03.960
<v Speaker 1>but also having the confidence to say, actually, in this case,

0:27:04.080 --> 0:27:07.359
<v Speaker 1>the risks are too high, the technology is not there

0:27:07.520 --> 0:27:10.359
<v Speaker 1>yet or may not be, and so we're going to

0:27:10.440 --> 0:27:13.600
<v Speaker 1>choose to not make this a customer facing or patient

0:27:13.640 --> 0:27:17.439
<v Speaker 1>facing thing. Keep it for doctors or healthcare providers and

0:27:17.440 --> 0:27:21.480
<v Speaker 1>they can be the intermediary between the chatbot that's getting

0:27:21.480 --> 0:27:24.520
<v Speaker 1>a lot of information and the patient at the other end.

0:27:24.600 --> 0:27:27.320
<v Speaker 1>Is that kind of how thinking is going, Yeah.

0:27:27.119 --> 0:27:27.359
<v Speaker 5>It is.

0:27:27.400 --> 0:27:30.520
<v Speaker 4>It's an interesting piece because if you're wanting to experience

0:27:30.560 --> 0:27:33.280
<v Speaker 4>an experiment and dip your toes and genitive AI, I

0:27:33.280 --> 0:27:35.800
<v Speaker 4>think that the two pieces of conversation we're having in

0:27:35.840 --> 0:27:38.199
<v Speaker 4>New Zealand at the moment is so, what are the

0:27:38.280 --> 0:27:42.720
<v Speaker 4>use cases at a gathering momentum offshore and generally speaking

0:27:43.200 --> 0:27:48.680
<v Speaker 4>in the healthcare context using GENAI to re platform, recode

0:27:48.720 --> 0:27:50.720
<v Speaker 4>old applications, and New Zealand has a problem at the

0:27:50.720 --> 0:27:52.960
<v Speaker 4>moment around that we've got a lot of legacy applications

0:27:53.000 --> 0:27:56.000
<v Speaker 4>being used, particularly in the public health system. The second

0:27:56.040 --> 0:27:58.800
<v Speaker 4>area is around contact center experience, and the third one

0:27:58.840 --> 0:28:00.600
<v Speaker 4>is that voice to text that I talk about. So

0:28:01.119 --> 0:28:03.439
<v Speaker 4>those are the three broad areas. The other thing that

0:28:03.440 --> 0:28:06.280
<v Speaker 4>we've packed up offshore, which is interesting to share with you,

0:28:06.400 --> 0:28:10.520
<v Speaker 4>is generally speaking, most of the GENAI use cases have

0:28:10.600 --> 0:28:14.560
<v Speaker 4>been done on top of platforms. They're leveraging Salesforce, Microsoft,

0:28:15.600 --> 0:28:19.080
<v Speaker 4>our electronic medical record platforms where genitive AI is being

0:28:19.119 --> 0:28:22.320
<v Speaker 4>built in as a feature if you like, in these platforms.

0:28:22.560 --> 0:28:26.000
<v Speaker 4>So the hallucinations piece isn't as big a risk because

0:28:26.000 --> 0:28:28.680
<v Speaker 4>it's built into a platform. It's well tested, So I

0:28:28.720 --> 0:28:31.320
<v Speaker 4>think it's a fine balance. But it's just interesting to

0:28:31.359 --> 0:28:34.280
<v Speaker 4>see what the trends are in terms of the practical

0:28:34.480 --> 0:28:37.760
<v Speaker 4>elements of gen AI, the hype versus in reality what's

0:28:37.800 --> 0:28:38.200
<v Speaker 4>going on.

0:28:38.400 --> 0:28:41.720
<v Speaker 1>Yeah, absolutely, that's kind of a lot about primary care,

0:28:41.760 --> 0:28:45.000
<v Speaker 1>and we talked about how that's really advanced. What about

0:28:45.040 --> 0:28:47.240
<v Speaker 1>in the hospital world.

0:28:47.520 --> 0:28:51.920
<v Speaker 4>Yeah, Again, it's an interesting discussion around looking what happens

0:28:51.920 --> 0:28:54.560
<v Speaker 4>off sure, and I think there's a couple of contextual things.

0:28:54.600 --> 0:28:57.680
<v Speaker 4>So as a practicing clinician, when I think about technology

0:28:57.800 --> 0:29:01.480
<v Speaker 4>like genitive AI, I think of it as another tool

0:29:01.560 --> 0:29:04.320
<v Speaker 4>in my clinical practice, like over stethoscope around my neck.

0:29:04.520 --> 0:29:07.160
<v Speaker 4>Most of the younger generation clinicians, I'm I'm a I

0:29:07.160 --> 0:29:09.160
<v Speaker 4>guess I called a veteran these days because they've been

0:29:09.200 --> 0:29:11.080
<v Speaker 4>around the health system for twenty five years. But if

0:29:11.120 --> 0:29:14.960
<v Speaker 4>I look at some of the newly trained doctors and nurses,

0:29:15.120 --> 0:29:18.640
<v Speaker 4>they all expect if you like, generative AI to be

0:29:18.720 --> 0:29:22.480
<v Speaker 4>available for some of the use cases around again that

0:29:22.600 --> 0:29:26.120
<v Speaker 4>voice detext piece, and we haven't done anything in New

0:29:26.200 --> 0:29:29.680
<v Speaker 4>Zealand at the moment, but in Australia, the first use

0:29:29.720 --> 0:29:32.240
<v Speaker 4>case is using voice to text and busy theaters to

0:29:32.320 --> 0:29:35.760
<v Speaker 4>drive through throughput so you can hit health targets around,

0:29:35.840 --> 0:29:37.720
<v Speaker 4>you know, like the waiting lists for hypophens and things

0:29:37.760 --> 0:29:42.000
<v Speaker 4>like that. So there is a willingness for in that

0:29:42.360 --> 0:29:47.640
<v Speaker 4>surgical operating theater for surgeons, anetheists, theater nurses to use

0:29:47.680 --> 0:29:50.240
<v Speaker 4>those technologies to be more efficient in terms of through

0:29:50.240 --> 0:29:53.080
<v Speaker 4>put through the theater because they're not writing notes after

0:29:53.120 --> 0:29:56.080
<v Speaker 4>an operation. It's been done real time, so yes, there

0:29:56.120 --> 0:29:58.840
<v Speaker 4>is potential, just not quite happening at the moment, and

0:29:58.880 --> 0:30:02.760
<v Speaker 4>I guess that's the clinical frontline piece. Really interesting report

0:30:03.160 --> 0:30:06.400
<v Speaker 4>that we did with Microsoft recently for New Zealand looking

0:30:06.440 --> 0:30:09.719
<v Speaker 4>at how genitive AI could be applied to the nursing

0:30:10.240 --> 0:30:14.360
<v Speaker 4>workforce and based on using some of the Microsoft technologies

0:30:14.960 --> 0:30:18.800
<v Speaker 4>voice to texts predictive analytics around patients becoming unwell because

0:30:18.800 --> 0:30:21.120
<v Speaker 4>they have temperatures gone up or their blood pressures dropped.

0:30:22.280 --> 0:30:24.760
<v Speaker 4>They said, if you put on these common tools, you'd

0:30:24.800 --> 0:30:28.000
<v Speaker 4>get a productivity increase of around nine to ten percent

0:30:28.040 --> 0:30:31.560
<v Speaker 4>for each nurse across the country. So that's proven offshore,

0:30:31.920 --> 0:30:33.800
<v Speaker 4>done a little bit of analysis around how I guess

0:30:33.880 --> 0:30:36.680
<v Speaker 4>nursing workforce works the New Zealand today and our public hospitals.

0:30:36.800 --> 0:30:41.400
<v Speaker 4>You apply these technologies and process improvement augmentation around genitive

0:30:41.440 --> 0:30:44.960
<v Speaker 4>AI would actually lift productivity ten percent, which I guess

0:30:45.480 --> 0:30:47.720
<v Speaker 4>you know eight to twelve hour shifts is you know

0:30:47.760 --> 0:30:49.960
<v Speaker 4>one to one and a half hours, which does make

0:30:49.960 --> 0:30:50.600
<v Speaker 4>a big difference.

0:30:50.760 --> 0:30:52.680
<v Speaker 1>It does, especially if you're looking at you know, a

0:30:52.720 --> 0:30:56.400
<v Speaker 1>shortage and if ten percent is saying you have the

0:30:56.440 --> 0:30:58.680
<v Speaker 1>equivalent to ten nurses that have nine nurses on shift.

0:30:58.680 --> 0:31:00.520
<v Speaker 3>That actually does make a difference end of the day.

0:31:00.640 --> 0:31:02.240
<v Speaker 4>It does, and you know, we've got to you know,

0:31:02.520 --> 0:31:04.560
<v Speaker 4>as you've heard in the media, we've got to adopt

0:31:04.600 --> 0:31:07.600
<v Speaker 4>a shortage of nursing shortage and in some areas what

0:31:07.680 --> 0:31:10.560
<v Speaker 4>I would call an allied health professional shortage BUZZIO is

0:31:10.560 --> 0:31:13.400
<v Speaker 4>occupational therapists. The biggest year actually at the moment is

0:31:13.440 --> 0:31:17.520
<v Speaker 4>anesthetic technicians. So that's actually preventing some of the through

0:31:17.560 --> 0:31:20.800
<v Speaker 4>put in private and public hospitals where there's just enough

0:31:20.800 --> 0:31:23.080
<v Speaker 4>of anesthetic technicians while you're sleep to look after you.

0:31:23.480 --> 0:31:25.000
<v Speaker 4>It's really interesting at the moment.

0:31:25.160 --> 0:31:29.000
<v Speaker 1>What about the casting forward into the future. Do you

0:31:29.080 --> 0:31:32.880
<v Speaker 1>see it, as you know, potentially being a kind of

0:31:33.360 --> 0:31:36.280
<v Speaker 1>first point of contact for patients to be able to

0:31:36.320 --> 0:31:38.840
<v Speaker 1>say instead of just pushing a button waiting for a

0:31:38.920 --> 0:31:41.400
<v Speaker 1>nurse pushing a button and saying, oh, I think my

0:31:41.480 --> 0:31:43.479
<v Speaker 1>leg's really hurting and I'm not sure, and so then

0:31:43.520 --> 0:31:46.440
<v Speaker 1>that can automatically go into like a database and be

0:31:46.520 --> 0:31:48.520
<v Speaker 1>triaged and get the nurses to who needs to. Like,

0:31:49.120 --> 0:31:51.040
<v Speaker 1>it strikes me there is a fair bit of potential

0:31:51.080 --> 0:31:55.440
<v Speaker 1>in that first line of support for generative AI in

0:31:55.560 --> 0:31:56.320
<v Speaker 1>the future.

0:31:56.800 --> 0:31:58.680
<v Speaker 4>Yeah, look, I totally agree with you. So I think

0:31:59.000 --> 0:32:03.120
<v Speaker 4>technologies like unit of AI we often talk about in

0:32:03.120 --> 0:32:04.880
<v Speaker 4>the current health system of New Zealand, we've got to

0:32:04.880 --> 0:32:07.000
<v Speaker 4>transform it generally. That means you spend in the next

0:32:07.000 --> 0:32:10.680
<v Speaker 4>five years trying to change the reality is we need

0:32:10.720 --> 0:32:14.000
<v Speaker 4>to ask New Zealanders, you know, what's your experience like

0:32:14.040 --> 0:32:16.360
<v Speaker 4>in terms of their most recent interaction with a healthcare

0:32:16.360 --> 0:32:18.920
<v Speaker 4>professional or a period of care and hospital. So my

0:32:19.400 --> 0:32:23.320
<v Speaker 4>view is first and foremost is that's where things are going.

0:32:23.320 --> 0:32:25.800
<v Speaker 4>I guess it's probably called consumerism and what do people

0:32:25.840 --> 0:32:28.320
<v Speaker 4>expect in terms of their health experience and how do

0:32:28.360 --> 0:32:31.120
<v Speaker 4>they compare it to other experiences like banking just to

0:32:31.240 --> 0:32:33.920
<v Speaker 4>use it? And I guess at parallel so I do

0:32:34.080 --> 0:32:37.520
<v Speaker 4>think one our customers, patients like you and I, will

0:32:37.600 --> 0:32:42.120
<v Speaker 4>expect a better level of experience and driven by digital

0:32:42.160 --> 0:32:44.640
<v Speaker 4>touch points, of which GENAI will power some of them.

0:32:44.640 --> 0:32:45.320
<v Speaker 3>It's number one.

0:32:45.480 --> 0:32:47.920
<v Speaker 4>The next question is do New Zealanders want it? And

0:32:47.960 --> 0:32:50.440
<v Speaker 4>the other that is yes. So about seven or eight

0:32:50.520 --> 0:32:53.400
<v Speaker 4>years ago a survey was done that I was involved

0:32:53.400 --> 0:32:56.640
<v Speaker 4>and where we went around the country asking patients what

0:32:56.720 --> 0:33:01.920
<v Speaker 4>their expectations were around digital tools to manage their disease

0:33:01.920 --> 0:33:03.800
<v Speaker 4>and illness if they had something like diabetes, or their

0:33:03.840 --> 0:33:06.000
<v Speaker 4>health and wellness where they're trying to prevent themselves from

0:33:06.000 --> 0:33:09.920
<v Speaker 4>getting diabetes, and the overwhelming response was yes, we want

0:33:09.920 --> 0:33:12.680
<v Speaker 4>a digital experience. I think the last piece to share

0:33:12.680 --> 0:33:14.920
<v Speaker 4>with you, and it's a really interesting thing to think through,

0:33:15.040 --> 0:33:19.040
<v Speaker 4>is we've got these workforce shortage challenges, which is a

0:33:19.080 --> 0:33:21.760
<v Speaker 4>big problem to solve. But one of the things in

0:33:21.840 --> 0:33:24.960
<v Speaker 4>other health systems have reimagined what healthcare could look like

0:33:25.000 --> 0:33:27.800
<v Speaker 4>in terms of a journey for patients and their families

0:33:27.880 --> 0:33:31.600
<v Speaker 4>is it's an interesting concept where there's kind of they

0:33:32.800 --> 0:33:36.080
<v Speaker 4>have the capacity if you empower the right cohorts to

0:33:36.120 --> 0:33:38.200
<v Speaker 4>manage their health and wellness to take some of the

0:33:38.200 --> 0:33:40.680
<v Speaker 4>burden off doctors and nurses if they've got the right

0:33:41.080 --> 0:33:42.880
<v Speaker 4>tools in front of them, or they can manage their

0:33:42.920 --> 0:33:45.800
<v Speaker 4>diabetes themselves. So I think the other thing that's coming

0:33:45.840 --> 0:33:50.360
<v Speaker 4>is we enable more digital journeys, patients and their families

0:33:50.400 --> 0:33:52.960
<v Speaker 4>and farnes will take more control over their health and

0:33:53.000 --> 0:33:55.840
<v Speaker 4>wellness and that'll take some of the burden off the

0:33:55.880 --> 0:33:59.680
<v Speaker 4>health system and it waits up all the workforce shortage problems.

0:34:00.000 --> 0:34:03.200
<v Speaker 4>Will help in terms of at the moment, you probably

0:34:03.680 --> 0:34:06.240
<v Speaker 4>got demand, but they can meet that with their own capacity.

0:34:06.440 --> 0:34:08.640
<v Speaker 4>It's a funny way of thinking about things, but definitely

0:34:08.680 --> 0:34:10.560
<v Speaker 4>that's the general trained off shore at the moment.

0:34:17.280 --> 0:34:21.440
<v Speaker 1>The other thing that strikes me is as not generative

0:34:21.480 --> 0:34:25.880
<v Speaker 1>AI specifically, but machine learning based tools, the kind of

0:34:25.880 --> 0:34:27.640
<v Speaker 1>classic AI as I kind of refer to it in

0:34:27.640 --> 0:34:31.800
<v Speaker 1>my head. The application of that is becoming so much

0:34:32.120 --> 0:34:36.800
<v Speaker 1>more capable and so much more broadly applied that actually

0:34:37.280 --> 0:34:41.279
<v Speaker 1>some of these widgets may not necessarily be needed where

0:34:41.280 --> 0:34:42.400
<v Speaker 1>they definitely were before.

0:34:42.400 --> 0:34:43.480
<v Speaker 3>And I'm thinking of things.

0:34:43.280 --> 0:34:45.799
<v Speaker 1>Like taku eyes take a photo of your eye to

0:34:45.800 --> 0:34:49.360
<v Speaker 1>get certain diagnoses. That strikes me as a way that

0:34:49.400 --> 0:34:53.920
<v Speaker 1>AI is helping us directly move towards some level of

0:34:53.960 --> 0:34:58.440
<v Speaker 1>equity because you don't need to have as much equipment

0:34:58.680 --> 0:35:01.640
<v Speaker 1>in order to be able to actually address or monitor

0:35:01.880 --> 0:35:02.560
<v Speaker 1>in some ways.

0:35:03.440 --> 0:35:04.359
<v Speaker 3>Are you seeing that as well?

0:35:04.440 --> 0:35:06.440
<v Speaker 4>You see the channel you know, I probably call that

0:35:06.560 --> 0:35:09.560
<v Speaker 4>democratization of health and wellness, right, It's a really interesting story.

0:35:09.560 --> 0:35:13.080
<v Speaker 4>So yeah, I've been involved with Tokui since the start

0:35:13.120 --> 0:35:16.160
<v Speaker 4>Spark Health in my previous role provide U some innovation

0:35:16.280 --> 0:35:18.719
<v Speaker 4>funding to get them using aws on the cloud to

0:35:18.760 --> 0:35:21.840
<v Speaker 4>run their algorithms around the email that you just talked about.

0:35:21.880 --> 0:35:24.160
<v Speaker 4>So and again, that's a little bit of that of

0:35:24.560 --> 0:35:29.480
<v Speaker 4>a consumer customer experience piece where you gather a photo

0:35:29.520 --> 0:35:32.120
<v Speaker 4>of the retina. How you gather it can be multiple

0:35:32.160 --> 0:35:34.759
<v Speaker 4>different ways, and then you teaching this algorithm to kind

0:35:34.760 --> 0:35:38.040
<v Speaker 4>of go, have you got diabetic retinopathy or hypertensive retinopathy?

0:35:38.280 --> 0:35:38.440
<v Speaker 2>Yes?

0:35:38.560 --> 0:35:40.400
<v Speaker 4>Or no? No, don't worry about it. Come back in

0:35:40.440 --> 0:35:43.160
<v Speaker 4>a year and get screened. Oh yes, you do set

0:35:43.200 --> 0:35:47.239
<v Speaker 4>up the referral path. So and I guess that's that's

0:35:47.360 --> 0:35:52.640
<v Speaker 4>one people like tokuais going, hey, we need to unlock, democratize,

0:35:52.680 --> 0:35:56.560
<v Speaker 4>provide better X this provide better experience. I definitely see

0:35:56.920 --> 0:36:00.080
<v Speaker 4>that coming. It's an interesting area because, as you have alluded, so,

0:36:00.080 --> 0:36:06.880
<v Speaker 4>it's diagnostics, right, and again my views medicine traditionally is

0:36:07.520 --> 0:36:09.960
<v Speaker 4>you come and see me, I take a history from you,

0:36:10.000 --> 0:36:11.399
<v Speaker 4>I run a whole lot of tests, and we work

0:36:11.440 --> 0:36:15.319
<v Speaker 4>out what your diagnosis is. With the sophistication of diagnostics

0:36:15.360 --> 0:36:18.160
<v Speaker 4>these days, you almost do the diagnostic first to help

0:36:18.200 --> 0:36:20.919
<v Speaker 4>you get to the diagnosis, because sometimes it's the gold

0:36:21.000 --> 0:36:23.720
<v Speaker 4>standard and you don't necessarily need to take the history

0:36:23.760 --> 0:36:26.560
<v Speaker 4>of how you've been feeling. Those types of things so

0:36:28.239 --> 0:36:31.120
<v Speaker 4>from my perspective, I think diagnostics are going to get better.

0:36:31.239 --> 0:36:34.160
<v Speaker 4>One of the real challenges that's happened off shore is

0:36:34.600 --> 0:36:40.520
<v Speaker 4>when patients engage in those diagnostic type tools. If it's right, great,

0:36:40.520 --> 0:36:42.880
<v Speaker 4>if it's kind of a little bit on the fence

0:36:42.920 --> 0:36:44.799
<v Speaker 4>around what your diagnosis is and how do you kind

0:36:44.800 --> 0:36:47.160
<v Speaker 4>of enter into the system to get it clarified. Because

0:36:48.280 --> 0:36:50.480
<v Speaker 4>there's the science of medicine that I talked about before,

0:36:50.520 --> 0:36:52.600
<v Speaker 4>and there's the art of medicine. And sometimes what you

0:36:52.680 --> 0:36:55.399
<v Speaker 4>find and a diagnostic is that it can be one

0:36:55.440 --> 0:36:58.200
<v Speaker 4>diagnosis or sometimes it can mean three or four other

0:36:58.440 --> 0:37:01.680
<v Speaker 4>diagnoses and you need to do further diagnostic tests to

0:37:01.760 --> 0:37:04.160
<v Speaker 4>kind of rule bring out till you get to the one.

0:37:04.200 --> 0:37:05.520
<v Speaker 4>So it's a little bit a little bit to think

0:37:05.520 --> 0:37:07.920
<v Speaker 4>through there. But I agree with you in terms of

0:37:08.400 --> 0:37:11.960
<v Speaker 4>companies like toku Wai's kind of setting the standard around

0:37:12.480 --> 0:37:16.200
<v Speaker 4>machine learning and changing the access to those types of services.

0:37:17.160 --> 0:37:20.520
<v Speaker 1>I imagine, you know, maybe ten twenty years, You've got

0:37:20.520 --> 0:37:22.200
<v Speaker 1>somebody at home and it's like our time for my

0:37:22.239 --> 0:37:24.319
<v Speaker 1>medical checkup, and they get out their smartphone and they

0:37:24.560 --> 0:37:26.879
<v Speaker 1>take some photos of various things part of their body,

0:37:26.920 --> 0:37:29.680
<v Speaker 1>and they say some words and they you know, maybe

0:37:30.360 --> 0:37:33.200
<v Speaker 1>get a smart cheap smart watch and it takes some

0:37:33.880 --> 0:37:36.520
<v Speaker 1>stuff like that, and then that can feed into an

0:37:36.560 --> 0:37:38.640
<v Speaker 1>algorithm which can be sent to a GP to go,

0:37:39.200 --> 0:37:41.440
<v Speaker 1>oh yep, it all looks okay, Like we can give

0:37:41.480 --> 0:37:45.080
<v Speaker 1>you the medical tick really, like you said, democratizing it

0:37:45.120 --> 0:37:48.120
<v Speaker 1>but also taking it out of urban centers as well

0:37:48.200 --> 0:37:50.640
<v Speaker 1>and allowing people who may not have as much access

0:37:50.680 --> 0:37:56.799
<v Speaker 1>to primary healthcare to really receive the early intervention care

0:37:56.880 --> 0:37:58.360
<v Speaker 1>that can actually make a big difference.

0:37:58.440 --> 0:38:01.160
<v Speaker 4>Yeah, yeah, look, I totally agree with you. One of

0:38:01.200 --> 0:38:02.520
<v Speaker 4>the things I was going to share with you today

0:38:02.600 --> 0:38:05.239
<v Speaker 4>is being some more and I'm involved in Martin and

0:38:05.239 --> 0:38:08.480
<v Speaker 4>PACIFICA getting into digital health tech. And the reason I'm

0:38:08.480 --> 0:38:11.799
<v Speaker 4>bringing this up is there's an element of driving kind

0:38:11.800 --> 0:38:16.360
<v Speaker 4>of a national way of working rural or urban around healthcare.

0:38:16.880 --> 0:38:20.000
<v Speaker 4>But the other thing that's really interesting is sometimes communities

0:38:20.120 --> 0:38:22.840
<v Speaker 4>need to solve for themselves. So they understand the business

0:38:22.840 --> 0:38:25.200
<v Speaker 4>problem or the health problem. They've got some tech that

0:38:25.239 --> 0:38:27.640
<v Speaker 4>they could use, but they actually come together as a

0:38:27.680 --> 0:38:31.279
<v Speaker 4>community around how they solve some of these problems. So

0:38:31.320 --> 0:38:33.560
<v Speaker 4>I think what we'll see in the future in terms

0:38:33.600 --> 0:38:35.640
<v Speaker 4>of what I'm trying to cover here is you'll have

0:38:36.520 --> 0:38:39.040
<v Speaker 4>national ways of working around these health checks that might

0:38:39.120 --> 0:38:41.600
<v Speaker 4>work for eighty percent of New Zealanders, but it doesn't

0:38:41.640 --> 0:38:45.920
<v Speaker 4>work potentially behaviorally for twenty percent. And I'll follow this

0:38:46.000 --> 0:38:49.800
<v Speaker 4>through So in the PACIFICA communities, it's really really hard

0:38:49.840 --> 0:38:53.839
<v Speaker 4>to get Pacifica to engage with the health system, and

0:38:53.880 --> 0:38:56.560
<v Speaker 4>so how do you engage with them? And if you're

0:38:56.560 --> 0:39:00.879
<v Speaker 4>offering this tooling, how digitally enabled are my mum, she's

0:39:00.840 --> 0:39:04.360
<v Speaker 4>saw them on Chinese she's seventy five, not particularly digitally enabled.

0:39:04.360 --> 0:39:06.359
<v Speaker 4>But if you had the tool to do a health

0:39:06.440 --> 0:39:09.920
<v Speaker 4>check remotely, who would go and do that with her?

0:39:09.960 --> 0:39:11.439
<v Speaker 4>And it's probably been me or one of my three

0:39:11.440 --> 0:39:15.400
<v Speaker 4>younger brothers actually facilitate the processes. They've been independent enough

0:39:15.440 --> 0:39:17.440
<v Speaker 4>to do it. So there's a few but the technology

0:39:17.480 --> 0:39:19.719
<v Speaker 4>is enabled, but how do you practically get people to

0:39:19.800 --> 0:39:23.760
<v Speaker 4>use the tech to do the remote health and wellness

0:39:23.840 --> 0:39:26.319
<v Speaker 4>checks on an annual basis with the GP So there's

0:39:26.320 --> 0:39:27.560
<v Speaker 4>some of that. And the reason I'm showing that with

0:39:27.600 --> 0:39:31.200
<v Speaker 4>you is is once you understand the opportunity the technology,

0:39:31.239 --> 0:39:35.319
<v Speaker 4>it's really interesting to see how communities solve for how

0:39:35.360 --> 0:39:37.680
<v Speaker 4>they practically get the adoption of the tech to improve

0:39:37.719 --> 0:39:41.480
<v Speaker 4>health and wellness outcomes, and I think that's awesome. And

0:39:41.520 --> 0:39:46.319
<v Speaker 4>the other side of that is I think in my

0:39:46.400 --> 0:39:49.799
<v Speaker 4>experience at Accenture that the work we're doing in our

0:39:49.840 --> 0:39:52.640
<v Speaker 4>words around mary Indigenous people of New Zealand, some of

0:39:52.640 --> 0:39:54.319
<v Speaker 4>the problems you're trying to solve in New Zealand at

0:39:54.320 --> 0:39:57.200
<v Speaker 4>the moment, we're ahead of other parts of the world.

0:39:57.280 --> 0:40:00.719
<v Speaker 4>So I think about the Native Indian in America, I

0:40:00.719 --> 0:40:03.920
<v Speaker 4>think about Aboriginal and Tory Straight Islanders in Australia. Some

0:40:04.000 --> 0:40:06.880
<v Speaker 4>of the things we're doing in New Zealand already around

0:40:07.280 --> 0:40:10.920
<v Speaker 4>machine learning junior of AI sometimes through that EWI actually

0:40:11.520 --> 0:40:13.480
<v Speaker 4>is ahead of everybody else. So there's a little bit

0:40:13.480 --> 0:40:15.440
<v Speaker 4>around what we do in New Zealand. If we get

0:40:15.440 --> 0:40:17.359
<v Speaker 4>it right and get the adoption, we can kind of

0:40:17.560 --> 0:40:20.719
<v Speaker 4>show the world how you can improve equity for Formardi

0:40:20.760 --> 0:40:22.760
<v Speaker 4>pacifica as some examples.

0:40:23.560 --> 0:40:25.920
<v Speaker 1>Can you share a kind of an example of what

0:40:25.960 --> 0:40:27.960
<v Speaker 1>you mean by that? What are some of the interventions? Y?

0:40:28.080 --> 0:40:29.719
<v Speaker 4>Yeah, so no, it's really interesting to share with you.

0:40:29.800 --> 0:40:34.000
<v Speaker 4>So probably two places to start. One of the things

0:40:34.000 --> 0:40:36.000
<v Speaker 4>that I think, you know, what does the future of

0:40:36.000 --> 0:40:37.960
<v Speaker 4>health and wellness look like in New Zealand and it's

0:40:38.000 --> 0:40:40.080
<v Speaker 4>a really interesting to think thing to share with you

0:40:40.200 --> 0:40:44.600
<v Speaker 4>that there's this this concept called social determinants of health.

0:40:44.760 --> 0:40:48.000
<v Speaker 4>And effectively, if you digitize you every experience in the

0:40:48.000 --> 0:40:50.239
<v Speaker 4>health system, from a GP through to physio through to

0:40:50.280 --> 0:40:53.319
<v Speaker 4>beteen a hospital doctor, you'd only get one fifth of

0:40:53.320 --> 0:40:56.880
<v Speaker 4>the data that determines yours and my health and wellness outcomes.

0:40:57.160 --> 0:40:59.680
<v Speaker 4>Then the number one data items your post code and

0:40:59.680 --> 0:41:02.080
<v Speaker 4>where you live. But there's a whole lot of behavioral

0:41:02.120 --> 0:41:04.760
<v Speaker 4>stuff around do you exercise, do you smoke, do you drink?

0:41:05.320 --> 0:41:07.040
<v Speaker 4>What are your family what's your family history. I've got

0:41:07.040 --> 0:41:08.880
<v Speaker 4>a strong family history of a schemic heart disease in

0:41:08.920 --> 0:41:11.920
<v Speaker 4>my family. Where do you live, how educated are you?

0:41:12.040 --> 0:41:14.800
<v Speaker 4>As your house warm or cold? Those types of things.

0:41:14.840 --> 0:41:17.320
<v Speaker 4>So one of the opportunities in New Zealand is to

0:41:17.360 --> 0:41:21.399
<v Speaker 4>have that holistic approach to social atterminans of health. It's

0:41:21.400 --> 0:41:26.600
<v Speaker 4>called now in Martyrdom, there's another concept called tafade Tapafa

0:41:26.880 --> 0:41:30.600
<v Speaker 4>and that looks at your emotional health and wellness, your

0:41:30.600 --> 0:41:33.680
<v Speaker 4>physical you're spiritual and your mental health and wellness and

0:41:33.719 --> 0:41:35.239
<v Speaker 4>so some of the things that you're doing around how

0:41:35.239 --> 0:41:38.319
<v Speaker 4>they're applying the tech to that whole person. In the

0:41:38.400 --> 0:41:41.799
<v Speaker 4>community is actually having better health and wellness outcomes and

0:41:41.840 --> 0:41:44.400
<v Speaker 4>just doing a health system response. So coming back to

0:41:44.440 --> 0:41:47.400
<v Speaker 4>some of the use cases that happening at the moment,

0:41:48.480 --> 0:41:51.200
<v Speaker 4>So there's a lot of the communities in a number

0:41:51.200 --> 0:41:53.840
<v Speaker 4>of EEHE where they're collecting data around all those things

0:41:54.120 --> 0:41:57.120
<v Speaker 4>mental health, I guess, general physical health and well being,

0:41:57.239 --> 0:41:59.880
<v Speaker 4>emotional and spiritual health and wellbeing, and is starting to

0:42:00.160 --> 0:42:04.279
<v Speaker 4>algorithms across the top of that and so and that

0:42:04.400 --> 0:42:07.440
<v Speaker 4>helps them do care plans as an example for that

0:42:07.520 --> 0:42:09.760
<v Speaker 4>are not just about you know, take your high blood

0:42:09.760 --> 0:42:13.120
<v Speaker 4>pressure medication, will walk for thirty minutes today, it's around

0:42:13.160 --> 0:42:16.960
<v Speaker 4>thinking about the holistic person. So, and they're using genai

0:42:16.960 --> 0:42:20.399
<v Speaker 4>to link that outcomes and drive insights. And they're also

0:42:20.480 --> 0:42:22.960
<v Speaker 4>using a bit of email around predictive analytics. And it

0:42:23.400 --> 0:42:26.640
<v Speaker 4>comes back to innovation in New Zealand thinking about health

0:42:26.680 --> 0:42:30.239
<v Speaker 4>and wellness differently, and then those models of delivering health

0:42:30.280 --> 0:42:34.560
<v Speaker 4>and wellness I think where things are going to go globally.

0:42:35.360 --> 0:42:38.920
<v Speaker 1>That's really interesting because it's moving away from this concept

0:42:39.000 --> 0:42:45.120
<v Speaker 1>that population aggregated data is the best way of assessing

0:42:45.120 --> 0:42:47.720
<v Speaker 1>a population's health and saying well, if you can actually

0:42:47.760 --> 0:42:52.799
<v Speaker 1>take data from specific populations and you can really dynamically

0:42:52.840 --> 0:42:57.759
<v Speaker 1>split it out by post code, by you know, ethnic background,

0:42:57.840 --> 0:43:02.520
<v Speaker 1>and run analysis really quickly and easily over different areas.

0:43:02.920 --> 0:43:07.840
<v Speaker 1>And that is directly a result of modern digital data tools.

0:43:07.920 --> 0:43:08.080
<v Speaker 2>Right.

0:43:08.200 --> 0:43:10.640
<v Speaker 1>It wouldn't have been possible even twenty years ago because

0:43:10.680 --> 0:43:14.759
<v Speaker 1>everything was so slow and difficult to actually to do.

0:43:15.360 --> 0:43:18.640
<v Speaker 1>But the result of being able to do it dynamically

0:43:18.680 --> 0:43:22.480
<v Speaker 1>and quickly means that you can really look at areas

0:43:22.480 --> 0:43:26.120
<v Speaker 1>where it is most needed and make targeted interventions that

0:43:26.200 --> 0:43:30.280
<v Speaker 1>are taking consideration more than just you know, high blood pressure.

0:43:30.239 --> 0:43:32.799
<v Speaker 4>Here's your medicine, Yeah, exactly, And so you know, to

0:43:32.800 --> 0:43:36.440
<v Speaker 4>share with you, we've started a conversation with the Ministry

0:43:36.440 --> 0:43:40.479
<v Speaker 4>of Social Development and with and also with bai Kaha,

0:43:40.560 --> 0:43:44.120
<v Speaker 4>the ministry of disabled people that got broken off from

0:43:44.160 --> 0:43:46.120
<v Speaker 4>the health system as part of the reforms, and with

0:43:46.239 --> 0:43:50.879
<v Speaker 4>ACC and the context for that conversation is in communities

0:43:50.920 --> 0:43:55.040
<v Speaker 4>where health and wellness is a real challenge, how will

0:43:55.120 --> 0:44:00.239
<v Speaker 4>you bring insights health social disability in some case is

0:44:00.320 --> 0:44:05.440
<v Speaker 4>injury prevention perspective, focusing on an individual or the healthhold

0:44:05.440 --> 0:44:08.040
<v Speaker 4>they live in or the community they live in. And

0:44:08.120 --> 0:44:11.799
<v Speaker 4>so there's an enabling policy. I'll call it from the

0:44:11.840 --> 0:44:16.120
<v Speaker 4>DIA at the moment in Wellington around sharing identity across

0:44:16.120 --> 0:44:19.360
<v Speaker 4>those government agencies. So NHI in the hospital context or

0:44:19.360 --> 0:44:22.839
<v Speaker 4>health context and acc have a unique number for us

0:44:22.880 --> 0:44:24.920
<v Speaker 4>as well, so you know, so they can link my

0:44:25.040 --> 0:44:28.760
<v Speaker 4>record from the health system to I guess a shoulder

0:44:28.800 --> 0:44:30.560
<v Speaker 4>dislocation I had a few years ago and I was

0:44:30.560 --> 0:44:35.680
<v Speaker 4>playing rugby from an accident perspective. So there's enabling policy

0:44:35.680 --> 0:44:37.480
<v Speaker 4>to share identity so you can have a single view

0:44:37.520 --> 0:44:39.880
<v Speaker 4>of somebody. Then the second bit of that is what

0:44:40.000 --> 0:44:42.319
<v Speaker 4>data can be shared, and the third part of that

0:44:42.520 --> 0:44:44.719
<v Speaker 4>is what are some of the GENAI use cases you

0:44:44.719 --> 0:44:48.160
<v Speaker 4>can run across the top of health data, social services data,

0:44:48.320 --> 0:44:51.560
<v Speaker 4>disability data, and so there's real buy in across these

0:44:51.560 --> 0:44:54.880
<v Speaker 4>four agencies to kind of look at what the art

0:44:54.920 --> 0:44:58.239
<v Speaker 4>of the possible is in bringing together these agencies and

0:44:58.680 --> 0:45:02.200
<v Speaker 4>sharing data and a entity. Then once you've got that data,

0:45:02.280 --> 0:45:04.759
<v Speaker 4>how can you improve the experience for these some of

0:45:04.760 --> 0:45:08.040
<v Speaker 4>these communities that are affected in those contexts. So early

0:45:08.080 --> 0:45:11.160
<v Speaker 4>doors at the moment for us, but something that we

0:45:11.160 --> 0:45:13.680
<v Speaker 4>do offshore a lot, which is, hey, how do you

0:45:13.680 --> 0:45:16.760
<v Speaker 4>get these government agencies to think about the total picture

0:45:16.760 --> 0:45:19.399
<v Speaker 4>of social permanence of health and how do you bring

0:45:19.440 --> 0:45:22.279
<v Speaker 4>them together to offer new experiences and what opportunities does

0:45:22.320 --> 0:45:26.000
<v Speaker 4>genetve Ai bring. So it's a fascinating project that we've

0:45:26.000 --> 0:45:28.560
<v Speaker 4>started about three months ago, and then we're about to

0:45:28.560 --> 0:45:30.239
<v Speaker 4>get those agencies to look at the ard of the

0:45:30.239 --> 0:45:32.280
<v Speaker 4>possible of some of the things we're doing off sure again,

0:45:32.360 --> 0:45:35.640
<v Speaker 4>coming back to part of the conversations day, the Genai

0:45:35.800 --> 0:45:41.560
<v Speaker 4>conversation opens up these conversations around thinking about reimagining health

0:45:41.600 --> 0:45:42.120
<v Speaker 4>and wellness.

0:45:42.280 --> 0:45:44.520
<v Speaker 1>Is there anything that you would like to add, anything

0:45:44.640 --> 0:45:46.160
<v Speaker 1>that important you think we haven't covered.

0:45:46.320 --> 0:45:50.040
<v Speaker 4>So something I've been doing for some time is wearing

0:45:50.120 --> 0:45:53.520
<v Speaker 4>auring and it's really interesting. It started off as just

0:45:54.160 --> 0:45:56.920
<v Speaker 4>I Guess a tool to collect health and fitness data,

0:45:57.800 --> 0:46:00.880
<v Speaker 4>and with Genai coming along and now takes that data,

0:46:01.040 --> 0:46:08.280
<v Speaker 4>runs algorithms and goes measures mental resilience and it measures

0:46:09.120 --> 0:46:11.160
<v Speaker 4>whether I should not go and exercise today because I'm

0:46:11.200 --> 0:46:13.279
<v Speaker 4>not ready to do that. So I use that every

0:46:13.360 --> 0:46:16.439
<v Speaker 4>day and it also has a I Guess a health

0:46:16.480 --> 0:46:19.560
<v Speaker 4>coach based on the data. So just to share with

0:46:19.600 --> 0:46:22.560
<v Speaker 4>you right now, my level of daytime stress based on

0:46:22.560 --> 0:46:24.920
<v Speaker 4>the ring and the algorithms to me and what I've

0:46:24.920 --> 0:46:26.759
<v Speaker 4>been like for the last five years, I'm relaxed, So

0:46:26.800 --> 0:46:28.640
<v Speaker 4>that Ben, you must be running a good into you today.

0:46:29.600 --> 0:46:30.400
<v Speaker 3>Yeah.

0:46:30.560 --> 0:46:33.080
<v Speaker 4>Then it measures my heart health and tells me how

0:46:33.120 --> 0:46:37.640
<v Speaker 4>old my heart is relative to my age. So according

0:46:37.760 --> 0:46:40.239
<v Speaker 4>according to the app, I'm fifty one, and according to

0:46:40.280 --> 0:46:42.879
<v Speaker 4>the app, my heart's forty seven. And so I guess

0:46:42.880 --> 0:46:44.880
<v Speaker 4>what I'm sharing with you is coming back to some

0:46:44.920 --> 0:46:48.279
<v Speaker 4>of these experiences over time, and that consumerism trend I

0:46:48.320 --> 0:46:52.440
<v Speaker 4>see generally speaking Kiwi's you know, we'll get some of

0:46:52.480 --> 0:46:56.719
<v Speaker 4>these experiences where they manage their health and wellness leveraging

0:46:57.480 --> 0:47:01.359
<v Speaker 4>machine learning, generative AI, and then and then when they

0:47:01.400 --> 0:47:03.520
<v Speaker 4>think they need the intervention of the health system or

0:47:03.560 --> 0:47:05.800
<v Speaker 4>to navigate the health system, then they'll touch the health system.

0:47:05.840 --> 0:47:08.000
<v Speaker 4>So right now I wouldn't get where I'm coming from.

0:47:08.120 --> 0:47:09.840
<v Speaker 4>But if my heart health went the other way, or

0:47:09.880 --> 0:47:12.400
<v Speaker 4>my pulse is getting higher, or I was getting my

0:47:12.440 --> 0:47:15.200
<v Speaker 4>mental resilience wasn't so good, I'd consider potentially, you know,

0:47:15.239 --> 0:47:16.399
<v Speaker 4>going and seeing my family GP.

0:47:16.600 --> 0:47:19.320
<v Speaker 1>And I guess even if you couldn't put an awring

0:47:19.400 --> 0:47:21.719
<v Speaker 1>on every finger in New Zealand, you could have them

0:47:21.760 --> 0:47:24.920
<v Speaker 1>in digital check up spots and churches and might I

0:47:25.320 --> 0:47:28.080
<v Speaker 1>and you know, a GP clinic where you can just

0:47:28.080 --> 0:47:31.400
<v Speaker 1>pop in and get that done really easily and dynamically

0:47:31.440 --> 0:47:33.920
<v Speaker 1>and without and linking that back to your health data

0:47:34.000 --> 0:47:36.560
<v Speaker 1>so that it can directly feed and maybe flag something

0:47:36.600 --> 0:47:38.120
<v Speaker 1>at your GP if there's something wrong.

0:47:38.200 --> 0:47:40.400
<v Speaker 4>Yeah, and look being a really important thing you just

0:47:40.440 --> 0:47:42.160
<v Speaker 4>packed up upon that I should have talked about before,

0:47:42.200 --> 0:47:44.680
<v Speaker 4>which is the data in the future is going to

0:47:44.719 --> 0:47:46.080
<v Speaker 4>be a mixture of what I put in as a

0:47:46.080 --> 0:47:49.320
<v Speaker 4>provider about you, but it also should include the information

0:47:49.400 --> 0:47:51.880
<v Speaker 4>you put in about you. I see that trend coming,

0:47:51.880 --> 0:47:54.279
<v Speaker 4>but pragmatically, do people want it? And I think the

0:47:54.320 --> 0:47:57.840
<v Speaker 4>answers over time yes, if they start learning about some

0:47:57.920 --> 0:48:00.239
<v Speaker 4>of the capabilities, but they need to be empowered to

0:48:00.280 --> 0:48:01.040
<v Speaker 4>manage the health.

0:48:05.120 --> 0:48:07.400
<v Speaker 1>I have to say I thoroughly enjoyed that interview. I

0:48:07.440 --> 0:48:11.720
<v Speaker 1>thought Will was really great in the content and the

0:48:11.760 --> 0:48:15.200
<v Speaker 1>specificity that he went into around the use of AI

0:48:15.239 --> 0:48:18.920
<v Speaker 1>and healthcare, right from generative AI into kind of the

0:48:18.960 --> 0:48:21.880
<v Speaker 1>Toku Eyes style machine learning and everywhere in between.

0:48:21.920 --> 0:48:23.120
<v Speaker 3>I thought it was really interesting.

0:48:23.520 --> 0:48:27.680
<v Speaker 2>He was great, you know, and obviously Accentua is heavily

0:48:27.719 --> 0:48:32.239
<v Speaker 2>involved in what's going on in the health sector. They're

0:48:32.239 --> 0:48:36.680
<v Speaker 2>helping out the government and probably earning a lot of

0:48:37.040 --> 0:48:39.960
<v Speaker 2>good consulting fees. But it was a real sort of,

0:48:40.280 --> 0:48:45.239
<v Speaker 2>I think, frank and upfront sort of assessment of where

0:48:45.239 --> 0:48:47.960
<v Speaker 2>we're at, and a real takeaway for me is what

0:48:48.200 --> 0:48:51.920
<v Speaker 2>we're coming from behind. Not on the primary health I

0:48:51.960 --> 0:48:54.480
<v Speaker 2>was surprised, you know, Will suggested that we're maybe the

0:48:54.520 --> 0:48:57.239
<v Speaker 2>third best in the world when it comes to GP

0:48:57.400 --> 0:49:02.000
<v Speaker 2>clinics and that offering digital health service. So my experience

0:49:02.000 --> 0:49:06.040
<v Speaker 2>off it hasn't been particularly great with patient portals and

0:49:06.520 --> 0:49:10.560
<v Speaker 2>literally watching doctors punching stuff into their computer while I'm

0:49:10.640 --> 0:49:14.920
<v Speaker 2>paying for them to do that. But I think we

0:49:14.960 --> 0:49:19.279
<v Speaker 2>all know that in hospitals, our systems have not been

0:49:19.360 --> 0:49:24.680
<v Speaker 2>great at evolving to meet the modern needs of the population,

0:49:25.239 --> 0:49:29.920
<v Speaker 2>and he's definitely put his finger on that. So I

0:49:30.000 --> 0:49:33.160
<v Speaker 2>was surprised and pleasantly surprised to hear a lot of

0:49:33.160 --> 0:49:37.759
<v Speaker 2>the experimentation and innovation that's already going on with population

0:49:37.880 --> 0:49:41.520
<v Speaker 2>health data and that and applying machine learning to it.

0:49:41.680 --> 0:49:44.400
<v Speaker 2>So that's great. There's some really cool pilots and stuff

0:49:44.440 --> 0:49:48.600
<v Speaker 2>going on with medical devices and people's homes, and then

0:49:48.719 --> 0:49:52.680
<v Speaker 2>all of that data eventually could be used in conjunction

0:49:52.760 --> 0:49:56.239
<v Speaker 2>with AI for predictive health. So there's a lot of

0:49:56.360 --> 0:50:00.279
<v Speaker 2>good stuff going on, but some big barriers. There's well,

0:50:00.680 --> 0:50:05.680
<v Speaker 2>we're way behind, We're struggling with a funding crisis in

0:50:05.840 --> 0:50:09.960
<v Speaker 2>health so and the data is not yet in a

0:50:10.000 --> 0:50:13.120
<v Speaker 2>state where frankly it's going to be reliable enough to

0:50:13.160 --> 0:50:15.200
<v Speaker 2>feed into AI system. So a heck of a lot

0:50:15.200 --> 0:50:15.759
<v Speaker 2>of work to do.

0:50:16.080 --> 0:50:21.879
<v Speaker 1>Yeah, project here wants an embattled, difficult project is now dead.

0:50:22.000 --> 0:50:28.279
<v Speaker 1>Project another failed attempt to conglomerate this healthcare data into

0:50:28.400 --> 0:50:32.239
<v Speaker 1>something national and consistent. But we have to get there

0:50:32.280 --> 0:50:34.319
<v Speaker 1>at some point. I have to believe that at some

0:50:34.360 --> 0:50:35.960
<v Speaker 1>point we're going to need to figure it out. And

0:50:36.280 --> 0:50:38.600
<v Speaker 1>maybe trying to create something from the ground up is

0:50:38.680 --> 0:50:41.040
<v Speaker 1>just hubris. Maybe we need to figure out a better

0:50:41.480 --> 0:50:45.400
<v Speaker 1>kind of glue wear approach or something. But whatever it is,

0:50:46.440 --> 0:50:49.920
<v Speaker 1>we need to unlock this data and start enabling it

0:50:50.040 --> 0:50:54.120
<v Speaker 1>the sharing between ACC and hospitals and GPS and like

0:50:54.719 --> 0:50:57.560
<v Speaker 1>will said in the interview, figuring out how people can

0:50:57.600 --> 0:51:00.799
<v Speaker 1>actually input their own data through their wearable where that's

0:51:00.880 --> 0:51:04.880
<v Speaker 1>Aora ring and Apple watch or a glucose monitoring system

0:51:05.000 --> 0:51:09.240
<v Speaker 1>or whatever else, it will really take us a step

0:51:09.560 --> 0:51:12.239
<v Speaker 1>forward and ahead of a lot of other countries if

0:51:12.239 --> 0:51:13.520
<v Speaker 1>we can manage to get there.

0:51:13.960 --> 0:51:16.359
<v Speaker 2>Yeah, one of the barriers he mentioned there, which I

0:51:16.400 --> 0:51:21.080
<v Speaker 2>was a little bit surprised about, is massive concerns about liability.

0:51:22.160 --> 0:51:25.840
<v Speaker 2>So we might need some regulation and compliance tweaks to

0:51:26.360 --> 0:51:30.839
<v Speaker 2>give health providers the confidence to use AI and not

0:51:30.880 --> 0:51:32.800
<v Speaker 2>be worried about getting sued. I thought that would have

0:51:32.840 --> 0:51:36.000
<v Speaker 2>been a bigger deal in a place like the US.

0:51:36.040 --> 0:51:39.239
<v Speaker 2>And actually last week was a visiting expert in AI

0:51:39.320 --> 0:51:41.879
<v Speaker 2>came through New Zealand and she told me exactly that

0:51:41.960 --> 0:51:45.400
<v Speaker 2>she's working with doctors and radiographers and that using AI

0:51:45.480 --> 0:51:50.799
<v Speaker 2>and machine learning to try and improve testing and analyzing

0:51:51.000 --> 0:51:56.040
<v Speaker 2>test results. And she said the senior doctors in particular

0:51:56.080 --> 0:51:59.960
<v Speaker 2>are really pushing back because they're worried about getting sued

0:52:00.080 --> 0:52:04.239
<v Speaker 2>and getting their clinic or their hospital suit as well

0:52:04.520 --> 0:52:08.400
<v Speaker 2>by making a wrong diagnosis. So that's clearly an issue

0:52:08.520 --> 0:52:12.600
<v Speaker 2>here as well. So having that confidence to be able

0:52:12.640 --> 0:52:16.160
<v Speaker 2>to put some trust in these systems is going to

0:52:16.200 --> 0:52:21.360
<v Speaker 2>be key, and maybe we don't have quite the regulatory

0:52:21.480 --> 0:52:22.520
<v Speaker 2>environment to allow that.

0:52:22.840 --> 0:52:25.000
<v Speaker 1>Yeah, just putting those policies in place so that you

0:52:25.000 --> 0:52:26.400
<v Speaker 1>know it's not going to be ramp and AI and

0:52:26.480 --> 0:52:28.880
<v Speaker 1>making misdiagnoses all over the place. They are going to

0:52:28.880 --> 0:52:34.120
<v Speaker 1>be in conjunction with good diagnosticians as well, so they're

0:52:34.120 --> 0:52:37.239
<v Speaker 1>being checked and human in the chair and all that

0:52:37.280 --> 0:52:37.840
<v Speaker 1>good stuff.

0:52:37.960 --> 0:52:40.680
<v Speaker 2>Yeah. And the other interesting thing that will point it

0:52:40.680 --> 0:52:44.319
<v Speaker 2>out when he goes out and talks to hospitals and

0:52:44.360 --> 0:52:47.560
<v Speaker 2>clinicians and that sort of thing, a lot of them say, no,

0:52:47.640 --> 0:52:49.920
<v Speaker 2>we don't actually have a policy or even a strategy

0:52:50.280 --> 0:52:54.160
<v Speaker 2>around using AI. And this is I think we see

0:52:54.200 --> 0:52:58.080
<v Speaker 2>just about in any area of industry and business at

0:52:58.120 --> 0:53:01.440
<v Speaker 2>the moment, people immediately jumped to the use case. So

0:53:01.440 --> 0:53:02.960
<v Speaker 2>I haven't got a strategy for it, but I know

0:53:03.520 --> 0:53:06.279
<v Speaker 2>my customers want this. And when it comes to healthcare,

0:53:06.880 --> 0:53:12.799
<v Speaker 2>it's using generative AI to surface experiences for patients. So

0:53:13.080 --> 0:53:18.040
<v Speaker 2>giving them a chatbot that they can query about sensitive

0:53:18.719 --> 0:53:22.719
<v Speaker 2>health related issues and they'll get reliable information. It's that

0:53:22.840 --> 0:53:25.400
<v Speaker 2>sort of stuff that actually the health sector thinks is

0:53:25.560 --> 0:53:27.720
<v Speaker 2>the frontline of the generative AI revolution.

0:53:27.960 --> 0:53:28.160
<v Speaker 4>Yep.

0:53:28.640 --> 0:53:31.040
<v Speaker 3>It's a big revolution and it's coming. We've just got

0:53:31.040 --> 0:53:32.080
<v Speaker 3>to make sure we're ready for it.

0:53:32.719 --> 0:53:36.480
<v Speaker 2>Absolutely. So thanks to doctor Will Reedy for coming on

0:53:36.520 --> 0:53:39.560
<v Speaker 2>the Business of Tech, and we'll have more episodes coming

0:53:39.600 --> 0:53:43.320
<v Speaker 2>in this ongoing series about how AI is impacting various

0:53:43.360 --> 0:53:46.000
<v Speaker 2>sectors and industries across a tea.

0:53:46.320 --> 0:53:49.000
<v Speaker 1>Show notes in the Tech section of the Business Desk

0:53:49.120 --> 0:53:53.799
<v Speaker 1>website and the podcast is available on iHeartRadio, well your

0:53:53.800 --> 0:53:55.600
<v Speaker 1>favorite podcast platform.

0:53:55.680 --> 0:53:57.319
<v Speaker 2>Let us know what you think of the show and

0:53:57.400 --> 0:54:00.600
<v Speaker 2>drop us a line with suggestions for future gare email

0:54:00.760 --> 0:54:04.239
<v Speaker 2>Ben on benat Businessdesk dot co, dot MZ, or you

0:54:04.280 --> 0:54:06.400
<v Speaker 2>can find both of us on LinkedIn and x.

0:54:06.880 --> 0:54:10.120
<v Speaker 1>Next Thursday, we'll be talking mergers and acquisitions and the

0:54:10.200 --> 0:54:14.040
<v Speaker 1>complex technical decisions relating to digital infrastructure that need to

0:54:14.040 --> 0:54:16.040
<v Speaker 1>be made when businesses come together.

0:54:16.480 --> 0:54:18.200
<v Speaker 2>Until then, have a great week.