“One of the key things we need to think about with the Internet of Things is the proliferation, or volumes of data, that we’re going to be experiencing. One of the things we learnt in developing national electronic health records, and thinking about interpolity between clinical systems, was if we start with this concept that we can provide more information – better information – to a clinician to help them do their job, and hopefully a set of that information is relevant to a consumer as well for them to be more engaged in their healthcare, that we were concerned about a proliferation of that data. And this was still very small sets of information about discharge summaries and referrals and the like. So when we look at the Internet of Things, what we’re talking about is devices; small objects that have a very specific purpose. And those small objects are literally going to generates huge amounts of very simple data that needs to be processed.
So one of the concerns that I guess I have is how are we going to deal with that volume of information? And the answer to that is the concept of analytics, and I guess the ideas from Big Data and visualisation of data as well, so we’ve got to have that analytics idea in there. But then the ultimate problem I think we need to look at is this idea of how do we hand power to those devices? Because the endgame of these devices is for them to start making decisions on our behalf. So where you apply an algorithm, that says based on input, do something, these devices are supposed to get to a point where they are autonomous and effectively working together. And this is where I come back to that idea, of we’re giving over power if you like to these devices, so how do we make sure more human-centred outcomes and human-centred design still remains at the core of what these devices do for us?
We talk about algorithms that are going to process that information and we write algorithms today in software, like I said a simple input, simple output. This idea of artificial intelligence, or deep learning algorithms if you like, is where we are allowing the system to learn how to learn and to learn how to respond and once again this is a very important part but because we’ve got so much information and the sort of connections that we can make between the data that make it useful is by and large going to be discovered by the system itself. In some respects it goes beyond the realm of the human mind to imagine and I think there are some very deep ethical questions that need to be asked. We get very interested in the ‘what’ of genomics, the ‘what’ of Internet of Things, the ‘what’ of the innovation; but it’s important that in business we continue to take a systems based view and systems based view makes us look at things like why we’re doing it, and are we getting what we thought we were going to get out of it, and what are the sets of questions we need to be asking today when we imagine that future and then how do we go about doing that. Its important that we are understanding the learning’s that we get as we go along so that we can reapply them. So not just the ‘what’, but why did we do it, where are we going and how do we get there and what do we need to keep doing.
It’s absolutely imperative that the CIO’s, but as part of the whole business, are considering these sorts of ways of technologies as they impact society. In many respects it’s sometimes hard to imagine healthcare, and Internet of Things if you like, but when you think about it there are lots and lots of devices that have a very specific purpose so rather than saying “well healthcare is late to this market” I should think healthcare is ahead of the curve; we just haven’t realized it. And so in this instance, you’ve got devices that have a very specific purpose to do an MRI or an image or to analyse blood or whatever it might be, and it’s that theory of how that device does its role within the clinical practice and then how the data that’s generated from that device makes its way into a clinical system that show up in front of the human beings, doctors when you’re interacting with systems. So, a lot of information system that puts lab results into a clinical information system that hold patient’s data and it’s that overlap of data. So I should think there’s a lot of lessons from healthcare about how devices that have a specific purposes generate information can actually have that information overlapped with other data-sets and then decision supports and those sorts of ideas apply to it such that we get value out of all that data and actually do something meaningful with it.
When I say that I think in many respects healthcare is ahead because we have the idea of devices that have very specific purposes and that’s one of the concepts of an Internet of Things is that a light bulb has a very limited set of things that it can do. But it has a set of data that’s relevant when you can overlap it with, say, sunset and sunrise times and have the light come automatically on. The trick here in most of these things especially in the consumer world is that you’re not really selling the idea of the Internet of Things; you’re selling a service or you’re making a service available. I think a lot of the lessons of how markets are approaching consumers that is actually relevant to the way government and business can look at its internal customers, the clinicians in this case and healthcare workers, so I don’t think it’s a matter of marketing concepts like the Internet of Things; I think it’s about providing really good solutions. As I said the idea that there are lessons from health I think is relevant because it’s not an Internet of Things if you like in hospitals, but it’s certainly a network of specifically connected devices and a lot of the informatics and effort have gone on to say “well how does those sets of information overlap and actually give me something meaningful to the clinician or user and can work with.”
So in terms of time-frames it’s really a mind-set shift and this is I think this is the important role I think you mentioned before about where the CIO’s sit in this. It’s an important role in IT, but in business to not get caught up in the hype and when new buzzwords come along whether it’s big data or these sorts of concepts come along, it’s really important to understand what they’re about and then reflect on what your own business does today. And look at the success that you have in the organization doing that. Disruptive innovation has its place in the world but the concept of incremental innovation is important as well because they’re the stepping stones that businesses are able to make very easily without really upsetting all of the stakeholders. So, I think that journey is shorter than we imagine but if we create a large barrier around the hype and the whole set of issues rather than focusing on what we practically know works today and how we build on that then I think we can make that cycle come in a lot sooner.”
David Bunker | Executive Director, Queensland Genomics Health Alliance | LinkedIn | Twitter