The idea that cognitive technology can transform the healthcare system in radical ways holds a special place in Matthew Howard‘s head. The UK Lead at IBM Watson Health has no doubts: “I consider it to be the most important development in healthcare analytics globally.” And, using cognitive applications such IBM Watson to help augment the ability of the clinical scientific community, he says, is critical for meeting future life science demands.
In fact, healthcare is a key strategic imperative to IBM. If you just look at some of the quotes by the company, they say very openly that Watson Health is their moon shot.
I wanted to explore with Howard what the impact of this promising technology is going to look like. In this interview, he shares his views on the potential of IBM Watson to the wider society, the democratisation of data, the role of machine learning, its strengths and the challenges, and the element of ‘trust’ around digital healthcare.
Gloria Lombardi: Let’s introduce the cognitive IBM Watson Health. What’s so special about this technology?
Matthew Howard: Watson is able to understand unstructured information such as normal human language and written words. The technology can combine the best of the machine’s ability to understand vast volumes of information simultaneously with a new ability to interact with people in a much more natural way.
This type of capability allows us to use IBM Watson as an augmented intelligence – it helps those working in healthcare to understand the information out there faster.
GL: Could you give me some examples of its concrete application?
MH: A good example is using IBM Watson as a treatment adviser. If you train Watson using the very best medical literature, guidelines and expertise, for example in cancer care, you can develop a ‘trusted adviser’ that is able to make recommendations on how to treat the patients – the Watson adviser will provide doctors with potential cancer treatment recommendations based on some of the world’s leading cancer experts who train the application. Based on that knowledge and expertise, clinicians can consider different treatment options for any particular individual. Watson is not there to make the decision; it is there to help inform the decision and provide supported, evidence-based suggestions.
Going beyond the adviser solution, there is an enormous wealth of other opportunities – from building wellness technology coaches to genomics innovation.
GL: What does it all mean for the future of work of clinicians and the medical community?
MH: What the technology will do is to improve the doctors’ ability to have at their fingertips the very best scientific literature – something like having an adviser at your side who can instantaneously provide information from the literature that is relevant, updated and contextually accurate to the patient in front of them, it’s going to be transformational.
We have very high expectations of our clinicians – we expect them to know all of the journals, all the clinical trials, every treatment, changes to drugs’ availability, and more. These are incredibly high expectations. So, what we are trying to do here, is to provide technology that enables our doctors to meet those expectations by combining their own expertise with the expertise of Watson.
GL: Will the clinicians of the future require familiarity with technological innovation? I am thinking about not just having medical knowledge and expertise but also confidence in using new technology such as IBM Watson.
MH: To some extent. One of the important parts of how cognitive technology works is that it will always tell you why it has reached a given set of recommendations. IBM Watson is designed to provide you with all the evidence, and uses it to explain the suggestions it is making.
So, it is not strictly necessary for the clinician, the scientist or the nurse to fully understand how every part of how the technology works. But, it is important for them to be able to see what was the particular scientific literature, for example, that made Watson go in a certain direction.
GL: Let’s explore the IBM Watson Health ecosystem. How do you select your partners? For example, recently I read about your deal with the Tel-Aviv-based startup Nutrino, which I found interesting. The aim here is to develop a nutrition application that enables smarter eating decisions.
MH: We work with ecosystem partners like Nutrino that we think have a great potential opportunity, exciting ideas and novel technologies. We provide them with access to Watson Developer Cloud to accelerate development. Nutrino actually participated in the IBM Alpha Zone Accelerator Program in Israel, the first and only IBM Accelerator worldwide.
We are already working with a broad range of healthcare and life science organisations, for example our recently announced partnership with Novo Nordisk to build diabetes care solutions on the Watson Health Cloud.
GL: Which geographical locations have the potential to be influenced by the technology in a radical way? Any unexpected regions? Perhaps countries that, until now, we would haven’t thought could be reached and benefit from it.
MH: One of the biggest challenges for us today is to work out how to get highly innovative, leading technologies into emerging markets. Longer term, we will need to find effective ways to provide cognitive technology in those areas at scale. The potential impact of this type of expert advice on locations where there are not enough medical staff is huge.
GL: Looking at the technology itself, what are the main challenges of IBM Watson at present?
MH: Cognitive technology is very new and developing. We need to build awareness quickly. It also can take time to train.
It will take time to find everything that works best. We are just at the beginning of the augmented intelligence journey.
But, this technology will evolve fast. It will move into the mainstream and become used daily by a much wider group of people. We should see a real step change in the amount of impact that has over the coming few years.
GL: Can we clarify the role of machine learning with respect to the IBM Watson Health cognitive technology?
MH: In this context machine learning means that, by training IBM Watson, the technology gets better at what it does. For example, when we train Watson as an adviser type of solution, we tell it when it gets things right and when it gets things wrong. By going through this iterative process several times, IBM Watson becomes smarter – the algorithm enables it to understand the data it is presented. Ultimately, the application learns how to change what it recommends and provide better advice.
GL: I’d like to conclude by discussing the element of ‘trust’, which seems to be one of the key challenges of digital healthcare today. What do you think should be done more, or less, or differently, to encourage individuals and organisations to be open with the opportunity brought by new technology?
MH: We (the healthcare industry) need to provide real world evidence of how technology can be huge force for good. Big data will only achieve scale and impact in healthcare if people use it and trust it – that means stakeholders across the whole system have to be able to trust each other in a transparent, controlled way to use data for the benefit of patients and healthcare systems, while always managing risk and ensuring privacy.