Joe Corrigan, head of intelligent healthcare, Cambridge Consultants, part of Capgemini Invent, examines the ‘collision’ of the medical and technology worlds.
With the slow inevitability of tectonic plates, the medical and technology worlds are colliding. From a distance, it seems that nothing is happening. But the ground is rising. New ways of working are being enabled by artificial intelligence (AI), data and edge computing. Doctors, patients and researchers are – like all of us – immersed in consumer-focussed digital services, which adds to the relentless pressure. This means that when a new user-centric medical service is launched, there can be sudden, seismic shifts in behaviour. Being able to foresee or cause these quakes will determine who ends up at the top of the mountain.
Let’s start with the world view from a pharma perspective. It is characterised by extended R&D cycles, leading eventually to the arrival of blockbuster drugs that have successfully negotiated clinical trials, with their ever-escalating costs. Contrast this with technology, the domain of agile development. Test fast, fail fast, learn fast, repeat. User experience is everything, and the ambition to forge personalised customer relationships drives continuous feedback and the dominance of social networks within the all-powerful digital ecosystem. Nevertheless, change is happening – against a backdrop of radical innovation in medtech and the increasing enthusiasm for digital adoption accelerated by the pandemic.
More of that in a moment, but first a word on drug discovery. For a while, AI has been at the vanguard of transforming this costly business. In 2018, the journal Nature alluded to the $2.6 billion price of developing a treatment. Much of that is effectively lost on the 90% of candidate therapies that fail between phase 1 trials and regulatory go-ahead. Now big biopharma companies are putting their faith in AI-led initiatives, while start-ups are using it to identify patterns hidden in large volumes of data. Quicker, cheaper and more successful drug discovery is of course the goal.
At Cambridge Consultants we’ve been conducting an AI-based research project into proteins, which are increasingly being repurposed for use in medicines, antibodies, vaccines and viral vectors for cell and gene therapies. The challenge is to change a protein sequence, subtly altering structure, to achieve desired performance. We’ve successfully applied natural language processing AI models to protein sequences to improve the probability of predicting their specific functions. The technique allows efficient optimisation of protein performance across a variety of applications, including drug development.
Clinical trials are another area of focus right now. We’ve created a digital platform that utilises AI in the system design of a trial to derive new digital endpoints that can take advantage of continuous, real-time monitoring. Currently, positive subjective responses to an Investigational Medical Product (IMP) lack credibility without clinical biomarker data, which is costly and invasive. But AI can remove subjectivity by tracking digital and chemical biomarkers in a non-invasive, continuous, contextual way and linking them to outcomes. Such a decentralised trials approach enables remote, continuous monitoring – reducing clinic visits, improving data quality and ultimately, patient outcomes.
AI is also beginning to take centre stage in patient care – and patient-choice. Take the interface between traditional diagnostics and a patient’s electronic health register (EHR). Early this year, our client Ellume’s device became the first fully at-home, over-the-counter connected COVID-19 test to gain FDA authorisation. The results of the antigen test are shared via the user’s smartphone to provide real-time reporting to the EHR, enabling health professionals to optimise therapy.
Patient choice, and consumer influence, is of course pivotal. Abbott’s FreeStyle Libre system, for instance, is designed to liberate people with diabetes from traditional finger-prick glucose monitoring. ‘Why prick when you can scan?’ as the marketing proposition says. For consumers, it’s all about the user-friendly experience. They prefer it and ask for it. For them, usability is king – and the industry needs to change to allow for this trend.
Many players are responding. They realise that behavioural insight and design innovation are becoming increasingly important where clinical adherence is low, such as asthma with a rate of below 50%. If adherence has the biggest impact on outcomes, surely creating a service that improves it is better than a better drug? It might sound provocative, but one could argue that patient needs are underserved by pharma. Traditionally, once a company has negotiated regulatory approval, the reward comes from as many doctor scripts as possible. Does the company want the real-world behavioural and contextual data that’s becoming available? Once the efficacy of the drug has been demonstrated, it can’t get any more approved than it already is.
Nevertheless, as we go forward it will no longer be enough to simply ship the drug – companies will have to consider how it fits into the patient’s lifestyle and shape a service accordingly. This means coming to terms with the interpretation of real-time data. The ability to develop AI models that continually ingest data from devices to learn from and rapidly iterate product is familiar to the likes of Amazon but unfamiliar to pharma. For now.
The emphasis will pivot to demonstrating outcomes that are not set in stone solely by clinical trials but are rethought and readdressed by consumer behaviour. Outcomes that are influenced by whether the patient takes the drug or experiences real-world benefits. The key opinion leaders – the doctors and other healthcare professionals – will be the kingmakers here, along with patients. They may very well prefer the service to a competing drug because it considers behaviours that improve outcomes.
The enthusiasm of healthcare professionals to explore AI in areas such as imaging or processing data is continuing to drive the pressure towards digital adoption. There’s a hugely significant market dynamic at play here. Once an interconnected platform for standardised data that can be accessed easily – and where the user benefits from adding their data to that platform – then everything changes. Network effects dominate and new de facto monopolies arise. Search algorithms on the web developed relatively slowly until Google used PageRank to exploit the metadata the users created when sharing links to create such a network effect.
But advances in AI now mean that when data, interlinks and interoperability are in place, the user-created value could create a ‘Google moment’ much more suddenly and with surprising consequences. This tipping point could very well create one winner in medical data – or at least one Amazon, Strava or Zoom for several niche fields.
What would the implications of such a shift be for medtech manufacturers and technology developers? Commoditisation of hardware leaps to mind – as evidenced by the Apple versus Android example. There’s a difference between the Apple infrastructure, which derives value from vertical integration of hardware and software, and the Android platform which provides compatibility and interoperability but razor-thin margins. It’s driven key players out of the market, including LG who quit making smartphones in July.
Medical devices are entirely different in that for each condition there are unique biomarkers that can be used to derive user actionable insights. And as I mentioned above, the support for the biomarkers comes from the KOLs in those markets. And when those user insights can be demonstrated to provide value to the end-user through outcomes, it becomes a truly differentiating opportunity for a manufacturer. Each patient group and subset of that group becomes a market with an independent platform value. So, we are likely to see commoditisation of certain services such as sharing data and secure storage, but clear differentiation and value that derives from niche applications that sit on those platforms.
In its paper – ‘Six winning roles for MedTech to thrive in the future of health’ – Deloitte identifies its most influential trends: data-sharing, interoperable data, access, empowered consumer, behaviour change and scientific breakthrough. Most of these are fuel to the fire of the tipping point of transformative data abilities I’ve described. Coming advances in AI, technology and interoperability will enable the next stage of evolution… the big switch to a data structure that allows for the network effect, the ‘Google moment’, and market domination. It’s time to listen to the tremors and act on them.
