Research and development in the treatment and prevention of disease is a costly business. According to one 2016 study, pharma companies spend, on average, $2.5 billion on trials for each new drug that makes it to market and a further $312 million on post-approval studies. Then there’s the challenge of repeat prescriptions; medication adherence for chronic conditions is estimated to be around 50%. However, the great cost attached to R&D still doesn’t tackle the significant barriers to sustainable patient service adoption.
But could AI soon change this?
AI-powered mHealth interventions are allowing pharma and healthcare innovators to support vast numbers of people quickly and cost-efficiently, facilitated by the devices we’re already carrying around in our pockets (or, more likely, in our hands).
Apple’s ResearchKit 2.0 framework, for example, was launched alongside iOS 12 and allows developers to create apps that can conduct surveys, obtain consent and assess motor activities, fitness, cognition, speech, hearing, hand dexterity and vision using the sensors in iPhone and Apple Watch.
Such technology is allowing health innovators to automate the collection of invaluable real-world patient data at scale, accounting for each individual’s own daily routine and circumstances and in real-time. With global smartphone proliferation forecast to reach 3.8 billion by 2021, vast numbers of participants can now be enrolled in mHealth studies and preventative health interventions, providing data daily or even hourly to create precise and accurate representations of large patient populations. What’s more, participants can be enlisted in just minutes or seconds - far more appealing than travelling to a clinic to be screened through lengthy paper-based forms.
From running clinical trials to collecting real world evidence aligned with patient services, it’s clear that AI holds enormous potential.
Novartis, for example, is harnessing mHealth to track the progression of ophthalmic disease in real time. Through its FocalView app, the company’s San Francisco-based digital innovation lab is conducting large scale, “site-less” trials with participants who may have struggled to attend a clinic in person.
And at Changing Health, service user data enables us to proactively identify social, environmental or demographic factors most likely to influence a person’s lifestyle behaviours, enabling the provision of automated, personalised support vis an app to help individuals sustain healthy day-to-day choices.
This is just the beginning. The technology is advancing at breakneck speed, creating ever more opportunities to reduce costs, shorten lead times and create more effective approaches to treating and preventing disease.
With those opportunities, however, comes the risk of losing sight of the patient’s own needs in the pursuit of technological innovation and efficiencies in R&D. If we forget to put patient centricity first in our enthusiasm for novelty or what we hope will be a technological breakthrough, patients will ultimately become disillusioned.
There are three key ways we can mitigate that risk:
1. Understanding the user’s own wants and needs
Health apps which are designed around patients and their needs achieve higher uptake and higher user satisfaction, reducing costs by necessitating less time spent addressing issues. That’s why mHealth should be designed by patient-focussed people rather than tech-focussed people; innovators need to understand why a person may be behaving in a way that is different to theoretical or modelled behaviour.
The key is, as the BMJ puts it, “putting the patient first in an open and sustained engagement of the patient to respectfully and compassionately achieve the best experience and outcome for that person and their family”.
2. Creating the best possible User Experience (UX)
User experience in the real world is a constant cycle, in which we have to monitor changing preferences. It’s a journey, not a destination, and there shouldn’t be a finite end to that journey; regular UX audits of any mHealth intervention, assessing how patients are using mHealth interventions and for how long, are essential to adapt to new patient populations and their individual needs.
3. Giving patients full control over their data
Research suggests that of the 24 most popular medicine-related apps, 19 share user data with third parties. It’s unclear whether how many users are aware of this - and even less clear whether they know that many of those third parties then pass their data to as many as 216 “fourth parties”. Informed patients understand how their data is being used; empowered patients take control of who’s using it, where and when.
In short: the next few years will bring some incredible new applications for big data in healthcare. Ensuring that data empowers patients as well as driving innovation will be complex. But by following three simple principles, we can ensure nobody gets left behind.
Professor Mike Trenell is co-founder of Changing Health, a specialist provider of personalised behaviour change programmes for Type II diabetes management, prevention and weight loss.