Fostering a decentralised model to improve health management requires honest, straight thinking. From exploring the opportunity of distributed ledger technologies to highlighting the importance of consent as a service, we have to address all of the challenges related to health data sharing — and that includes the monetisation of health data across multiple healthcare stakeholders. How do we build a sustainable and inclusive financial system that auto-regulates this new health data industry?
AI may play a role in auto-regulating this new data and consent-related economy
It has already been proven that various stakeholders would benefit from access to third-party data in the healthcare industry, at different stages of the patient journey and in very different contexts. The marketplace, based on blockchain infrastructure, will create the network and related incentives needed for data owners and collectors to make their data available to others; it will contribute to unlocking the data economy’s full potential.
Considering this new data-empowered healthcare system, we realise how interconnected all parties are, and the inherent value in access to qualitative data, with the patient remaining in the centre of everything:
Welcome to the health data economy! Researchers and experts estimate this could dramatically increase profit in the healthcare industry up to 55% by 2025 (Accenture Share-of-profit increase per industry between baseline in 2035 and AI steady state in 2035). The AI healthcare market is expected to hit $6.6 billion by 2021, according to Accenture. The study added that clinical health AI applications have the potential to create annual savings of $150 billion for the U.S. healthcare economy by 2026.
Build a wealth re-distribution model to foster health data sharing
All patients may shortly be all able to sell more of their own data and control their consent, using advanced services like Synapse.ai (see above). However, this also raises critical ethical: Given the amount of value and benefit expected from all parties, as well as the projected savings in healthcare spending due to increased efficiency along the healthcare value chain, should we consider a smart model which helps us to ensure that wealth is re-distributed consistently? What would that model look like?
First, the value of our health data needs to be tracked along the value chain, and a decentralised model would help stamp any data contribution to healthcare models, research or product development, or even emergency services. Companies like Ocean Protocol are already building this traceability into their chain. But what if we train a model to understand the contribution our data makes to the healthcare economy? This algorithm may help us partially understand the causality effect between different data sources. As individuals, it may also be able to provide valuable information and give relevant recommendations regarding the data we share.
This would lead us to a second need: we must reinvent our healthcare economics and rewards system, both at an individual and corporate level, in order to design a system that serves the entire population, especially the under-served. For instance, how do we value blood data from an emerging middle-class Indonesian if this contributes to the development of a new immunotherapy luxury drug for the affluent population? First, if we could demonstrate how valuable his contribution was, we could make him eligible to receive the drug for free. Second, we should consider the relative value of such data and ensure that the value generated will be at least equal to the healthcare value needed by this person in his region. This kind of value map and system would naturally require lots of monetary regulation. We believe AI, empowered with decentralised systems, can help to do lots of the matching pre-work to identify real value in a broader economic context.
Any citizen needs to be empowered and motivated. As previously described in our first Live With AI 2018 report and in Living Digital 2040: Future Of Work, Education And Healthcare by the Lee Kuan Yew Centre, all of these personalised technologies, information and data matter very little if citizens are not motivated to manage their own health. And beyond their own health management, we would need to consider their peers’ ecosystem, as we will always prioritise the value we bring to our- selves and to our community. Artificial intelligence, embedded in a decentralised model, could help map this value and enable a system which motivates people’s participation. How? By continuously driving a citizen’s willingness to share more data, leveraging strong personalisation triggers, and behavioural science principles. We need to remember that we each have our own biases, and none of us will perceive this benefit to society in exactly the same way.
A decentralised model will undoubtedly be very valuable to our new data economy and significantly improve our healthcare system. That said, plenty of unknowns remain, and we anticipate several different barriers. On the one hand, an economic one, as our own data will be worth more than ever, but its value will be fractioned. On the other hand, there is a legal boundary that we must address in order to instil trust at the core of this new health data sharing model. AI already crosses borders, fostering health-data inter-operability, making a case for a worldwide “consent as a service” decentralised model and the auto-regulation of our health data value without bias, and with the unique objective to serve everyone. As it is all about our new data ownership and its evolution, new legal frameworks are needed to serve international and universal regulation.
Pierre Robinet – OGILVY CONSULTING