Healthcare has always been a top priority for Canadians – it is one of the few industries that impacts every person at every stage of their life – but COVID-19 has created greater focus on the systems and relationships that make up the circle of care. From doctor’s clinics and hospitals to retail pharmacy and long-term care, this past year has not only revealed how broad this circle is, but just how agile it can be with the right solutions in place. The crisis has spurred incredible examples of digital transformation and has ultimately challenged existing assumptions about how we operate and what is possible. More specifically, it has crystalized the need for improved data sharing across the many health stakeholders, from primary care to public health, and the value of AI and machine learning across data platforms.
While on a virtual visit to Canada last month, Microsoft CEO, Satya Nadella sat down with leaders from across Canada’s healthcare industry and public sector to discuss global trends, common challenges to healthcare transformation and opportunities for the future.
Each of Canada’s provinces and territories operate under different healthcare systems and privacy protocols; this has led to data segmentation and created a barrier to population-level data. According to Satya, there are two elements that need to be implemented to unlock the potential of this data for clinical use. The first is differential privacy, which simultaneously enables researchers and analysts to extract useful insights from datasets containing personal information and offers stronger privacy protections. This is achieved by introducing “statistical noise”. The noise is significant enough to protect the privacy of any individual, but small enough that it will not impact the accuracy of the answers extracted by analysts and researchers.
The second element, which can be added on top of differential privacy, is a regime of machine learning called federated learning. With this framework, the learning is done centrally, as well as locally. If this were to be applied in Canada, every province and territory could have its own data and the complexities of the centrally shared model can be minimized. We believe the combination of federated learning and differential privacy can be a technology infrastructure that can support countries like Canada in benefitting from population-level data insights without sacrificing privacy.
We’re seeing how this type of data sharing is possible, even across borders. As part of the Cascadia Innovation Corridor, BC Cancer and Fred Hutchinson Cancer Research Center are creating data sharing constructs, removing barriers to data discovery and data access which make breakthroughs in research difficult. The next step is determining how the data can be applied on the clinical side.
Creating better outcomes
When asked how the public sector should approach sharing data with the private sector, Satya noted that private healthcare organizations, like pharmaceutical companies, need access to clinical data to innovate in areas like precision medicine. Population-level data is needed to drive individual, personalized medicine – that’s the paradox. It starts with the public sector establishing the boundaries of policy, which should prioritize the health outcomes for citizens, and from there determining how a private company can participate in that ecosystem, for example by lowering costs and/or creating more personalized care.
It’s been inspirational to see healthcare end-to-end, with leaders across the public and private sector taking hold of digital tools to accelerate their mission. Canada’s healthcare community demonstrated unmatched responsiveness as they innovated to deliver personalized care to all Canadians – and this is only the start. There will continue to be a structural change in healthcare that will deliver better health outcomes and will ultimately support global economic recovery. For more information on healthcare innovation, please visit https://www.microsoft.com/en-ca/industry/health