Lack of Data Access, but Not Availability, Hinders AI Training for High-Risk Conditions in Ontario.

Advanced disease prediction is an important step toward achieving a proactive healthcare system. New technologies such as artificial intelligence are very promising in their ability to predict the onset of future disease much earlier than has been possible in the past. However, artificial intelligence requires training and training requires data. In this study, we report on the ready availability, but lack of accessibility and real-time access to healthcare data required to treat five high-cost diseases that are predictable using AI and preventable using well-established evidence-based therapies. There is urgent need for action on the part of governments and other interest holders to define and invest in the infrastructure required to make data for training and deploying AI at scale more accessible.

Design for a Canadian Digital Health Policy & Practices Observatory.

Canada has struggled to make digital health a reality. We identified 6 key issues that appear to impede progress: 1) an inability to coordinate the actions of a rapidly evolving set of stakeholders, 2) patients who lack the ability and resources to play a meaningful role in health system decision-making, 3) world-class innovation that doesn't reach the market, 4) an inability to kick-start interoperability projects that can catalyze system transformation, 5) an inability to procure early-stage innovative technologies at scale, and 6) an inability to share data seamlessly across organizational silos for patient coordination and care, health system management and research. We propose a set of policies and practices that can help Canada assess, monitor and provide feedback to stakeholders and citizens on how well they are progressing toward seamless digital health.