Population Digital Health: Continuous Health Monitoring and Profiling at Scale.

Unstructured: Our article provides a viewpoint on population digital health - the use of digital health information sourced from Health IoT and wearable devices for population health modeling - as an emerging research initiative for offering an integrated approach for continuous monitoring and profiling of diseases and health conditions at multiple spatial resolutions. Global healthcare systems are increasingly challenged by rising costs as life expectancy and the average age of people increases. Population digital health looks at how wearables, IoT, and AI can offer an alternative approach for understanding health issues within the population, significantly reducing cost and improving the completeness of information collection by current practices, such as electronic health records - including integration with mhealth personal health records - or survey instruments. This significantly improves our collective understanding of public health priorities, including factors affecting disease prevalence, occurrence and risk factors, ultimately helping to design targeted programmatic interventions apt at reducing the cost of healthcare provision and leading to better life quality, also reducing disparities. Realizing this vision requires overcoming several unique challenges, including data quality, availability, sparsity, and social and technical barriers in the use of health technologies. Our article highlights these challenges and offers solutions and empirical evidence to demonstrate how these challenges can be addressed. As population digital health addresses the impact large-scale sensor data collection and AI can have on improving healthcare delivery and society, we sincerely believe the topic is well within the journal's scope and would be highly interesting to its readership. Our experiments using a combination of real-world health IoT data and electronic health records also highlight the potential cross-disciplinary benefits of population digital health and challenge the research community to address the vision and challenges. Therefore, our article serves the dual purpose of challenging the research community and offering insights into the use of AI and sensor data, and how population digital health can serve as a catalyst for further research by the broader research community.