A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
This addresses challenges in healthcare AI for institutions aiming to optimize operations and improve patient outcomes, though it appears incremental as it builds on existing concepts without claiming major breakthroughs.
The paper tackles the systemic problems hindering AI adoption in healthcare, such as data privacy and explainability, by proposing a novel canonical architecture for developing and managing AI predictive models throughout their lifecycle, with qualitative evaluation on real-world problems.
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes. Despite the hype with AI, the full realization of this potential is seriously hindered by several systemic problems, including data privacy, security, bias, fairness, and explainability. In this paper, we propose a novel canonical architecture for the development of AI models in healthcare that addresses these challenges. This system enables the creation and management of AI predictive models throughout all the phases of their life cycle, including data ingestion, model building, and model promotion in production environments. This paper describes this architecture in detail, along with a qualitative evaluation of our experience of using it on real world problems.