  Reinforcement Learning in Healthcare is typically concerned with narrow
self-contained tasks such as sepsis prediction or anesthesia control. However,
previous research has demonstrated the potential of generalist models (the
prime example being Large Language Models) to outperform task-specific
approaches due to their capability for implicit transfer learning. To enable
training of foundation models for Healthcare as well as leverage the
capabilities of state of the art Transformer architectures, we propose the
paradigm of Healthcare as Sequence Modeling, in which interaction between the
patient and the healthcare provider is represented as an event stream and tasks
like diagnosis and treatment selection are modeled as prediction of future
events in the stream. To explore this paradigm experimentally we develop
MIMIC-SEQ, a sequence modeling benchmark derived by translating heterogenous
clinical records from MIMIC-IV dataset into a uniform event stream format,
train a baseline model and explore its capabilities.
