Reading
EHRGym is a containerized reinforcement learning environment specifically designed for training and evaluating computer agents that can perform clinical workflows in Epic-like electronic health record (EHR) systems. It supports GRPO training and natively integrates with the TRL framework.
Section 01
EHRGym is a containerized reinforcement learning environment designed specifically for training computer agents that can operate Epic-like electronic health record (EHR) systems. It supports GRPO training and integration with the TRL framework, addressing core obstacles in medical AI deployment such as complex interactions with real EHRs and compliance sensitivity, while providing realistic and secure training scenarios.
Section 02
Artificial intelligence faces challenges in translation to the medical field. The key issues are the complex interfaces of real electronic health record (EHR) systems, sensitive data, and strict compliance requirements, making it difficult for researchers to directly train and test agents. Traditional simulation solutions fail to capture details of real workflows such as multi-step decision-making and cross-module navigation.
Section 03
It adopts a dual-service containerized design: the Next.js EHR application mimics Epic's layout and interactions (including modules like patient lists and medical record reviews), and the OpenEnv environment server implements standard interfaces such as reset()/step(). It follows OpenEnv standards to ensure ecological interoperability and has natively integrated with the TRL library to support GRPO fine-tuning.
Section 04
The task library is divided into three stages: unit skills (basic navigation/filtering), single objectives (ordering medical instructions/completing documents), and multi-step workflows (full clinical processes). Each task has scoring criteria; rewards combine terminal success and process progress, while penalties are applied for invalid operations and errors.
Section 05
It uses Synthea to generate synthetic medical records in FHIR format (zero privacy risk, scalable and controllable). It adopts standard encodings like LOINC/SNOMED CT/RxNorm to ensure authenticity, and medical record documents are generated based on structured templates.
Section 06
The action space includes low-level mouse and keyboard operations as well as high-level semantic actions. The observation space includes target text, screenshots, routing, etc. The reward design follows sparse terminal rewards, dense process rewards, and penalty mechanisms.
Section 07
It can be used for clinical decision support (assisting information extraction and decision-making), interface optimization (analyzing agent behavior to improve design), medical education (virtual training), and multi-modal AI (extending support for data like medical images).
Section 08
Current non-goals: Not a pixel-perfect clone of Epic, no full enterprise EHR functions. Future directions: Expand clinical scenarios, integrate medical knowledge bases, enable multi-agent collaboration, and introduce time/resource constraints to simulate real environments.
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