ED-Copilot: Reduce Emergency Department Wait Time with Language Model
  Diagnostic Assistance
Liwen Sun Abhineet Agarwal Aaron Kornblith Bin Yu Chenyan Xiong
In the emergency department (ED), patients undergo triage and multiple
laboratory tests before diagnosis. This process is time-consuming, and causes
ED crowding which significantly impacts patient mortality, medical errors,
staff burnout, etc. This work proposes (time) cost-effective diagnostic
assistance that explores the potential of artificial intelligence (AI) systems
in assisting ED clinicians to make time-efficient and accurate diagnoses. Using
publicly available patient data, we collaborate with ED clinicians to curate
MIMIC-ED-Assist, a benchmark that measures the ability of AI systems in
suggesting laboratory tests that minimize ED wait times, while correctly
predicting critical outcomes such as death. We develop ED-Copilot which
sequentially suggests patient-specific laboratory tests and makes diagnostic
predictions. ED-Copilot uses a pre-trained bio-medical language model to encode
patient information and reinforcement learning to minimize ED wait time and
maximize prediction accuracy of critical outcomes. On MIMIC-ED-Assist,
ED-Copilot improves prediction accuracy over baselines while halving average
wait time from four hours to two hours. Ablation studies demonstrate the
importance of model scale and use of a bio-medical language model. Further
analyses reveal the necessity of personalized laboratory test suggestions for
diagnosing patients with severe cases, as well as the potential of ED-Copilot
in providing ED clinicians with informative laboratory test recommendations.
Our code is available at https://github.com/cxcscmu/ED-Copilot.