  In the emergency department (ED), patients undergo triage and multiple
laboratory tests before diagnosis. This time-consuming process causes ED
crowding which impacts patient mortality, medical errors, staff burnout, etc.
This work proposes (time) cost-effective diagnostic assistance that leverages
artificial intelligence systems to help ED clinicians make efficient and
accurate diagnoses. In collaboration with ED clinicians, we use public patient
data to curate MIMIC-ED-Assist, a benchmark for AI systems to suggest
laboratory tests that minimize wait time while accurately predicting critical
outcomes such as death. With MIMIC-ED-Assist, we develop ED-Copilot which
sequentially suggests patient-specific laboratory tests and makes diagnostic
predictions. ED-Copilot employs a pre-trained bio-medical language model to
encode patient information and uses reinforcement learning to minimize ED wait
time and maximize prediction accuracy. On MIMIC-ED-Assist, ED-Copilot improves
prediction accuracy over baselines while halving average wait time from four
hours to two hours. ED-Copilot can also effectively personalize treatment
recommendations based on patient severity, further highlighting its potential
as a diagnostic assistant. Since MIMIC-ED-Assist is a retrospective benchmark,
ED-Copilot is restricted to recommend only observed tests. We show ED-Copilot
achieves competitive performance without this restriction as the maximum
allowed time increases. Our code is available at
https://github.com/cxcscmu/ED-Copilot.
