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Related Experiment Video

Updated: May 24, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

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Published on: January 15, 2017

Modeling the Clinical Reasoning Workflow: A Dynamic, Time-Aware CDSS for the Emergency Department.

Chaeyeon Park1, Sanga Ahn1, Hansol Chang1,2

  • 1Dept. of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic Clinical Decision Support System (CDSS) using a Time-Aware Retrieval-Augmented Generation (RAG) framework to aid emergency departments. The system improved diagnostic accuracy from 69.5% to 86.7% by integrating patient data and clinical guidelines.

Keywords:
Clinical Decision Support Systems (CDSS)Emergency DepartmentLarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)Time-Aware Modeling

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Emergency Departments (ED) face challenges in rapidly diagnosing critical conditions.
  • Timely and accurate diagnosis is crucial for effective patient management in emergency settings.
  • Existing systems may not adequately integrate dynamic patient data with clinical guidelines.

Purpose of the Study:

  • To develop and evaluate a dynamic Clinical Decision Support System (CDSS) for emergency departments.
  • To assess the impact of a Time-Aware Retrieval-Augmented Generation (RAG) framework on diagnostic accuracy.
  • To determine the system's ability to integrate evolving FHIR data with clinical guidelines.

Main Methods:

  • A retrospective pilot study involving 70 cases was conducted.
  • The study included critical conditions like Aortic Dissection, Myocardial Infarction (MI), Stroke, and Trauma.
  • A dynamic CDSS utilizing a Time-Aware RAG framework was implemented to process patient data.

Main Results:

  • The Top-1 diagnostic accuracy of the CDSS significantly improved from 69.5% at triage to 86.7% at discharge.
  • The system demonstrated effectiveness in integrating dynamic FHIR data with established clinical guidelines.
  • A notable reduction in diagnostic uncertainty was observed.

Conclusions:

  • The dynamic CDSS powered by a Time-Aware RAG framework shows significant potential in enhancing diagnostic accuracy in emergency departments.
  • Integrating real-time patient data with clinical guidelines is a viable strategy to improve emergency care.
  • Further validation in larger studies is warranted to confirm these promising results.