# Integrated Radiology–Biochemistry Diagnostic Flow Framework for Emergency Clinical Decision Support: A Simulation-Based Educational Model

## TL;DR

This paper introduces a simulation-based framework combining radiology and biochemistry to teach emergency diagnostic reasoning through structured scenarios.

## Contribution

A novel simulation-based framework integrating radiology and biochemistry for emergency diagnostic education is proposed.

## Key findings

- The framework produced scenario-specific flowcharts linking biochemical cues to imaging decisions.
- Synthetic scenarios demonstrated consistent branching logic and clear escalation pathways.
- The model provides a structured basis for simulation-based teaching in emergency medicine.

## Abstract

This study presents a simulation-based Integrated Radiology–Biochemistry Diagnostic Flow Framework developed to support emergency diagnostic reasoning in a structured and stepwise manner. The framework brings together clinical presentation, relevant laboratory markers (when applicable), imaging findings, and an explicit decision/escalation point within a single pathway. It was applied to 40 fully synthetic emergency scenarios representing different acute care domains. Within this controlled setting, the standardized structure allowed each case to be organized toward a predefined management-oriented endpoint and facilitated transparent linkage between biochemical and radiological reasoning. Because the scenarios were entirely synthetic and no learners or clinical workflows were evaluated, the framework is intended as an educational proof-of-concept. It may serve as a practical teaching tool for medical students and early trainees and provides a basis for future empirical evaluation in training environments.

Background: Emergency radiology often demands rapid integration of clinical cues, biochemical markers, and imaging findings to support time-critical diagnostic reasoning. However, educational resources that explicitly structure this interdisciplinary integration particularly between radiology and laboratory medicine remain limited. Objective: Our objective was to develop an Integrated Radiology–Biochemistry Diagnostic Flow Framework as a simulation-based methodological proof-of-concept and to document its structure, logic pathways, and internal consistency across common emergency presentations. Methods: We designed an algorithmic framework combining (i) clinical triggers, (ii) targeted biochemical markers with predefined threshold and trajectory rules, (iii) imaging indication and modality selection (US/CTA/MRI/NCCT), and (iv) key radiologic patterns linked to escalation pathways. No patient data or human participants were included. Instead, forty fully synthetic emergency scenarios were generated to populate the framework and to examine logical completeness, branching coherence, and red-flag escalation routes. Results: The framework yielded scenario-specific diagnostic flowcharts that systematically connect biochemical escalation cues with imaging selection and expected imaging findings. The synthetic scenario library demonstrated consistent branching logic across conditions and enabled transparent visualization of imaging-centered decision pathways suitable for simulation-based teaching and structured case discussion. Conclusions: This study reports a reproducible methodological proof-of-concept framework and a synthetic emergency scenario library. Further learner-based studies are required to evaluate usability, perceived realism, and educational effectiveness in authentic training settings.