Hospital EHR → Digital Twin → Personalized Treatment Plans
Create personalized digital patient models from EHR data to simulate treatment outcomes and optimize care pathways for individual patients.
Workflow Steps
Epic MyChart API
Extract patient clinical data
Use Epic's FHIR API to pull comprehensive patient data including medical history, current medications, lab results, imaging data, and vital signs into a structured JSON format for analysis.
Python (scikit-learn)
Build predictive patient model
Create a machine learning model using patient data to predict treatment responses, disease progression, and potential complications. Use ensemble methods combining clinical decision trees with neural networks.
MATLAB Simulink
Create physiological digital twin
Build a dynamic simulation model representing the patient's cardiovascular, metabolic, and organ systems. Input real patient parameters to create a personalized physiological model that responds to different treatments.
Power BI
Generate treatment recommendation dashboard
Create an interactive dashboard for clinicians showing simulated outcomes for different treatment options, risk scores, and personalized care pathway recommendations based on the digital twin predictions.
Workflow Flow
Step 1
Epic MyChart API
Extract patient clinical data
Step 2
Python (scikit-learn)
Build predictive patient model
Step 3
MATLAB Simulink
Create physiological digital twin
Step 4
Power BI
Generate treatment recommendation dashboard
Why This Works
Combining Epic's comprehensive patient data with Python's ML capabilities and MATLAB's physiological modeling creates accurate digital twins that can predict individual patient responses to treatments.
Best For
Healthcare providers want to simulate treatment outcomes for individual patients before implementing care plans
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