NURAI 2026 OCR-to-EHR Healthcare Digitization Simulator

Paper-Based Patient Information to Structured EHR Data

An interactive HTML5 architecture for demonstrating how scanned patient forms can be transformed into editable digital records, clinical context summaries, vital-sign observations, and exportable CSV/XML data files for healthcare digitization in Iraq.

NURAI 2026 OCR Simulation EHR Mapping Vital Signal Visualization CSV/XML Export

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1. Paper Template Intake

Upload a scanned form image or generate a mock Iraqi hospital patient template.

Upload scan/image

No paper template loaded yet. Click “Generate sample paper form” to simulate a handwritten clinical document, or upload an image/text file.

2. OCR Output and Field Extraction

The OCR node translates paper text into structured candidate fields. The text can be edited before mapping.

Simulated OCR text

OCR quality

Field mapping

Clinical context

3. Structured EHR Record Builder

Extracted paper information is normalized into patient demographics, encounter details, diagnostics, interventions, and notes.

3b. Reconstructed Table

Tabular pages (drug schedule, vitals, labs) are rebuilt into rows & columns from the OCR geometry — not a flat text dump.

3c. Teach the model (correct mistakes)

Fix any wrong reading below. Each correction is saved as a training pair (crop → correct text) to fine-tune the model later — human-in-the-loop.

4. Vital-Signal Observation Node

Vital signs extracted from paper are presented as clinical observations and visualized using HTML5 canvas.

5. Comprehensive Clinical Report

Human-readable summary generated from mapped EHR fields, diagnostic context, and intervention notes.

No report generated yet. Run the OCR simulation and map the data to build the clinical report.

6. Paper-to-System Node Translation Map

Each extracted phrase is embedded into a clear system component node and normalized as an EHR data element.

Category	Paper information	EHR data element	Normalized output	Confidence
No mapped fields yet.

7. Export Node

Export structured patient information as CSV or XML for downstream hospital information systems.

Export preview will appear here.

8. Recommended Production Architecture

A future-ready implementation path for a real OCR-to-EHR platform.

Layer	Component	Purpose
Input	Scanner/mobile capture	Securely capture paper forms and handwritten templates.
AI	OCR + handwriting recognition	Extract text, fields, checkboxes, timestamps, and tabular data.
NLP	Clinical entity extraction	Identify symptoms, diagnoses, interventions, medications, and vital signs.
Validation	Human review dashboard	Confirm uncertain fields before saving to the EHR.
EHR	FHIR-style resources	Represent Patient, Encounter, Observation, Condition, and Procedure concepts.
Export	CSV/XML/JSON APIs	Share structured files with reporting, analytics, and hospital systems.
Security	Audit, encryption, access control	Protect sensitive patient information and track all actions.