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USE CASE · HEALTHCARE

EMR, clinical, and rare-disease data,
extended into research and AI diagnostics.

Medical privacy laws, bioethics regulations, HIPAA — RealDataEcho opens the way with synthetic clinical data, making diagnostic model training, cohort studies, and multi-center collaborative research possible while patient privacy stays protected.

CHALLENGES

The three big walls blocking
medical data utilization

Patient privacy burden

EMR and genomic records are the most sensitive data of all — lengthy IRB reviews and consent procedures delay research.

Rare diseases & small cohorts

Rare diseases with few patients lack the AI training data needed, stalling diagnostic model development.

Limits of multi-center data sharing

Sharing clinical data between hospitals involves demanding procedures, making large-scale studies and standard validation difficult.

SCENARIOS

The 3 key usage scenarios in clinical settings

The synthetic clinical data patterns most frequently applied in university hospital and research institute PoCs.

SCENARIO 01

Training AI diagnostic models on EMR

Convert real patient records safely into synthetic data. Collaborate with external AI developers and research labs to build diagnostic-support models quickly, without going through IRB procedures.

  • Shortened external IRB outsourcing procedures (8 weeks → 1 week)
  • Proven diagnostic accuracy of 92% — on par with source data
  • Passed medical-law and HIPAA de-identification standards
Synthetic EMR dataset (summary)
Patient IDAgeDx CodeHbA1cReadmit
SYN-P000152E11.97.4Yes
SYN-P000238I105.8No
SYN-P000367E11.98.9Yes
SYN-P000445J45.0—No
Diagnostic model accuracy (vs source)
0.92
SCENARIO 02

Rare-disease data augmentation

Augment rare-disease data with synthesis by up to 100×. AI diagnostic and prognosis-prediction models that were impossible with small cohorts become reality.

  • Small cohorts augmented up to 100×
  • Model F1 score improved by +0.21
  • Multi-center research data sharing available immediately
Rare-disease training performance
Source cohort of 320 patients — F1
0.62
After synthetic augmentation — F1
0.83
Augmentation factor
100×
PoC validated
SCENARIO 03

Clinical cohort studies & statistical analysis

Run cohort studies and epidemiological statistics on synthetic patient data without the IRB burden. Even in multi-center collaborations, source data never has to leave the hospital.

  • Multi-center research data sharing available immediately
  • 93% statistical concordance with source data
  • Synthetic sets can be published for reproducibility verification
Multi-center collaboration cycle
Before (IRB · DTA negotiation)
12 wks
With RealDataEcho
2 wks
Reduction
−83%
OUTCOMES

Measured results in the healthcare domain

Averages measured across university hospital and clinical research institute PoCs.

0.93
Distribution similarity
Validated on EMR and clinical data
100×
Rare-disease augmentation
Makes small-cohort training possible
−83%
Faster multi-center collaboration
Average 12 weeks → 2 weeks
< 0.01%
Re-identification risk
Passes the HIPAA Safe Harbor standard
"
"With only 320 rare-disease patients, training an AI diagnostic model was a struggle. After augmenting with synthetic data, the F1 score jumped from 0.62 to 0.83 — and multi-center collaborative research became far easier."
Professor L.University hospital in Korea · Clinical Medicine Research Center

Accelerate medical research while protecting patient privacy.

EMR · imaging · genomics · rare diseases — start a domain-specific PoC with a 30-minute demo.

Request a Demo → Healthcare PoC Inquiry