Responsible Use Guideline

MakeData.ai, Inc.

Summary

Synthetic healthcare data are intended for interoperability testing, solution development, and education. The data produced are entirely fictional, may include inaccuracies, and do not represent actual persons or clinical scenarios. Any likeness to a real person or event is purely coincidental.

Responsible Use

• Avoid privacy violations, do not combine synthetic data with real data.
• Don’t use for clinical decisions or trials.
• Don't rely solely on synthetic data for final models.
• Document usage and inform stakeholders.

Example Use Cases

MakeData.ai supports several key use cases for synthetic data:

1. Software Testing: Use synthetic data to test software applications in healthcare settings. This includes verifying that applications work correctly under various scenarios without exposing real patient data.
2. Demo Data Creation: Create datasets for demonstrating software features and capabilities. This is useful for highly contextualized customer demonstrations.
3. Proof of Concept Creation: Use synthetic data to build and validate initial models or prototypes. This allows you to test ideas quickly and safely before using real data.
4. Initial ML Model Training: Train machine learning models on synthetic data for initial model development stages. This helps in initially building models faster, removing early data access issues. Avoid overfitting and full model training with synthetic data.

Understanding the Limitations

• Demographic Representation: Be aware that synthetic data from MakeData.ai is not statistically accurate for all demographic details. Do not use it for research or analysis that requires precise demographic accuracy.
• Clinical Consistency: While our data is clinically consistent, it should not be used as a substitute for real clinical data in decision-making processes or clinical trials.