Case Study: Washington University in St. Louis advances privacy-preserving research with MDClone synthetic data

Washington University in St. Louis accelerates 3 pilot studies with MDClone synthetic data

Washington University School of Medicine in St. Louis faced significant challenges in making its complex patient data available for research at scale while strictly protecting patient privacy and confidentiality. The hurdles included disorganized data formatted for clinical care rather than research, long regulatory wait times, and limited resources. To overcome this, the school partnered with MDClone and implemented its synthetic data generation platform.

MDClone's solution created synthetic data that accurately mimics real patient populations, allowing researchers to analyze it as if it were original data without any risk to patient identities. This enabled Washington University researchers to conduct three successful pilot projects and develop advanced machine learning models. The results demonstrated that the synthetic data was scientifically valid, accelerating the pace of research, streamlining grant and abstract preparation, and uniquely enabling secure data sharing for high-impact studies, such as predicting mortality risk in heart failure patients.

Washington University in St. Louis