Syntegra Medical Mind generates realistic synthetic patient records, preserving clinical patterns from real healthcare data like Electronic Health Records (EHR), claims, and genomics, enabling healthcare professionals to learn and analyze without compromising patient privacy.
Syntegra Medical Mind helps healthcare professionals (HCPs) to access and analyze synthetic patient data for various purposes, such as research, education, and decision-making. Syntegra Medical Mind uses generative AI to create synthetic records that are statistically equivalent to the original data, but do not contain any identifiable information that could be traced back to real patients. This way, Syntegra Medical Mind ensures patient privacy and confidentiality while providing valuable insights for HCPs.
Syntegra’s synthetic data closely matches the format and statistical properties of the original, capturing even rare cohorts and outliers without needing to access or retain actual patient information. This process enables clinicians to gain a comprehensive understanding of disease patterns among specific populations while avoiding traditional privacy concerns. The ability to create such fit-for-purpose healthcare data is a testament to Syntegra's capabilities.
Imagine a hematologist working on a rare blood disorder. In the traditional setting, obtaining sufficient real-world data could be challenging due to the rarity of the condition and privacy restrictions. With Syntegra, the hematologist can access synthetic data that accurately represents a larger cohort of patients with this rare disorder. The system could generate a detailed temporal sequence of medical events for these synthetic patients, allowing the clinician to explore diverse disease trajectories, potential comorbidities, and varied responses to treatments. This robust and privacy-preserving data source can greatly enhance the hematologist's understanding and subsequently improve patient care.
Syntegra also aids in addressing data bias and improving algorithmic fairness, essential aspects of modern healthcare education. It can expand datasets, understand and correct for data bias, and even impute missing values to minimize gaps in the data. This process helps clinicians to make informed decisions and design equitable treatment plans.
The synthetic data layer created by Syntegra serves as a game-changer in healthcare organizations. By removing traditional barriers to data access and utilization, Syntegra fuels innovation needed to improve patient care and clinical outcomes. The provided synthetic data is validated by industry-leading metrics, ensuring its fidelity and privacy, thus fostering trust and promoting its use in various educational contexts.
In a research paper published in Frontiers in Artificial Intelligence, the authors explore the potential of virtual cohorts and synthetic data in advancing dementia research. They demonstrate the use of virtual cohort techniques to create a synthetic dataset that closely resembles a highly detailed sample of preclinical dementia research participants. By employing innovative computational techniques, the authors propose that synthetic data and virtual cohorts offer a promising solution to address the limitations of traditional research methods. Ultimately, these advancements have the potential to drive scientific developments in the field of dementia.
“Virtual Cohorts and Synthetic Data in Dementia: An Illustration of Their Potential to Advance Research.” Frontiers in Artificial Intelligence. May 2021. https://doi.org/10.3389/frai.2021.613956