In healthcare and clinical trials, when privacy, rapid responses and subject scarcity are prevalent, how can synthetic research help?
Synthetic Data Explained:
Data Generation: Algorithms create data that mimics real datasets by understanding patterns, correlations, and statistical properties of the original data.
Preserving Privacy: The generated data retains the overall structure and statistical characteristics of the real data but does not include any actual personal or sensitive information.
Use in Analysis: Researchers and analysts use this synthetic data in various analyses, simulations, or model training, benefiting from realistic data patterns without risking privacy breaches or ethical concerns.
Synthetic Data in Action:
Acute Myeloid Leukemia Case Study by Day One Strategy
Design: The study contrasted synthetic data with real patient interviews to understand Acute Myeloid Leukemia (AML) patient journeys. Using GPT 4.0 for synthetic responses, researchers crafted detailed patient personas and tested responses against real patient interviews.
Responses:
💬 AML Patient, Age 26: “I couldn’t take some of my GCSEs. I got three main GCSEs because I was home tutored. But going to college, I couldn’t really go to a massive college because of the germs after the transplant and stuff”
💬 AI: “My education was interrupted significantly. Even though tutors and online courses helped, it wasn’t the same”
💬 AML Patient, Age 62: “My family are supportive, but I think I am trying to, as much as possible, be normal for them. They don’t see me get down very often. People think you’re back to normal”
💬AI: “There’s been a lot of anxiety and stress, not just for me, but for my entire family. We’ve had to have difficult conversations about the future, about finances, and about the potential outcomes if the treatment doesn’t work as we hope”
Outcome:
Synthetic data can be valuable
-rapid, reliable and cost-effective
-helpful with subject scarcity & privacy concerns
-provides broad, plausible themes and insights
-output is much aligned to the overall themes found from true patients
Synthetic data does fall short
-reliance on the underlying real data for accuracy
-data sources can be bias
-limitations in capturing the depth of human experiences
-lacks nuanced experiences that real patient stories revealed.
Conclusion
In cases where time is against you, privacy is a concern, subjects are hard to find, and costs must be considered (sounds like clinical trials) synthetic data offers a true and valuable cost-effective rapid research tool.
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