Synthetic data in Healthcare
Explore the value of synthetic data in healthcare
Healthcare organizations and the role of data
Healthcare organizations’ data usage is important as it enables evidence-based medical decisions, personalized treatments, and medical research, ultimately leading to enhanced patient outcomes, improved operational efficiency, and advancements in medical knowledge and technologies. Synthetic data can significantly benefit healthcare organizations by providing privacy-preserving alternatives. It enables the creation of realistic and non-sensitive datasets, empowering researchers, clinicians, and data scientists to innovate, validate algorithms, and conduct analysis without compromising patient privacy.
Healthcare industry
- Improve Patient Care
- Reduce the time required to access data
- Protect Personal Health Information (PHI) from the Electronic Health Record System (EHR , MHR)
- Increase data utilization and predictive analytics capabilities
- Address the lack of realistic data for software development and testing
- Share data and collaborate efficiently with health systems, payers, and related institutions to solve bigger problems faster
- Overcome data silos
- Perform studies and clinical trials to understand the drug product’s impact (efficacy) on this new disease
- Complete a full analysis in less than a month, with less effort
- Accelerate the pace of data-driven research by providing the ability to access data faster and easier
- Access to more data for hypothesis evaluation
- Solution for generating and sharing data in support of precision healthcare
- Check project feasibility before submitting for original data access
Case studies
Why do health organizations consider synthetic data?
- Privacy-sensitive data. Health data is the most privacy-sensitive data with even stricter (privacy) regulations.
- Urge to innovate with data. Data is a key resource for health innovation, as the health vertical is understaffed, and over-pressured with the potential to save lives.
- Data quality. Anonymization techniques destroy data quality, while data accuracy is crucial in health (e.g. for academic research and clinical trials).
- Data exchange. The potential of data as a result of collaborative data exchange between health organizations, health systems, drug developers, and researchers is enormous
- Reduce costs. Healthcare organizations are under extreme pressure to reduce costs. This could be realized via analytics, for which data is needed.
Why Syntho?
Syntho’s platform positions Health organizations first
Time series and event data
Syntho supports time series data and event data (often also referred to as longitudinal data), which typically occurs in health data.
Healthcare data type
Syntho supports and has experience with the various data types from EHRs, MHRs, surveys, clinical trials, claims, patient registries and many more
Product road map aligned
Syntho’s roadmap is aligned with strategic leading health organizations in the US and Europe
Do you have any questions?
Talk to one of our healthcare experts
Proud winners of the Global SAS Hackathon
Syntho is the winner of the Global SAS Hackathon in Healthcare & Life Science
We are proud to announce that Syntho won in the healthcare and life sciences category after months of hard work on unlocking privacy-sensitive healthcare data with synthetic data as part of cancer research for a leading hospital.
Healthcare blog
Save your synthetic data in healthcare report!
- Explore the role of synthetic data in healthcare sector
- What is synthetic data and why do healthcare organizations use it?
- Value adding reference use cases in healthcare (like pharma, hospitals, health-tech, etc.)
