The push for Real-World Evidence in the EU
By 2025, the European Medical Agency (EMA) will expect Real-World Evidence (RWE) to be included in regulatory submissions and post safety evaluations. As a result, healthcare organizations will need to adapt their data strategy, making their data more (securely) accessible to other players within the healthcare ecosystem.
RWE holds the promise of improving medical and drug development, authorization, and efficacy assessment. By using real-world health data, researchers can gain deeper insights into patient populations, enabling the development of personalized and precision medicine. This means that patients will have access to better, more targeted treatments that are tailored to individual patients’ needs, resulting in better health outcomes.
In practice, the use of real-world data poses significant challenges. Because real-world data, like from EHRs, is incredibly sensitive, it’s rightfully protected by stringent privacy safeguards. As a result, extracting insights from sources outside of traditional clinical trials, such as electronic health records (EHRs), registries, and mobile devices, is a substantial task. While necessary, this limits speed and flexibility in analysis and research.
To harness real-world data in a secure and effective way, organizations have to navigate these concerns and find a balance between keeping sensitive data protected, and making it more usable for analysis. Supported by a 2.2M CHF grant from the Swiss government, Decentriq is set to make simple, secure real-world data collaboration a reality.
Decentriq tackles major hurdles in real-world data adoption
Decentriq was chosen for the 2.2.M CHF Innosuisse grant from over 1000 proposals to further the development of our healthcare data collaboration solution. Our platform’s combination of advanced Privacy Enhancing Technologies (PETs) makes it uniquely suited to address key challenges faced by healthcare organizations as they work with sensitive data.
Sensitive data is never shared, only insights
- Confidential Computing ensures data encryption end-to-end, even during computation. It also provides proof that data remains private throughout. This eliminates the need to trust cloud providers, or even us at Decentriq.
- Differential Privacy safeguards sensitive information by adding a layer of “noise” to the data, providing statistical guarantees about its privacy.
Full flexibility for analysis that fits existing workflows
- Synthetic Data allows data scientists to explore the data without revealing sensitive patient information, aiding privacy-preserving collaborations.
- Data scientists can use R, Python, and SQL to run analyses, making Decentriq easier to incorporate into existing workflows
- Decentriq is designed to integrate seamlessly into existing infrastructure, extending the current analysis workflows of the Data Scientists to new and unseen data.
Innosuisse grant to fuel innovations in healthcare data collaboration
The Innosuisse grant, sponsored by the Swiss government, will fuel the development of our data collaboration platform and help us tailor it to meet the unique needs of healthcare and life sciences organizations. The platform will offer a complete solution to collaborating on sensitive patient data throughout various stages:
- Identifying relevant patient cohorts
- Analyzing and exploring data in a privacy-preserving way
- Enabling secure and privacy-preserving data transformations and harmonizations— without revealing patient data
- Developing and training new ML/AI algorithms on partner data through typical data science workflows
- Deploying trained models on sensitive data without exposing the model parameters or the sensitive data
Our solution will empower hospitals, pharmaceutical companies, and biotech firms to maximize the potential of real-world data while ensuring the highest levels of data privacy and security.
Paving the way for future healthcare innovations
As the EU moves ahead with its vision for RWE in medicines regulation by 2025, Decentriq is working at the forefront of this transformation to provide healthcare and life sciences organizations with the tools they need to harness real-world data securely and effectively. With a simpler method for data collaboration and analysis, the healthcare ecosystem can more easily develop innovative treatments to speed up improvements in patients’ care journey.