- Search for rare disease patients for clinical trials efficiently and in real-time?
- Leverage real-time EHR data for faster and more efficient search, identification, and stratification of rare disease patients for clinical trials?
- Find rare disease patients, even if they are undiagnosed or misdiagnosed?
Rare diseases often present as heterogeneous in nature, which makes identifying and recruiting rare disease patients for clinical trials a challenge, as rare disease patients are often undiagnosed or misdiagnosed. The identification, diagnosis and proper treatment of rare disease patients are all large hurdles.
The numbers and location of clinical trial candidate patients, the phenotypes they exhibit, the treatment regimens they currently undergo, and the comorbidities they experience, are all critical factors that need to be thoroughly understood to improve the care of patients with rare diseases and support their inclusion into clinical trials.
For the more than 6,000 rare diseases on record, there is strong evidence
that at least 50% of patients are never correctly diagnosed.
Thankfully, Clinerion has a solution. Patient Network Explorer simplifies the process of finding candidate patients for recruitment into clinical trials not just by disease code, but also by triangulating on sets of phenotypes, conditions, and treatment models found in electronic health records (EHRs).
In addition, Clinerion has launched its Federated Machine Learning Platform, which uses machine learning (ML) technology and trains novel predictive diagnostic algorithms on EHRs in our global network of hospitals to find both diagnosed and undiagnosed rare disease patients efficiently.
Clinerion operates on strict patient privacy guidelines compliant with GDPR.
