Rare Disease Patient Discovery
Screen for potentially undiagnosed rare disease patients by deploying algorithms across an entire population
Audit treatment patterns and outcomes to identify patient groups requiring escalation to later lines of treatment
Expedite patients through their diagnostic odyssey, sparing them additional tests and potential misdiagnoses, and improving outcomes
Establish existing standard of care and patient characteristics to inform trial design and elgibility
Identifying cases of rare disease can be challenging, often involving a long pre-diagnosed phase of disease and much clinical expertise in making the diagnosis. Even when diagnosed, optimising treatment may be challenging, meaning some patients may not receive the latest new treatments when they most need them. There is an incredible opportunity to address this challenge, with machine learning and AI, which can rapidly accelerate referral to treatment.
how it works
We engage with local data teams to bring together big datasets using our proprietary Data Science Platform. Here, it’s combined, transformed and organised for analysis. Through clinical engagement and modelling, we identify factors that may predict undiagnosed rare disease.
Machine learning models are built and tailored to the specific disease. Patients are scored, ranked or highlighted based on eligibility predicted by the model.
Careful validation is conducted in collaboration with local clinicians.
Users access the platform to view lists of patients for clinical review. Clinicians review their full medical records with the aim of expediting diagnosis or changes in treatment. The outcomes of this review are fed back into the system, which improves over time.
Patient lists that bring together key information that can assist in diagnostic review, in a clear, easy to navigate way.
An advanced model, that optimally finds patients for clinical review, carefully validated and built with clinicians along the way.
A talented and expert team with a long history of working with rare diseases and hospital datasets.
Case studies
Increasing diagnosis rates and time-to-diagnosis for a rare liver disease
Find out more