Rare Disease Patient Discovery

Increasing diagnosis rates and time-to-diagnosis for a rare liver disease

Our clients, a leading treatment centre and a large pharmaceutical partner, wanted to identify patients eligible for diagnosis of a rare lysosomal storage disorder. Early and accurate diagnosis is critical for effective treatment and patient outcomes.

Real World Health was tasked with developing a model to identify this cohort and flag for diagnostic review.

Our expert team collaborated with client-clinicians to identify relevant datasets and features significant to the disease’s prevalence. Using a machine learning model, we used these features to predict those most likely to be eligible for diagnosis. The model was rigorously tested and validated to ensure accuracy and reliability, achieving a sensitivity of 73% (true positives vs. false positives), indicating a high identification rate of eligible patients.

The implementation of the machine learning model significantly increased the number of newly diagnosed patients eligible for treatment. This led to a marked increase in clinical trial enrolment, enhancing research and development efforts for the rare disease. Our collaborative effort demonstrated the power of combining expert knowledge with advanced data analytics to drive meaningful improvements in healthcare.

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