The problem
The number of clinical trials is rising, yet participation rates keep falling and many trials fail to recruit a single patient. Patients have no easy way to discover or enrol in new, potentially life-saving trials they could become eligible for, because the information is scattered, hard to aggregate and confusing to navigate. At the same time, researchers spend heavily on recruitment with no efficient way to be both selective and active in finding suitable participants.
What we did
We helped build Horizon, an active scanner that continuously searches the web for new treatments and trials a patient could qualify for and notifies them automatically, much like a price alert for a flight. When a match appears, patients can apply online to enrol, with their data kept anonymised and secure until they choose to share it. Machine learning helps spot patterns in how different groups respond to treatments and matches patients to local trials, which improves recruitment efficiency and lowers costs for researchers. It runs on a subscription model for patients alongside participant-matching fees for researchers.
The outcome
We help patients find treatments and trials they would otherwise miss, while helping researchers recruit faster and bring new treatments to market sooner.