Why can’t anyone find the undiagnosed 50% patients? AI-based identification to drive rare disease diagnosis

Rare diseases are often under diagnosed and we find even that the prevalence is under estimated. Typically, a large cohort remain undiagnosed, up to 50% of a rare disease population are getting late diagnosis. 

Why? 

Because the hardest to diagnose diseases do not present consistent symptoms. The diseases are heterogenous in their presentation and patients typically come to see their doctors with different sequences of symptom onset.  These patients will also at first have very non-specific and confounding symptoms. 

There are more than 7,000 rare diseases so no human can know them all.  

When the patient is with the doctor, the clinician will often think of a disease that that they
understand and know about, which is similar and more common and diagnose
accordingly. 

When we think of ultra-rare diseases then in many cases a clinician may never see a patient with the disease.  So, if we think that through, then clinician education on these diseases may be the wrong approach, given that there are 7,000 and how seldom a clinician will encounter these patients. 

So, what would the ideal situation be? 

We will show how Volv find more patients earlier and also provide a way to bring them into the
clinical pathways that will bring earlier correct diagnosis and appropriate treatment regimes.