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Clinerion Patient Network Explorer

Clinerion Patient Network Explorer

Optimized study design, precise site selection and faster patient search and identification for clinical research - in real time.

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Rare Diseases: the way forward

Enabling better identification and diagnosis by combining EMR data analysis and AI

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26.02.2020, Basel, Switzerland

Douglas Drake

By Douglas Drake, Customer Solutions Director at Clinerion Ltd

February 29th, 2020, is not only a rare leap year day, but also Rare Disease Day, an observance organized by the European Organization of Rare Diseases (EURORDIS) and supported by the National Organization of Rare Diseases (NORD) to raise awareness for unknown or overlooked illnesses.[...]



Rare diseases, by nature of being rare, are often untreated, undiagnosed or frequently misdiagnosed. In addition, rare diseases may present differentially  – meaning patients don’t all appear the same but are heterogenous and therefore hard to diagnose –  which leads to a substantial delay in diagnosis, and makes it very difficult for both patients, their families and healthcare givers to manage. Studies show that the impact of rare diseases is much wider than the individual affected and represent a significant challenge for the healthcare system itself.

In a survey of patients and caregivers in the USA and UK, patients reported that it took on average 7.6 years in the USA and 5.6 years in the UK to get a proper diagnosis, during which time patients typically visited eight physicians (four primary care and four specialist) and received two to three misdiagnoses [1] .

Of the 7,000 known rare diseases, 90% do not have an FDA-approved medication, which means patients have to go with no treatment or go with off-label use of existing medicines to treat their symptoms [2] . Patients with rare diseases can go up to 20 and 30 years before diagnosis, or even go entirely undiagnosed. [3,4,5]


Clinerion is therefore proud to highlight our partnership with Volv Global [Read full press release] to enable better rare disease identification and diagnosis. Together, we are working to enable our EMR-based Patient Network Explorer hospital network to identify phenotype, condition and treatment models better.

This will allow us to flag potential rare disease patients correctly and then work with their treating physicians to create the outreach programs, test the patients for rare disease, and reach a correct diagnosis.

Volv Global is applying cutting-edge AI and machine learning technology to highlight possible rare disease patients. Volv’s unique methodology not only ascertains patient cohorts at risk of disease, but also helps with trial recruitment, understanding patient journeys and can make assessment of real market size for generating rationale to create new drugs to meet these important unmet needs.

Together, we are working to enable better healthcare, for these severe but undiagnosed conditions, one patient at a time.

Read more about our partnership with Volv >

1. Shire Report 2013; Rare Disease Impact Report: Insights from patients and the medical community (

2. Sara Toth Stub 2020; Conquering Rare Disease - Should taxpayers keep paying to develop drugs for unusual disorders? (

3. Meta A et al. Eur J Clin Invest 2004; 34:326-42

4. Mistry PK et al. Br J Heematol 2009; 147: 561-70

5. HOS Analysis; Data from 2019 Patient Flows, Fabry Disease data ex-US, GD/MPSII including US

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