---
title: Volv Global Blog | Case Study
description: Case Study | Blog on the topic of rare and orphan diseases and other related topics
---

# Volv Global Blog

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## Case Study

![This case study derives from a pharmaceutical company that approached Volv to develop a prediction model to find patients suffering from a rare disease](https://blog.volv.global/hubfs/Blog%20images/bkg_DataScienceHeader_Notext.svg)

[Rare Diseases,](https://blog.volv.global/tag/rare-diseases) [Innovation,](https://blog.volv.global/tag/innovation) [Data Science,](https://blog.volv.global/tag/data-science) [Case Study,](https://blog.volv.global/tag/case-study) [AI,](https://blog.volv.global/tag/ai) [Predictive models,](https://blog.volv.global/tag/predictive-models) [inTrigue](https://blog.volv.global/tag/intrigue)

### [Finding Patients for Rare Diseases: A Case Study](https://blog.volv.global/finding-patients-for-rare-diseases-case-study)

#### The Case Study Problem

This case study derives from an ongoing Volv engagement, which started in July 2017. A pharmaceutical company approached Volv Global to develop a prediction model to identify additional patients suffering from the rare disease treatable by its specialist medicine.

This pharmaceutical company faced four difficulties: the disease prevalence was one in a million of population; specialist clinicians were able to diagnose the disease with no more than 76% accuracy; only one in four patients were ever identified; and those that were identified, were done so generally after six years of misdiagnoses. To compound these four difficulties, the pharmaceutical company was unable to provide medical records for any already diagnosed patients.

[Christopher M de M Rudolf](https://blog.volv.global/author/christopher-rudolf) 

[Read More](https://blog.volv.global/finding-patients-for-rare-diseases-case-study)