Why Every Health Data Scientist Should Know About OMOP CDM

Author(s): Mazen Ahmed

Originally published on Towards AI.

Standardising Healthcare Data

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A large issue I struggle with at work is standardising healthcare data.

I gather data from hospitals around the world in an attempt to produce a centralised and unified database for participating hospitals and researchers to benefit from.

Each hospital seems to have it’s own way of recording healthcare data. There are many standards out there which hospitals follow but there is no universal agreement on how healthcare data should be recorded.

There are many domains in medicine, each of which have specific variables that need to be collected. For example cardiology data collection requires the collection of different variables than oncology or endocrinology. These domain-specific requirements adds a layer of complexity to standardisation, not only do we need to align general health data such as age, sex and BMI, but we must also ensure that the needs of each speciality is sufficiently met.

Disparate Hospital Data Sources produced in Canva

Producing a data model that has the ability to accurately capture information from every healthcare domain is an immense challenge. However, if this challenge is met and the resulting model is widely adopted this would lead to a scalable and interoperable… Read the full blog for free on Medium.

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Published via Towards AI