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Standards for Health Data Equity

*** This theme is currently dormant. Get in touch at if you’re interested in re-activating it! ***

This theme aims to tackle biases in health data science through developing and sharing standards, guidelines and best practice principles.

Health data come with biases, assumptions and knowledge gaps that reflect existing flaws in medical practice and the society we live in. Inequitable healthcare access and delivery contribute to biases in health data - it is well documented that minoritised and underserved population groups are systematically under-represented in health datasets. This leads to a risk that insights and innovations derived from these data may not apply to, or work for, entire sections of society. 

To advance health equity in data-driven research and innovation, first we need to:

1) understand the limitations of existing health datasets,

2) be able to make informed decisions on the use of their data and the potential biases that exist within them, and

3) invest in initiatives to improve health datasets of the future. 

The focus of this theme is to share and align efforts on developing standards for health datasets to support health equity.

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