Healthcare equity analytics platforms are increasingly used by major countries worldwide. A prominent example is the HL7 FHIR SDOH Clinical Care Implementation Guide, developed by the Gravity Project. It has been used in U.S. clinical healthcare since 2019, and countries such as the UK, Canada, and Australia have developed their own adaptations of these metrics. More recently, the European Union enacted the European Health Data Space (EHDS) in 2024.
I think one of the major setbacks of these analytics is that they lack complete, consistent data, cannot identify individuals, are subject to data biases, and can lead to incorrect interpretations. For example, if the existing algorithm shows that black people have lower healthcare spending, it doesn’t mean they need less care.
Secondly, these metrics are applied within medical settings, but up to 80% of issues are rooted in systemic racism, housing policy, environmental pollution, and wealth inequality. Even if analytics platforms excel at measuring social inequality, they lack the financial, legal, or social authority to provide remedies. It requires systemic policy changes to address the root environmental, social, and economic causes.
Thirdly and lastly, an architectural redesign of analytics platforms is needed to provide fast, up-to-date data on individuals, rather than relying on the existing, outdated, and fragmented data. Moreover, it should be deployable across the broader community and geographic setting, not limited to the medical system.
What is your take on this? Did I miss something?
If you are interested, I am thinking about shedding light on the healthcare systems of a few countries that employ health equity analytics. Please let me know in the comments.
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