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Comment by noonanibus | Hacker News Reader

Forgive me if I'm mistaken, but isn't this exactly what the FHIR standard is meant to address? Not only does it enable global inter-health communication using a standardized resource, but it's already adopted in several national health services, including (but not broadly), America. Is this not simply a reimplementation, but without the broad iterations of HL7?

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Right, it would make more sense to use HL7 FHIR (possibly along with CQL) as a starting point instead of reinventing the wheel. Talk to the CodeX accelerator about writing an Implementation Guide in this area. The PlanDefinition resource type should be a good fit for modeling cancer guidelines.

https://codex.hl7.org/

https://www.hl7.org/fhir/plandefinition.html

This is the comment I was looking for.

You would aim to use CQL expressions inside of a PlanDefinition, in my estimate. This is exactly what AHRQ's, part of HHS, CDS Connect project aims to create / has created. They publish freely accessible computable decision support artifacts here: https://cds.ahrq.gov/cdsconnect/repository

When they are fully computable, they are FHIR PlanDefinitions (+ other resources like Questionnaire, etc) and CQL.

Here's an example of a fully executable Alcohol Use Disorder Identification Test: https://cds.ahrq.gov/cdsconnect/artifact/alcohol-screening-u...

There's so much other infrastructure around the EHR here to understand (and take advantage of). I think there's a big opportunity in proving that multimodal LLM can reliably generate these artifacts from other sources. It's not the LLM actually being a decision support tool itself (though that may well be promising), but rather the ability to generate standardized CDS artifacts in a highly scalable, repeatable way.

Happy to talk to anyone about any of these ideas - I started exactly where OP was.

I downloaded and opened an CDS for osteoporosis from the link (as a disease in my specialty), I need an API key to view what a "valueset" entails, so in practice I couldn't assert if the recommendation aligns with clinical practice, nor in the CQL provided have any scientific references (even a textbook or a weak recommendation from a guideline would be sufficient, I don't think the algorithm should be the primary source of the knowledge)

I tried to see if HL7 was approachable for small teams, I personally became exhausted from reading it and trying to think how to implement a subset of it, I know it's "standard" but all this is kinda unapproachable.

You can register for a free NLM account to access the value sets (VSAC). HL7 standards are approachable for small teams but due to the inherent complexity of healthcare it can take a while to get up to speed. The FHIR Fundamentals training course is a good option for those who are starting out.

https://www.hl7.org/training/fhir-fundamentals.cfm?ref=nav

It might seem tempting to avoid the complexity of FHIR and CQL by inventing your own simple schema or data formats for a narrow domain. But I guarantee that what you thought was simple will eventually grow and grow until you find that you've reinvented FHIR — badly. I've seen that happen over and over in other failed projects. Talk to the CodeX accelerator I linked above and they should be able to get you pointed in the right direction.

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