Ankle brachial pressure index

[heather.leslie]

@ian.mcnicoll

I think the GOAL archetype has some sort of attempt at this by allowing the Target Path to point to a fragment of ADL to identify eg. the units and range constraints for a body weight.

I understood this to be pointing to where the data related to the goal is found/stored eg for an openEHR query - a very explicit link to the data structure. It came from Sam, a very long time ago. This is not quite the same as your interpretation

@ian.mcnicoll

Inspired Oxygen, which in most case sits nicely as a CLUSTER within the content of pulse oximetry or other ENTRY archetypes but occasionally needs to be a stand-alone OBSERVATION.

I’m curious to understand the use case…

[heather.leslie]

@thomas.beale

We need to think together (clinical + technical groups) on what these additions might be.

Maybe, but your explanation about why you didn’t need Clinical Program collaboration created a significant barrier to participation, confirmed by:

@thomas.beale

… I think we get a reasonable amount of insight into current clinical needs this way.

There is no doubt that both programs working together in sync is a critical success factor for openEHR IMO, but we need a serious change in culture for the future Clinical Program to be able to effectively work alongside the techs/engineers/Specs program, as equals. I didn’t ask for a collaboration so that you could teach me. In fact, I wanted opportunities to educate you about our modelling practice because, time and time again, despite your long list of tech-friendly clinicians, it is clear that you often don’t understand the reasoning and experience that underpins the current modelling, or have any curiosity to find out.

@thomas.beale

The lesson here in my view is that we need a meta-classification of clinical statement types that indicates the modelling style to be used, i.e. things like:

• native data group (molecular style): BP, Apgar, Problem/Dx etc
• virtual data group (molecule with citations of other elements): BMI and some other scores/scales;
• common data collection - typical lab panels e.g. CHEM7, thyroid, LFT etc
• standalone element (atomic style) - height, weight, HR, most lab analytes
• … maybe more

I know you want to engineer modelling patterns to make sense, but health data is inherently messy and for every pattern we potentially identify, we find instances that break them. There is a danger in potentially overthinking this only to create more confusion with little added value .