# openEHR in Real Systems: Where Does It Break Down in Practice?

**URL:** https://discourse.openehr.org/t/openehr-in-real-systems-where-does-it-break-down-in-practice/11932
**Category:** Tools
**Created:** [11 April 2026 08:39 UTC](https://discourse.openehr.org/t/openehr-in-real-systems-where-does-it-break-down-in-practice/11932 "2026-04-11T08:39:01Z")
**Posts on this page:** 1
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### Author: ![Cozy\_Beard](https://discourse.openehr.org/user_avatar/discourse.openehr.org/cozy_beard/32/5883_2.png) [@Cozy\_Beard](https://discourse.openehr.org/u/Cozy_Beard)
#### Post date: [13 April 2026 19:07 UTC](https://discourse.openehr.org/t/openehr-in-real-systems-where-does-it-break-down-in-practice/11932/5 "2026-04-13T19:07:59Z")

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Hi Pablo, thanks again for taking the time to engage on this.  
To clarify a bit further, yes, my main focus is really on the logical layer rather than the physical persistence layer. What I’m trying to better understand is how teams manage the lifecycle of archetypes and templates once they are already in active use across multiple systems.  
In particular, I’m interested in how mature implementations deal with situations where a template evolves but is already tightly coupled to:

API contracts that external systems depend on  
Validation rules embedded in services  
Downstream consumers like reporting or analytics pipelines  
From my experience in workflow-driven and integration-heavy environments, even small structural changes can introduce unexpected side effects if versioning and coordination are not handled very deliberately.

So I’m curious how this is typically approached in practice:  
Do teams treat archetype/template versions similarly to API versioning, with strict backward compatibility guarantees  
Is there a common pattern for isolating changes, such as versioned endpoints or transformation layers  
How do you usually coordinate these changes across teams and environments without slowing down delivery  
I’m especially interested in any real-world patterns or tooling that have proven effective at scale, since that’s where these challenges tend to surface the most.  
Appreciate your insights, this is a really valuable discussion.

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