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Overview
The choice between how to model grouped data often boils down to choosing between a pre-coordinated and post-coordinated approach. Pre-coordinated data entails defining concepts such that they are comprised of multiple terms with a very specific definition (think: Severe Left Knee Pain). Post-coordinated data involves creating granular individual concepts that are then grouped together to create responses to questions, with each concept otherwise not making sense on its own (think: Severe + Left + Knee + Pain).
In theory, post-coordinating data is the better (more flexible/powerful) option. However, in a real world setting, post-coordinated is much harder to do for not just data entry, but also data re-use in reporting, decision support, research, etc. It is critical to understand how the data is going to be used and, based on that, deciding whether it makes more sense to pre or post coordinate data.
Some Examples:
Pre-Coordination | Post-Coordination |
|---|---|
Severe Complicated Measles | Severe + Complicated + Measles |
Left Upper Lobe Scarring | Left + Upper + Lobe of Lung + Scarring |
Recurrent intravascular papillary endothelial hyperplasia of the right middle finger | intravascular papillary endothelial hyperplasia + middle finger structure + right |
Red Flags:
When pre-coordinating leads to an explosion of concepts ("combinatorial explosion")
When post-coordinating leads to creating concepts that are more granular than practically needed
Be careful with rule out, unconfirmed, suspected items; something that puts a diagnosis into question or negates it should be handled carefully
Additional helpful information:
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