Clinical context layer vs retrieval-augmented generation
A clinical context layer maintains how facts relate to one another over time. Retrieval-augmented generation (RAG) finds fragments similar to a query and hands them to a model, which then has to work out the relationships on every call. The two are complementary, not interchangeable.
At a glance
| Aspect | Clinical context layer | Retrieval-augmented generation (RAG) |
|---|---|---|
| What it does | Maintains a persistent model of relationships among facts, updated as events arrive. | Retrieves fragments that are semantically similar to a query and presents them to a model. |
| When context is built | Continuously, independently of any single question. | At the moment of each inference, for that question. |
| Who works out relationships | The layer maintains them; the model interprets an already contextualized view. | The inference model must infer relevance, relationships and what is missing on each call. |
| Fit | Open-ended, changing situations where the right question is not known in advance. | Bounded problems with predictable information and a constrained question. |
When to use which
A clinical context layer fits when
- The situation changes continuously and signals arrive over time.
- Several applications need the same understanding of the same patient.
- Significance depends on relationships between facts, not on any one fact.
Retrieval-augmented generation (RAG) fits when
- The question is narrow and the source material is predictable.
- You need relevant passages from a known body of documents for one task.
- A bounded answer is enough and nothing needs to persist between calls.
What each does not do
A clinical context layer
- It does not replace inference: a model still interprets the context it is given.
- It does not retrieve documents by similarity; that remains a useful technique inside applications.
Retrieval-augmented generation (RAG)
- It does not establish how retrieved fragments relate to one another or determine their significance.
- It does not keep understanding between calls, so much of the same work is repeated.
Status
Specified · Prototype next · Not in clinical use
Clinical Context AI (IMI) proposes; a clinician decides.
Last reviewed
Related reading
Clinical context layer vs data warehouse
A data warehouse or data lake stores large volumes of data so it can be queried and analysed. A clinical context layer maintains what the facts mean in relation to one another for a patient, right now. A warehouse can be one of the sources a context layer draws on.
ComparisonClinical context layer vs EHR-embedded decision support
Decision support built into the electronic health record typically applies defined rules to recorded data at set points in a workflow. A clinical context layer maintains how facts, including signals outside the record, relate over time. It draws on the record and can feed insight back; it does not replace it.
DefinitionWhat is a clinical context layer?
The definition, what it is not, and how it works.
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