> For the complete documentation index, see [llms.txt](https://docs.snomed.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.snomed.org/snomed-ct-practical-guides/snomed-ct-mapping-guide/introduction/2-use-cases.md).

# Use cases

## Interoperability

For interoperability, it would be ideal for all systems in the healthcare ecosystem to implement the same terminology in the same way, and with the same information model. However, this is not realistic, as there are many systems with historical proprietary clinical systems already in existence.

In these situations, maps can be useful in different ways.

* Maps can enable systems that use different terminologies or code sets to communicate temporarily while they migrate to a common terminology. These maps will not alter the UI for data entry, but can be implemented alongside data entry to allow users to see the term mapped to their entry term, or implemented in the back end when information is being sent, received, retrieved, or reported.
* Maps can also be used to help migrate legacy code systems to standard code systems like SNOMED CT, by providing a way to translate historical terms to the new standard; this would be a one-time, one-step migration strategy
* Maps may be required to enable systems with legacy or proprietary code systems to work with knowledge resources that use standards such as SNOMED CT for decision support.

## Integration

As secondary users of data, we often cannot influence how it is collected, since it is often historical or collected for a variety of use cases. Where data may be collected from different sources with different code sets and/or versions, maps that allow comparison of different code sets by converting the codes to a single SNOMED CT code set are useful.

## Categorising and reporting

Data collected for one purpose is often required to be used for other purposes, not just the original intention; this is data reuse and repurposing. Codes collected might need to be assigned to higher-level groups for reporting, funding, cohort identification, etc. Maps can be used to assign higher-level grouper code sets to the original code set, allowing data to be categorised and reused.

## Conformance

Data conformance is about ensuring the data collected meets a set of defined data quality measures. Maps to a standardised clinical terminology, such as SNOMED CT, can provide a consistent representation of data collected across disparate systems using different measures or methods. Providing accuracy, completeness and consistency to data that would otherwise not be standardised. This does not mean though that maps will transform 'dirty', or poor quality data into high quality data they potentially will add to the quality through standardisation through enhanced quality measures.

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