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 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 be useful to allow systems that have implemented different terminologies or code sets to communicate temporarily while the systems migrate to the same terminology. These maps will not alter the UI for data entry but can be implemented alongside the data entry to allow users to see the term that is mapped to their entry term, or implemented in the back end when the information is being sent, received or retrieved and reported.

• Maps can also be used to help migrate legacy code systems to standard code systems like SNOMED, 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 allow systems with legacy code or proprietary code systems work with knowledge resources which uses standards like SNOMED CT to allow decision support.

Integration

As secondary users of data, it is often not possible to influence how data is collected as it is often historical data, or is data collected for a variety of use cases. Where data may be collected from different sources containing different code sets and/or different versions, maps allowing the comparison of different code sets by converting the codes to a single code set of SNOMED CT is useful.

Categorising and reporting

Data that is collected for one purpose is often required to be used for other purposes, and not just the original intention; this would be data re-use and re-purposing. 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 to allow 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 consistent representation for data collected in disparate systems using differing 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.