How supports reuse of clinical information
- Extent of practical use
- Approaches to implementation
- Lessons learnt
Why is this important?
The objective of
and all users of SNOMED CT is to facilitate the accurate recording and sharing of clinical and related health information and the semantic interoperability of health records.
What is this?
How SNOMED CT Supports Reuse of Clinical Information
is a clinical terminology with global scope covering a wide range of clinical specialties, disciplines and requirements. As a result of its broad scope, one of the benefits of
is a reduction of specialty boundary effects that arise from use of different terminologies or coding systems by different clinicians or departments. This allows wider sharing and reuse of structured clinical information. Another benefit of
is that the same data can be processed and presented in ways that serve different purposes. For example, clinical records represented using
can be processed and presented in different ways to support direct patient care, clinical audit, research, epidemiology, management and service planning. Additionally, the global scope of SNOMED CT reduces geographical boundary effects arising from the use of different terminologies or coding systems in different organizations and countries.
|t||SNOMED CT concept model|
also allows additional details to be considered when retrieving data. For example, the concept |
which has a
that specifies that the |
and this allows the organism causing this disease to be analyzed.
Extent of Practical Use
Many systems use
to represent some types of clinical information. The extent of use is varied in terms of:
- The clinical content captured (i.e. what is included and what is not).
- How the structure of this content relates to the structures in the records.
- The scope and consistency of use and reuse (i.e. within and across national and local organizations, across departments, within proprietary applications or specifically configured instances of proprietary applications).
Approaches to Implementation
has been implemented in a variety of ways which differ in the extent to which they harness particular features of the terminology. In some cases, these differences merely reflect the specific requirements of a particular use. Other factors include the design of existing systems prior to the introduction of
, sophistication of available technology and support for a range of other health informatics standards.
- Representation of stored clinical information.
- Ease of data entry
- Different approaches to data entry are valuable and may be mediated in a variety of ways to enable ease of data entry.
- The method of data entry should not result in inconsistent representations of the same types of clinical information.
- The most effective approaches constrain data entry specific to the clinical context and reason for use.
Unconstrained searches across the entire content of are rarely appropriate for routine data entry.
Constraints that limit data entry to a fixed set of concepts are useful where the clinical context and reasons for use are narrow.
- Constraints that alter dynamically to meet requirements of a particular data entry context offer a more generalizable approach that can be configured to meet different requirements.
Natural Language Processing (NLP) to parse and tag text with expressions has been found useful in some applications.
Communication interfaces, including message structures, need to be designed to retain the common elements of clinical content structure and coding. Communication should enable the receiving system to reuse the clinical information effectively based on the expressions within it.
- Retrieval, analysis and reuse
Record storage and indexing can be designed to optimize use of the semantic features of for selective retrieval and to support flexible analytics.
Retrieval in the patient care setting should result in the display of clinical records including highlighting of critical information selected taking account of the computer processable expressivity of .
- Real time decision support ranges from simple flagging of contraindications to guidelines for investigation and management.
- Batch mode decision support identifies patients with chronic diseases and risk factors who require recalls for review and other scheduled interventions.
- Analysis of data can be completed for selected populations of patients for a variety of purposes including audit, service planning, epidemiology and clinical research.
The features of
support reusability of clinical information. However, reusability also requires a consistent structured representation of clinical information that complements the meaning supported by
. Without this, overlaps and conflicts between structural and terminological representations of clinical content can result in ambiguous and potentially conflicting interpretations.
Human factors may result in inconsistent recording of similar clinical information. This issue can be minimized by effectively constraining data entry.
An important limitation is the diversity of views related to the structure of clinical information and the overlap between information models and terminology. There are also differing views on application design, different requirements for collection of clinical information and different views on record structures and data entry methods appropriate to different use cases.