In this section, we consider how user interfaces can be designed to harness the capabilities of SNOMED CT, and to make clinical querying more accessible to non-technical users. We describe both user interfaces for authoring queries, as well as user interfaces for viewing query results.
When querying patient records containing SNOMED CT-enabled data, a variety of interfaces may be adopted to support the user in authoring queries. In this section we first consider user interfaces for querying SNOMED CT, and then look at user interfaces for querying SNOMED CT enabled patient records.
When querying clinical data, it may be necessary to first define a subset of SNOMED CT concepts (e.g. disorders or procedures) that may then be compared against values in a patient record. A number of different options exist for creating these SNOMED CT subsets, including:
Selecting individual SNOMED CT concepts (i.e. extensional definition)
Authoring queries directly using a query language (i.e. intensional definition)
Authoring queries using a structured form (i.e. a form which generates an intensional definition)
This approach uses a SNOMED CT browser to allow individual SNOMED CT concepts to be searched, selected and added to a subset. For large subsets this can be quite time consuming, however it is quite suitable for smaller subsets. A number of commercial tools are available which help to perform this task, including Apelon's Distributed Terminology System and B2i's Snow Owl terminology server. The figure below illustrates Snow Owl's authoring interface for Simple reference sets.
Other user interfaces allow a subset to be defined using a text-based query written using a predefined query language (e.g. SNOMED CT Expression Constraint Language, or SNOMED CT Query Language). These interfaces tend to be for the more technical user. However, some clinical users may be taught to use these interfaces if required.
Two examples of this style of interface are illustrated below.
A third style of user interfaces for authoring SNOMED CT subsets uses a structured form. A form-driven query tool may allow the user to select an operator (e.g. 'memberOf', 'descendantOf'), the concept or subset to which this operator is applied (e.g. 'Example problem list', 'Disorder'), and then one or more attribute values to limit the set of concepts returned. (Note: The attribute name may either be selected from a list, or hard coded on the form). Once the form is completed, a text-based query is automatically constructed from the selected values, and executed against SNOMED CT. This style of interface can be designed to allow users to exploit the rich semantics of SNOMED CT, while shielding them from the underlying technical details. The following figure illustrates how a generic form-driven interface for authoring SNOMED CT queries works. Vendor products which implement form-driven interfaces for authoring SNOMED CT queries include B2i's Meaningful Query web interface.
Patient Record Query Interfaces
When SNOMED CT queries are integrated (or embedded) into patient records queries, additional constraints are often added across demographic data (e.g. age, address) and episode of care data (e.g. healthcare provider, dates). These data items are often referred to as 'concrete values' and are typically not included in a terminology. A number of styles of interfaces are used to author patient record queries that include SNOMED CT content, including:
Free text semantic search
Queries using a predefined language (e.g. SQL, XQL, OQL or AQL)
Queries using a structured form (including both SNOMED CT and concrete value criteria)
The following figure shows an example of a search for 'diabetes' using Cerner's Semantic Search tool. This tool enables clinicians at the point of care to search in real time through a patient's multiple charts, pathology reports and other documents for topics such as 'heart disease' and 'diabetes', using SNOMED CT's hierarchical and non-hierarchical relationships.
When a SNOMED CT enabled query over patient records is executed, the results of this query can be visualized in a number of ways, including tables, charts, scatter diagrams and colored epidemiology maps. While some of these results visualization techniques can be used with any coding system, others are able to utilize the unique features of SNOMED CT in powerful ways.
For example, the following figure shows a report produced by Cerner's data warehouse query tool. This tool uses a simple graphical interface which directly creates powerful reports using the SNOMED CT hierarchy content. The screenshot shows a report of attendances with diagnoses which are a descendant of the SNOMED CT concept 417746004 |traumatic injury|.
SNOMED CT's rich polyhierarchy provides a vast number of potential 'aggregators' for analytics, and possible views of SNOMED CT encoded data. This polyhierarchy can be exploited by visual exploratory data analysis tools to enable the visual inspection of complex datasets.
For example, the NHS have been using the Gephi open-source network analysis and visualization software, to explore SNOMED CT encoded renal datasets.
The first representation below shows a projection of all concepts directly coded in the patient data, with the node size reflecting the frequency of each code. 36689008 |acute pyelonephritis| has a high frequency in the data and is therefore represented by a big node, while 254915003 |clear cell carcinoma of kidney| has a low frequency in the data and is therefore represented by a small node.
Using a simple concentration algorithm, which aggregates subsumed concepts up to a given threshold, the representation below is achieved. In this representation, the size of the purple nodes reflects the frequency of each code plus its subtypes, the size of the blue nodes reflects the frequency of each code's subtypes, and the size of the red nodes reflects the frequency of each code on its own. This enables trends to be visually detected – for example, 36171008 |glomerulonephritis| and 36171008 |acute pyelonephritis| - even when the frequency of these concepts themselves is relatively low.
Innovative data visualization and analysis tooling is expected to become much more widespread as the powerful capabilities of SNOMED CT content are increasingly utilized.
