Mapping and matching concepts

Managing codes and taxonomies

Main article Management of code based taxonomies. Describes how codes such as EMIS local codes, Read 2, OPCS4 etc are handled in the information model applying the maps as described in this article.

Concepts - background

Information consists of ideas. Another word for an idea is a 'concept' . A concept may be named,( in which case the meaning of the concept can usually be understood), or they may be an unnamed expression, which is made up of a set of interrelated named or unnamed concepts.

For example the term "chest pain" implies the idea of a pain in the chest. In Snomed-CT it is a named concept. "Chest pain, worsened by exercise" may be an example of an expression style concept made up from the concept of "chest pain", and the statement that it is "made worse by -> exercise". In this case “made worse by” and “exercise” are both different concepts but no author has yet created a single named concept for this expression.

The new generation of health record management systems tend towards the recording of concepts, with the objective being for the record entry to closely match the idea behind the entry. These types of concepts can be called term based concepts as the term is the thing that describes the idea.

A modern term based concept is defined in relation to other concepts by a set of assertions indicating whether the concept is equivalent to, or a subtype of, a set of other concepts. The standard approach to this is via the use of Description Logic (DL). By using DL, a computer can automatically classify a concept which can result in a computer deducing additional knowledge over and above the human who created the concept. Snomed-CT is the worlds largest ontology of healthcare term based concepts and is authored using DL. A collection of concepts defined in this way constitute an "Ontology" and there is a standard language OWL that is used to represent the definitions.

The idea of codes originated from a different starting point. The intention of a coded entry is to pre-classify an entry before it is recorded. The code is designed for a particular set of business processes e.g. analytics or payment and it is important to understand the context in which a code has been used. A coded concept, being pre-classified, relies on categorisation of the codes, and that classification may or may not imply that one code is a subtype of another. Nothing can be inferred from a code other than its relation to another code as authored. Consequently, as the philosophy is different, code based concepts have to be dealt with differently from term based concepts, even if they seem to saying the same thing.

Because of their history, it is not always possible to assert the exact meaning of a code. However, it is often the case that meaning can be inferred or approximated from a coded entry. With preference to move to an ontology, this inference can be achieved via the use of a mapping process that matches coded concepts to term based concepts that are identified from a code.

There are two strategies to link codes to concepts.

1. A coded term may be stated confidently to be the same as, or a variation on, a concept. Typically code systems like Read2 or CTV3 can be dealt with in this way because they are designed to try and capture the idea in the clinicians mind, and they have been incorporated as concepts anyway. Likewise many system supplier codes have been created in this way. In this case the term code can be said to be a term code of the concept. Read2 G33 - Angina pectoris is a term code for the concept of angina pectoris.

2. A coded term might be the same term as a concept but may have been entered without the assertion that is a true representation of a state. Typically code systems such as ICD10 and OPCS fall into this category. E11 - Diabetes type 2, seems to be the same as the concept of diabetes type 2, but was entered without clinician attestation and may have been approximated for payment purposes. In this case a legacy concept is produced and a map between this concept and the similar clinical concept is generated.

A map is just another form of relationship, but unlike an ontological equivalent or subclass axiom it implies that the relationship is an approximation. It is a sort of statement that something is possibly or probably similar to something else and thus has much less weight than an asserted relationship.

Legacy Code based concepts can be mapped to Core concepts , and this enables the use of the vast volumes of data already recorded in systems. Maps must be used with care as it is almost always the case that the use of a mapped code in a query is dependent on the purpose of the query. This means that mappings are more of a guide to the things to include rather than a confident statement of meaning. When querying records the query author may need to determine which codes to include or exclude on a case by case basis.

Code relationships to term based concepts

As mentioned above the relationships are managed as mappings which state the type or degree of match.

Maps generally fall into 4 patterns. These are illustrated in the context of code based concepts as follows:

Simple match

A core concept may be matched to many code based concepts. In a simple match the legacy concept is deemed to be probably equivalent to, or a subclass of. the code concept

sn:194828000 |Angina (disorder)
    :matchedTo emis:G33 |Angina Pectoris|.

Complex optional match

A concept may be matched to a number of alternative concepts and it is expected that a query author may wish to select these.

In this example, the concept : "Ketoacidotic coma due to diabetes mellitus (disorder)" has a complex map which is selection of either

a) Coma unspecified

and

b) one of either Diabetes mellitus in pregnancy: Pre-existing diabetes mellitus, unspecified, or Diabetes mellitus in pregnancy, unspecified, or Diabetes mellitus arising in pregnancy

In effect meaning that the compound entry in the record would need to have 2 icd 10 codes to fulfill the criteria.

sn:26298008
  :hasMap [
       :combinationOf  [ 
                           :oneOf  icd10:R402 ] 
                       [
                           :oneOf  icd10:O24.3 icd10:O24.9 O24.4]

Source resources properties and local codes

In the above examples, coded concepts were considered as context independent in the sense that the same code used by many providers and many systems would generally mean the same thing and can be treated the same way.

It is equally common to find provider and system specific constructs, including coded items whose meaning depends on the table or field within the source system. A similar approach to mapping of standard code schemes can be taken except that the source properties of the source concept must be explicitly described in order to provide context.

In the same way that codes can be mapped, so can source resource types such as tables or fields, message types or message segments. Mapping may involve functional transformation

Defining source context

The first step in managing source concepts is to define the concept in the context of the originator of the data. This employs the use of a context object usually sent as parameters through the REST API. For example the following

{
  "organisation": "Barts",
  "system" : "CernerMillenium",
  "message" :"cds_type_130",
  "field" :"admin_cat_code"
}

Mapping nodes

A second step is for the mapping author to identify whether the source context is equivalent to another source contexts. This is done in order to rationalise the number of mappings steps needed between a source concept and the final target concept.

Matching to concept

The third step involves creation of a source concept and a core concept. In the above example the source is concept has been mapped to a core concept and the core concept is returned

bc:BC_xyz
  rdfs:label "admin_cat_code"

and potential core map

im:administrative_category_code_on_admission
           :matchedTo 
           bc:BC_xysdasdasd.

  

The information model has fully defined the administrative category property as a property of a subclass of encounter dealing with hospital stays. Consequently the source system's table and field can be fully mapped to the common model field.