Health Information modelling language - overview

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This article describes the language used for creating information models used in health records, as well as the means by which health record queries can be defined in a system independent manner.

For those familiar with the semantic web languages, it is safe to assume that the language described herein is simply a dialect (or profile) of the standard W3C recommendations of RDF, RDFS, OWL2, SHACL and SPARQL. The profile is designed to simplify implementations of the model and to constrain it’s features to ensure relevance and optimum performance. The language is designed to support an information model conceived as a graph and thus implementation of the model and related health data are represented logically as a graph.

It should be noted that this is the modelling language, not the physical data schema or actual query. The modelling language uses logical constructs for use by an interpreter to convert to the relevant schema or query. If the actual implementation supports the modelling languages themselves as low level languages (e.g. RDF or SPARQL) then the logical model and the physical model are the same.

Before tackling the language it is worth reading the article on information modelling.

Purpose background and rationale

Question: Yet another language? Surely not.

Answer: No, not at all. What can be stated though is that it is not a health domain language. It is a language used commonly across all sectors across the world, applied to the health and social care domain.

One of the ambitions of the language is to free the representation of health data from the modelling silos within healthcare itself. This is based on the principle that the only difference between health data and other data is the vocabulary and ontologies used.

The language is used to enable the information model to be readable by machines. The language covers all areas of the model and uses different grammars for the different approaches. The different parts of the IM can be listed as follows. This article covers item 8 which models items 1 to 7:

  1. An ontology, which is a vocabulary and definitions of the concepts used in healthcare, or more simply put, a vocabulary of health. The ontology is made up of the world's leading ontology Snomed-CT, with a London extension and supplemented with additional concepts for data modelling.
  2. A data model, which is a set of classes and properties, using the vocabulary, that represent the data and relationships as published by live systems that have published data, Note that this data model is NOT a standard model but a collated set of entities and relationships bound to the concepts based on real data, that are mapped to a single model.
  3. A library of business specific concept and value sets, which are expression constraints on the ontology for the purpose of query
  4. A catalogue of reference data such as geographical areas, organisations and people derived and updated from public resources.
  5. A library of Queries for querying and extracting instance data from reference data or health records.
  6. A set of maps creating mappings between published concepts and the core ontology as well as structural mappings between submitted data and the data model.
  7. An open source set of utilities that can be used to browse, search, or maintain the model.
  8. A modelling language using the World wide web semantic languages that can be used to exchange all elements of the model.

The main purpose of a modelling language is to exchange data and information about information models in a way that both machines and humans can understand. A language must be able to describe the types of structures that make up an information model. Diagrams and pictures are all very well, but they cannot be used by machines.

It is necessary to support both human and machine readability so that a model can be validated both by humans and computers. Humans can read diagrams or text. Machines can read data bits. The two forms can be brought together as a stream of characters forming a language.

A purely human based language would be ambiguous, as all human languages are. A language that is both can be used to promote a shared understanding of often complex structures whilst enabling machines to process data in a consistent way.

It is almost always the case that a very precise machine readable language is hard for humans to follow and that a human understandable language is hard to compute consistently. As a compromise, many languages are presented in a variety of grammars and syntaxes, each targeted at different readers. The language in this article all adopt a multi-grammar approach in line with this dual purpose.

Multi grammars are the norm in computer languages. Different software implementations use different technologies and different grammars are needed. The crucial point is that for systems to interoperate effectively, the different grammars must mean the same thing in the end, they are just presented in different ways with different syntaxes.

Requirements and Contributory languages

Health data can be conceptualised as a graph, and thus the model of health data is a graph model.

The language must be both human and machine readable in text form.

The language must use the recognisable plain language characters in UTF-8. For human readability the characters read from left to right and for machine readability a graph is a character stream from beginning to end.

Two grammars are required; one for human legibility, and the other for optimised machine processing. However, both must be human and machine readable.

The modelling language is an amalgam of the following languages:

  • RDF. An information model can be modelled as a Graph i.e. a set of nodes and edges (nodes and relationships, nodes and properties). Likewise, health data can be modelled as a graph conforming to the information model graph. RDF Forms the statements describing the data. RDF in itself holds no semantics whatsoever. i.e. it is not practical to infer or validate or query based purely on an RDF structure. To use RDF it is necessary to provide semantic definitions for certain predicates and adopt certain conventions. In providing those semantic definitions, the predicates themselves can then be used to semantically define many other things. RDF can be represented using either TURTLE syntax or JSON-LD.
  • RDFS. This is the first of the semantic languages. It is used for the purposes of some of the ontology axioms such as subclasses, domains and ranges as well as the standard annotation properties such as 'label'
  • OWL2 DL. This brings with it more sophisticated description logic such as equivalent classes and existential quantifications and is used in the ontology and for defining things when an open world assumption is required
  • SHACL. Used for everything that defines the shape of data where a closed world assumption is required. Although SHACL is designed for validation of RDF, as SHACL describes what things 'should be' it can be used as a data modelling language
  • SPARQL Used as the logical means of querying model conformant data (not to be confused with the actual query language used which may be SQL)
  • RML, an extension of R2RML, used to map between RDF and other formats such as RDBMS, JSON or CSV.

A model presented in the human legible grammar must be translatable directly to the machine representation without loss of semantics.

Example (OWL2)

Consider a definition of a grandfather, in the first example the grandfather is an equivalent to a person who is male and has children who are people that must have children.

using the turtle language

   owl:EquivalentClass [
              [owl:onProperty :hasGender;
               owl:somValuesFrom :Male],
              [owl:onProperty :hasChild;
               owl:somValuesFrom  [owl:intersectionOf
                                         [owl:onProperty :hasChild;
                                          owl:someValuesFrom :Person] ) ])     

JSON is a popular syntax currently and thus this is used as an alternative.

JSON represents subjects , predicates and objects as object names and values with values being either literals or or objects.

JSON itself has no inherent mechanism of differentiating between different types of entities and therefore JSON-LD is used. In JSON-LD identifiers resolve initially to @id and the use of @context enables prefixed IRIs and aliases.

The above Grandfather can be represented in JSON-LD (context not shown) as follows:

{"@id" : ":Grandfather",
 "owl:EquivalentClass" : [
            {"owl:intersectionOf" :[
                     { "@id": ":Person"},
                     { "owl:onProperty" : ":hasGender",
                       "owl:somValuesFrom": {"@id":":Male"}},
                      { "owl:onProperty" : ":hasChild",
                        "owl:someValuesFrom" : {
                           "owl:intersectionOf": [
                                { "@id":"Person"},
                                {"owl:onProperty" : ":hasChild",
                                  "owl:someValuesFrom" : {"@id":":Person"}}]]}}

Sublanguages and syntaxes

Foundation grammars and syntaxes - RDF, TURTLE and JSON-LD

Discovery language has its own Grammars built on the foundations of the W3C RDF grammars:

  • A terse abbreviated language, TURTLE
  • SPARQL for query
  • JSON-LD representation, which can used by systems that prefer JSON, wish to use standard approaches, and are able to resolve identifiers via the JSON-LD context structure.

Identifiers, aliasing prefixes and context

Concepts are identified and referenced by the use of International resource identifiers (IRIs).

Identifiers are universal and presented in one of the following forms:

  1. Full IRI (International resource identifier) which is the fully resolved identifier encompassed by <>
  2. Abbreviated IRI a Prefix followed by a ":" followed by the local name which is resolved to a full IRI
  3. Aliases. The core language tokens (that are themselves concepts) have aliases for ease of use. For example rdfs:subClassOf is aliased to subClassOf,

There is of course nothing to stop applications using their own aliases and when used with JSON-LD @context may be used to enable the use of aliases.

Data is considered to be linked across the world, which means that IRIs are the main identifiers. However, IRIs can be unwieldy to use and some of the languages such as GRAPH-QL do not use them. Furthermore, when used in JSON, (the main exchange syntax via APIs) they can cause significant bloat. Also, identifiers such as codes or terms have often been created for local use in local single systems and in isolation are ambiguous.

To create linked data from local identifiers or vocabulary, the concept of Context is applied. The main form of context in use are:

  1. PREFIX declaration for IRIs, which enable the use of abbreviated IRIs. This approach is used in OWL, RDF turtle, SHACL and Discovery itself.
  2. VOCABULAR CONTEXT declaration for both IRIs and other tokens. This approach is used in JSON-LD which converts local JSON properties and objects into linked data identifiers via the @context keyword. This enables applications that know their context to use simple identifiers such as aliases.
  3. MAPPING CONTEXT definitions for system level vocabularies. This provides sufficient context to uniquely identify a local code or term by including details such as the health care provider, the system and the table within a system. In essence a specialised class with the various property values making up the context.

Ontology - OWL2 DL

For the purposes of reasoning the semantic ontology axiom and class expression vocabulary uses the tokens and structure from the OWL2 profile OWL EL, which itself is a sublanguage of the OWL2 language

However, in addition some standard OWL2 DL axioms are used in order to provide a means of specifying additional relationships that are of value when defining relationships. The following table lists the main owl types used and example for each. Note that their aliases are used for brevity. Please refer to the OWL2 specification to describe their meanings

Owl construct usage examples
Class An entity that is a class concept e.g. A snomed-ct concept or a general concept
ObjectProperty 'hasSubject' (an observation has a subject that is a patient)
DataProperty 'dateOfBirth' (a patient record has a date of birth attribute
annotationProperty 'description' (a concept has a description)
SubClassOf Patient is a subclass of a Person
Equivalent To Adverse reaction to Atenolol is equivalent to An adverse reaction to a drug AND has causative agent of Atenolol (substance)
Disjoint with Father is disjoint with Mother
Sub property of has responsible practitioner is a subproperty of has responsible agent
Property chain is sibling of'/ 'is parent of' / 'has parent' is a sub property chain of 'is first cousin of'
Inverse property is subject of is inverse of has subject
Transitive property is child of is transitive
Existential quantification Chest pain and

Finding site of - {some} thoracic structure

Object Intersection Chest pain is equivalent to pain of truncal structure AND finding in region of thorax AND finding site of thoracic structure
Individual All chest pain subclasses but not the specific instance of acute chest pain
DataType definition Date time is a restriction on a string with a regex that allows approximate dates
Property domain a property domain of has causative agent is allergic reaction
Property range A property range of has causative agent is a substance

Use of Annotation properties for original codes

Annotation properties are the properties that provide information beyond that needed for reasoning.  They form no part in the ontological reasoning, but without them, the information model would be impossible for most people to understand. Annotation properties can also be used for implementation supporting properties such as release status, version control, authoring dates and times and so on. 

Typical annotation properties are names and descriptions. They are also used as meta data such as a status of a concept or the version of a document.

Many concepts are derived directly from source systems that used them as codes, or even free text.

The concept indicates the source and original code or text (or combination) in the form actually entered into the source system. It should be noted that many systems do not record codes exactly as determined by an official classification or provide codes via mappings from an internal id.  It is the codes or text used from the publishers perspective that  is used as the source.

Thus in many cases, it is convenient to auto generate a code, which is then placed as the value of the “code” property in the concept, together with the scheme. From this, the provenance of the code can be inferred.

Each code must have a scheme. A scheme may be an official scheme or  proprietary scheme or a local scheme related to a particular sub system.

For example, here are some scheme/ code combinations

Scheme Original Code/Text/Context Concept code/ Auto code Meaning
Snomed-CT            47032000         47032000         Primary hydrocephaly
EMIS- Read H33-1   H33-1   Bronchial asthma
EMIS – EMIS EMISNQCO303 EMLOC_EMISNQCO303 Confirmed corona virus infection
Barts/Cerner Event/Order=687309281 BC_687309281 Tested for Coronavirus (misuse of code term in context)
Barts/Cerner Event/Order= 687309281/ResultTxt= SARS-CoV-2 RNA DETECTED BC_dsdsdsdx7 Positive coronavirus result


Note that in the last example, the original code is actually text and has been contextualised as being from the Cerner event table, the order field having a value of 687309281 and the result text having a value of ResultTxt= SARS-CoV-2 RNA DETECTED

Data model - SHACL shapes

The shapes constraint language, as in the semantic ontology, the language borrows the constructs from the W3C standard SHACL, which can also be represented in any of the RDF supporting languages such as TURTLE or JSON-LD.


SHACL for part of Encounter record type data model, note that it is both a class and a shape so it is classified as a subclass of an event which means it inherits the properties of an event (such as effective date), but the super class "has concept" property has a range constrained to a London extension" which is the class of encounter types such as gp consultation.

 a sh:NodeShape , owl:Class;
     rdfs:label "Encounter (record type)" .
     im:isA im:Event ;
     im:status im:Active;
     rdfs:subClassOf im:PatientEvent;
     rdfs:comment "An interaction between a patient (or on behalf of the patient) and a health professional or health provider. It includes consultations as well as care processes such as admission, discharges. It also includes the noting of a filing of a document or report.";
          [sh:path im:additionalPractitioners;
           sh:class im:PractitionerInRole] , 
          [sh:path im:completionStatus;
           sh:class im:894281000252100] , 
          [sh:path im:duration;
           sh:minCount "1"^^xsd:integer;
           sh:class im:894281000252100] , 
          [sh:path im:linkedAppointment;
           sh:class im:Appointment] , 
          [sh:path im:concept;
           sh:maxCount "1"^^xsd:integer;
           sh:minCount "1"^^xsd:integer;
           sh:class im:1741000252102]

Concept Set definitions - RDF/ ECL

In line with expression constraint language used to define sets of concepts, defining a set is a query over the ontology resulting in a set of concepts to use in a subsequent query.

The information model uses RDF predicate to hold the definition the set meta data (e.g. the name of the set, the fact that it is a set etc) and uses SHACL predicates for modelling the boolean logic

Example - complex set

Lets say a commissioner needs to know who the patients are that have had Covid vaccines.

Covid vaccines are recorded either as immunisation records, or medication records, or both.  To query the medication records, a set of vaccine medication concepts are searched for, these being stored in medication order record entries. Covid vaccines change every few weeks as new brands or strengths are released.

A definition of a covid vaccine is helpful, thus a concept set is defined.

<<39330711000001103          # is a Covid vaccine
OR                                            #or (
<<10363601000001109:          # is a uk product
     <<s10362601000001103 = 10362601000001103} }      #has vmp Covd vaccine)

Health query language

Main article Resolution health query language

Describes trhe approach to modelling queries for operation on the information model itself or on health records

Data mapping and matching

This part of the language is used to define mappings between the data model and an actual schema to enable query and filers to automatically cope with the ever extending ontology and data properties. 

The processes involved in mapping and matching concepts are described in the article on mapping of concepts codes and structures

ABAC language

Main article : ABAC Language

The Discovery attribute based access control language is presented as a pragmatic JSON based profile of the XACML language, modified to use the information model query language (SPARQL) to define policy rules. ABAC attributes are defined in the semantic ontology in the same way as all other classes and properties.

The language is used to support some of the data access authorisation processes as described in the specification - Identity, authentication and authorisation .

This article specifies the scope of the language , the grammar and the syntax, together with examples. Whilst presented as a JSON syntax, in line with other components of the information modelling language, the syntax can also be accessed via the ABAC xml schema which includes the baseline Information model XSD schema on the Endeavour GitHub, and example content viewed in the information manager data files folder