Anatomy of an archetype

With this blog I want to establish a simple baseline statement or overview about openEHR archetypes, aggregated from other posts and publications – a reference point if you like – from which we can journey further and in more detail into the issues around clinical modelling using specific archetypes.

Archetypes are a strong basis for data liquidity. Create the archetype once, agree through peer-review, re-use when and where required – the foundation for a ‘universal health record’.

Definition:

Formal Definition

An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on a reference (information) model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. [Archetype Definitions and Principles]

Clinician’s ‘lay’ definition:

An archetype is a structured, computable specification for one single, discrete clinical concept. – much simpler!

Each archetype is a rich health data specification, defined and agreed by the clinicians themselves to ensure that each model is ‘fit for clinical purpose’. Collectively these archetypes together can create an electronic health record ‘lingua franca’, or an example of PCAST report‘s ‘Universal Exchange Language (although archetypes are not limited only to exchange of health information).

Archetypes can be used to model a range of clinical concepts:

Purpose

Archetypes are:

  • Potential ‘agents for change’ – allowing knowledge-driven EHRs as an orthogonal approach to EHR development.
  • The basis for a coherent and consistent approach for the whole continuum of clinical care and related activities:
    • Recording, storing and querying health information in electronic health information;
    • Exchanging health information – the broader the agreement & governance, the greater the potential for interoperability;
    • Data aggregation;
    • Knowledge-based activities; and
    • Comparative data analysis.

Using archetypes requires:

  • A change of mindset – no longer silos of data; no longer message and document driven.
  • Upfront planning & coordination in development of archetypes, but not necessarily more time
  • CLINICIAN LEADERSHIP & ENGAGEMENT
    • To ensure the quality of clinical data in our EHRs
    • To warrant the clinical data is safe & ‘fit for purpose’

Design

Each archetype is designed:

  • As a maximal data set effectively everything one can think of about a clinical concept in all situations by the consumer and any and all providers. This design intent is pragmatically constrained to be sensible in some situations, however good archetype design will always ensure that the full model can be achieved through nesting of additional archetypes to create the maximal dataset/universal use-case ideal.
  • For the ‘universal use-case‘ – for re-use in any and all scenarios, from use at home, to primary care, community care, hospital care, secondary use and for research.

Each archetype needs to represent the variety of data that is required to represent the information required for all aspects of clinical care and related activities, including:

  • The ability to capture both free-text vs structured data;
  • Normal statements such as “Nil significant” or “NAD”;
  • Graphable data;
  • Images – eg a homunculus, or a surgical diagram;
  • Multimedia including photos, video and audio or an ECG wave format;
  • Questionnaires, checklists etc

Think about how the clinician may record the data: different clinicians will need differing approaches to recording their health information and the models need to allow for this clinical diversity. Some will prefer to use more free text and others more structure; some need more detail than others; we need to capture current requirements for recording and exchange while keeping in mind what might be best practice in the near and distant future.

The best designers, without a doubt, have a clinical background. I have seen clinicians and non-clinicians given identical clinical content, but the archetypes produces are surprisingly different. Clinician modellers definitely model their domain better than those who are not familiar with clinical practice. They are perhaps better able to envision the fractal nature of medicine and take that in to account in creating archetypes, especially the complex areas which require nesting of archetypes within each other to capture the depth and richness required in the models.

Integration with terminologies such as SNOMED, ICD or LOINC are strategic and important – the structure of an archetype is not enough to represent clinical information adequately, and similarly terminology alone is inadequate. However the combination of terminology within an archetype structure, by naming elements or provision of value sets, is semantically very powerful. There is still considerable work to be done to determine how to represent the information overlap – that which can be expressed either in the archetype structure itself or via terminology. This remains a ‘grey zone’ and is a pressing area for collaboration or research.

Archetype classes – support clinician’s processes

There are four main categories of archetypes that are useful to understand – each corresponding to classes in the openEHR Reference Model.

1.  Compositions – which correspond to commonly used clinical documents, such as ‘antenatal visit’ or ‘care plan’.
2.  Sections – these are effectively used to assist with human navigation within EHRs and correspond to document headings, for example ‘antenatal examination’ or ‘summary’.

3.  Entries – these are the most common and are fundamental building blocks of EHRs. There are four main types of Entry archetypes:

  • Observations – recording measurable or observable data e.g. blood pressure, symptoms or weight;
  • Evaluations – recording clinically interpreted findings e.g. adverse event, diagnosis or assessment of risk;
  • Instructions – recording the initiation of a workflow process, such as a medication order or referral;
  • Actions – recording clinical activities e.g. procedure or medication administration. Actions complement the instruction and can record the ensuing state of the instruction, such as ‘completed’ or ‘cancelled’.

4. Clusters

Courtesy: Thomas Beale, Sam Heard: Ocean Informatics

Lifecycle & Governance

The Clinical Knowledge Manager (CKM) – www.openEHR.org/knowledge – is an international, online clinical knowledge repository under the auspice of the openEHR Foundation that provides:

  • Access to a library of cohesive archetypes and related knowledge artefacts;
  • Peer review, life cycle management & publication process via a Web 2.0 approach for clinical content, terminology binding, terminology reference set binding and translations;
  • Clinical Knowledge governance underpinned by a digital asset management system and providing:
    • Provenance, audit trails, validation checking
    • Release sets for implementation

Archetypes are freely available under a Creative Commons license.

Use

The need for EHRs to be able to represent clinical diversity is absolutely underrated. Existing EHR systems corral clinicians into certain types of recording etc, but for safety we need more diverse ways for clinicians to record what they need for patient care, and also to ensure that what is exchanged is appropriate for the patient and their individual circumstances. A ‘one size fits all’ document approach to emergency summaries or messages can be potentially unsafe. Elderly, pregnancy, chronic disease, paediatrics are extremely common examples where there are specific additional requirements needed to summary data sets

Templates are computable specifications for a specific clinical scenario or use-case. The openEHR paradigm takes the governed and agreed archetypes from CKM and creates clinically useful specifications that can be implemented in systems by:

  • Aggregating the necessary archetypes together; and
  • Constraining the maximal dataset archetypes so that only relevant data elements are active.

Examples include all clinical content required for a specific Message, Document, Clinical consultation or Report e.g. a histopathology report, a referral, an antenatal visit or a discharge summary.

In this way we can, in colloquial terms, ‘have our cake and eat it too’. At the same time, CKM ensures tight clinical knowledge governance, yet templates allow for expression of clinician diversity.

Benefits

Archetypes provide a ‘glide path’ to knowledge-level interoperability of health information. They can:

  • Bootstrap new application development – a source of agreed clinical content, avoid re-inventing the wheel for each vendor, organisation or project;
  • Provide a forward ‘roadmap’ for existing applications – supporting gradual transition from proprietary silos to common data representations, perhaps via mapping to common messages as an interim step;
  • Support data integration – the means to migrate valuable silos of legacy data to a common, re-usable representation
  • Enable data aggregation & comparative analysis – the basis for ensuring ‘apples’ are ‘apples’, not ‘oranges’;
  • Simplify messaging – keep message content consistent with EHR content;
  • Enable knowledge-based activities eg Clinical Decision Support Systems – consistent & coherent; create once, re-use in all systems.

2 thoughts on “Anatomy of an archetype

  1. Pingback: ICMCC News Page » Anatomy of an archetype

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