wiki:openEHR Models, Archetypes and Biomedical Ontologies

Version 14 (modified by Tatsukawa, Akimichi, 12 years ago) (diff)


openEHR Models, Archetypes and Biomedical Ontologies

この文章は[ openEHR Models, Archetypes and Biomedical Ontologies ]の日本語訳です。内容の正確性は保証しませんので,原文を参照してください。

注意: このページはいずれ openEHR wiki に移動します。それまでは、技術的あるいは臨床的なメーリングリストにて議論を深めてください。

Note: This page will move to the openEHR wiki when it becomes available. For the moment, discssion is encouraged on the technical and clinical lists.



  • 本ページの目的
  • 背景
    • オントロジーとは何か? そして何故それが大切なのか?
    • オントロジーは情報分野において果して意味があるか?
    • 経緯
  • 成功を判断する基準
  • 医学生物学オントロジー
  • openEHRのどの部分がオントロジーと関係するか?
    • openEHR参照モデル
    • openEHRアーキタイプ
    • openEHRにおける語彙
  • 参照
  • Purpose of this page
  • Background
    • What are Ontologies and why do we care?
    • Do Ontologies make sense for Information?
    • Some History
  • Success Criteria
  • Biomedical Ontologies
  • What parts of openEHR have Ontological Relevance?
    • The openEHR Reference Model
    • The openEHR Archetypes
    • Terminologies and openEHR
  • References


Purpose of this page

このページの主な目的は、オントロジーの専門家を交えて openEHR とオントロジーについて幅広い議論をする足掛りとなることにある。議論の目的は、openEHRが本当の意味で推論その他の処理(すなわち意思決定支援や薬物禁忌システム、診療ガイドラインなど)を行なうにふさわしい医療情報のモデルとしての姿を探るためにある。このような現実的な目的を持っている。 The main purpose of this page is start a wider discussion about openEHR and ontology, involving ontology experts. The goal of such a discussion would be to find improvements to openEHR so as to ensure that it really is a model of health information suitable for computerised inferencing and other processing - e.g. decision support, medication interaction analysis and guideline processing. The aim is thus a practical one.


During the history of openEHR its authors learned much about ontology, and made what we believe is a reasonable analysis underlying parts of the openEHR architecture. Now it is time to get people with a lot more experience in this area to have a look at what we have done, and to show us how to improve.

この文書の構造は以下のようになっている。 The structure of this page is as follows:

  • 背景
  • 成功を判断する基準。すなわち、いかにして我々がなしてきたことが成功しているかを知ること
  • 医学生物学とその他の領域のオントロジーの概観とそれらのopenEHR に対する貢献
  • openEHRの持つオントロジーの性質についての概観
  • 統合の試み。すなわち、openEHRとオントロジーの結合を探り、openEHRをよりよいものとする
  • a bit of background
  • success criteria: how will we know if changes we make are good?
  • a summary of Biomedical and other ontology efforts that appear to be relevant to openEHR
  • a summary of the parts of openEHR that are ontological in nature
  • an attempt at synthesis: finding the connections, and particularly, how to make openEHR better.



オントロジーとは何か? そして何故それが大切なのか?

What are Ontologies and why do we care?

オントロジーとは、現実世界の出来事を記述するための形式的な手法である。オントロジーは主に次の2つの目的のために利用される。a)人々や計算機が同じ事実を同じものとして合意する、b) 個々の事実(例えば、患者Aの血圧は慢性的に高いこと)を分類する(患者Aは高血圧患者である)ことで計算機による推論を可能にし、結果としてこの患者の持つ複数の事実を一個のカテゴリー(脳卒中の高リスク群)に分類する。このような推論が可能となる。

Ontologies are formalised ways of describing aspects of the real world. They are used for two main purposes: a) so that multiple people and computers can agree on the same facts and b) so that computerised inferencing can be performed, usually based on classifying individual facts (patient A has chronically raised blood pressure) in categories (hypertensive person) so as to access facts of the category (increased risk of stroke) relevant to the individual. Many other kinds of reasoning can be done.


There is much work going on in ontologies around the world, including in biomedical ontologies. Most of the work is designed with computer-based reasoning on facts e.g. recorded in patient data in mind. One of the challenges of ontological models is that to work, the data on which inferencing is to be done using the ontology must themselves have a meaning consistent with the ontology. In practical terms this means that the information model(s) of the data must be consistent with the ontologies, in other words, if the data record a 'allergy' for a patient, this has the same meaning as 'allergy' does in the ontology. However, this is often not the case due to poorly defined terms; 'allergy' might have been used to mean 'an allergic reaction' or 'a diagnosed allergy.


Ontologies also exist in software, although most software developers have no idea of this, due to the failure so far of mainstream ICT education to take account of semantics within technical models (i.e. 'class', 'object' or E-R models in the programming sense). Nevertheless, everytime any 'modeller' or programmer creates code, a UML model or an information schema, they are creating some kind of ontology, usually of informational concepts. Software models should be understood as ontologies, because they make commitments to certain flavours of the concepts they model - including the base data types (Integer, Boolean etc) of programming languages.


A basic categorisation of ontologies used in the ontology world is upper and domain or specific ontologies. An upper ontology is domain-independent, and extremely general; they are applicable over many domains.

もし今までオントロジーなるものを目にしたことがなければ、John Sowaが定義した上位レベルの分類がその理解に役立つだろう。これは上位オントロジーのよく知られた例である。言うまでもなく、これは医学生物学の領域において間違いなく最も優れたものというわけではないが、それでも非常に参考になる。

If you have never seen an ontology before, you may find John Sowa's top-level categories interesting - this is a well-known example of an upper ontology. Needless to say, this is regarded as by no means the best or most relevant in the biomedical sphere, but it is a useful reference point.


Do Ontologies make sense for Information?


Although at some level all ontologies are 'descriptions of an aspect of reality', for the purposes of this page, we will distinguish between two broad categories of ontology:

  • '事実に対するオントロジー' - 現実に存在する物体や処理や事象に関するオントロジーである。
  • '情報に関するオントロジー' - どんな種類であれ、情報に関するオントロジーである。すなわち、媒体となる手段に関する言明である。このようなオントロジーの基底にある概念は、探索や記録や出力などといった考え方に関係している。
  • 'ontologies of reality' - ontologies whose subject matter is real things, processes or events, rather than information
  • 'ontologies of information' - ontologies whose subject matter is information of any kind - i.e. utterances committed to a medium. Concepts undelying such an ontology are likely to have to do with the process of investigating, recording, reporting or similar ideas.


We draw this distinction because as soon as something s recorded, there is a question of what the recorded form looks like:

  • 記録されたものの実体はどのような型を持つか(すなわち、それはメモであるか、何らかの結果であるか、それとも診断名であろうか)
  • 記録された情報はどのような構造を持つか? 例えば出産のような出来事は明らかに様々な構造を持ちうる。
  • 情報の項目は互いにどのような関係を持つであろうか? この文書では「こちらも参照」とか「詳細はこちら」などは意味が通じるだろうが、現実に報告された情報においてはそうではないだろう。
  • what types of recorded entities are there (e.g. notes, results, diagnoses)
  • what is the structure of the recorded information? Clearly quite different recordings could be made of the same event in reality, such as a childbirth
  • what are the relationships between items of information? Relationships such as 'see also', 'more detail' and so on make sense here, but not between the entities in reality being reported on.


In openEHR we are interested in ontologies of both kinds. Since the EHR is about recorded information, ontologies of information are relavent. However, within recorded information of course we expect to find:

  • 報告された現象に関する構造と意味。例えば、腹部の診察に関する記録は、少なくとも何らかの解剖学的な用語と特徴が記載されるだろうし、それらは我々の持つ解剖学的知見に反してはならず、したがって、解剖学のオントロジーのような医学生物学分野の実体に関するオントロジーと矛盾してはならない。
  • 例えばICDコードやSNOMED用語など、基本的なオントロジーから抽出された概念への参照
  • structuring and semantics that are in some way related to the phenomena being reported on. E.g. a record of an abdominal examination is likely to include at least some anatomical terms and characterisations, which should not violate what we know of anatomy, and therefore, should be compatible with ontologies of biomedical reality such as an anatomy ontology.
  • references to concepts from ontologies of the first kind, e.g. ICDx or SNOMED terms.


Ontologies of the first kind are therefore just as important. In our opinion, it is not yet clear how they inter-relate....


Some History


At the moment we are not trying to provide a comprehensive summary of the work done in the area of health information ontologies, but it is worth mentioning some of the work of ontological significance that has occurred over the years:

  • 理論的なアプローチ
    • 1968: WeedのPOMRが診療情報をSOAP形式で記述することを考案した [ref]
    • 1978: Elsteinが(主に診断において)臨床上の推論に関する仮説的な演繹モデルを記述した [ref]
    • 1992: Rector, Nowlan とKay が 「患者にとって何が語られ、考察され、なされたか」(引用)という情報が電子カルテに記述されなければならないというアプローチを主張した[ref]
    • 1994: EUが資金を提供したGEHR (Good European Health Record)プロジェクトが電子カルテと情報モデルにとっての必要条件を考案した [ref]
    • 2003: TangeらがPOMR, Elsteinと「行動のための会話」という理論を統合した [ref]
  • 現実的なアプローチ:
    • 1998- : オランダのG-EPJ ('EPJ' = 'EHR')プロジェクトは、openEHRとよく似た工程を採用している [ref]
    • 2001-3: オーストリアのGeHR (Good electronic Health Record) プロジェクトは、形式的なアーキタイプを導入するというアプローチを採用した [ref]
    • 2005- : スウェーデンのSambaプロジェクトでは、相互接続のプロセスを臨床、管理、通信の3種類に分類した [ref]
  • Actを基盤としたアプローチ:
    • 1992: RICHE協会は診療サービスを提供するプロセスにおいて行為 act によって診療情報を表現する仕組みを開発した [ref]
    • 1993- : HL7v3 RIM (参照情報モデル)は、診療情報を行為 act によって表現するための現代的なアプローチである [ref]
  • 医学用語: 全ての医学用語は、それがどんな構造をもっているにせよ、何らかのオントロジーといえる。例えば、
    • MeSH
    • ICDコード
    • Read codes
    • LOINC
    • その他多数
  • Theoretical approaches
    • 1968: Weed's POMR defined a problem/SOAP model of clinical information [ref]
    • 1978: Elstein described a hypothetico-deductive model of clinical reasoning (mainly diagnosis) [ref]
    • 1992: Rector, Nowlan and Kay described an approach in which EHR information included (paraphrasing) 'what can be said, thought and done for the patient' [ref]
    • 1994: GEHR (Good European Health Record) an EU-funded project that developed requirements for an EHR and an information model [ref]
    • 2003: Tange et al proposed a synthesis of the POMR, Elstein and 'conversation for action;' theory [ref]
  • Practical approaches:
    • 1998- : the Danish G-EPJ ('EPJ' = 'EHR'), which described a cycle very similar to the one used in openEHR [ref]
    • 2001-3: the Australian GeHR (Good electronic Health Record) project, an approach that introduced formal 'archetypes' [ref]
    • 2005- : the Swedish Samba project distinguished 3 kinds of interlinked process: clinical, management and communication [ref]
  • Act-based approaches:
    • 1992: RICHE consortium devised a method of representing health information in terms of acts carried out in the care delivery process [ref]
    • 1993- : The HL7v3 RIM (reference information model) is a current approach that attempts to represent health information as acts. [ref]
  • Medical terminologies: all medical terminologies with any structure whatever are ontologies of some kind, whether they think they are or not, including:
    • MeSH
    • ICDx
    • Read codes
    • LOINC
    • and many others


Success Criteria


If we are to take an ontological analysis of openEHR seriously, we need to establish success criteria. These might include:

  • openEHRのリポジトリに対してテストを定義する。それはオントロジーに基づいてアーキタイプか参照モデルを解析し、その正しさを検証したり間違いを検出するテストである
  • 設計段階でのテストを定義する。それはアーキタイプに対して実行され、問題点を指摘するテストである。おそらく OWL/protege環境が利用されるであろう。
  • defining tests to run on an openEHR repository that would prove correctness or show errors in the underlying ontological approach of the reference model or the archteypes.
  • defining design-time tests to be run on archetypes that would show up problems; this might be done using an OWL / protege environment.


Biomedical Ontologies


Relevant biomedical ontology resources to be investigated with respect to the openEHR appears to include the following.

  • NCBO (National Centre for Biomedical Ontology) OCI - 臨床研究のためのオントロジー [home page]; [visual schematic];
  • OBO - Open Biomedical Ontologies [home page]; OBO Foundry (ここに実際のオントロジーがある); ここにある多くのオントロジーは「事実」を対象としたものであるが、以下のオントロジーは「情報」を対象としている:
    • Ontology for biomedical investigations (OBI)
    • Evidence codes
  • Basic Formal Ontology (BFO) [home page] (「現実」を対象としたオントロジーである), 以下のオントロジーと協調する:
    • SNAP, 実在、トロープ(その性質と機能)、空間に関するオントロジーである
  • SPAN, 過程、時間的ならびに時空に関するオントロジーである
  • Grenon、Smith、Goldbergらによる論文"Biodynamic Ontology: Applying BFO in the Biomedical Domain"は医学生物学領域におけるよい導入になっている [ref]
  • The NCBO (National Centre for Biomedical Ontology) OCI - the ontology of clinical investigation [home page]; [visual schematic];
  • The OBO - Open Biomedical Ontologies [home page]; the OBO Foundry (where the actual ontologies are); most of these appear to be 'ontologies of reality', although the following ones seem to be about information:
    • Ontology for biomedical investigations (OBI)
    • Evidence codes
  • The Basic Formal Ontology (BFO) [home page] (an ontology of reality), incorporating:
    • SNAP, an ontology of substantial entities, tropes (their qualities and functions) and spatial regions
    • SPAN, an ontology of process, temporal and spatio-temporal regions
    • The paper "Biodynamic Ontology: Applying BFO in the Biomedical Domain" by Grenon, Smith and Goldberg is a good introduction to BFO in the biomedical domain. [ref]


What parts of openEHR have Ontological Relevance?

openEHRの環境では、オントロジーの観点から独立していると考えられる3つ実体がある。それは、参照モデル、アーキタイプ、語彙(openEHRの内部で定義された語彙のみならず、ICDコード, ICPC, LOINC and SNOMED CTなどの外部で定義されたものも含まれる)

Within the openEHR environment there are 3 entities that can be considered in an ontological way: the reference model, the archetypes, and terminology (both the internal openEHR vocabularies and well-known external terminologies such as ICDx, ICPC, LOINC and SNOMED CT).


The openEHR Reference Model


The openEHR Reference Model defines many classes, but the part of the model of most ontological interest is the 'Entry' part, which is formally specified in the openEHR EHR Information Model; it can also be seen online as detailed UML. A summarised UML form of the Entry types in openEHR is illustrated below.

6番目の型は GENERIC_ENTRYと呼ばれ、旧式のシステムとCEN EN13606, HL7 CDAなどの統合のための構造とをマッピングし、またメッセージと関係データベースとの対応付けを行なう。この型のUMLモデルはこちらにある。すなわち、openEHR の統合IMに文書がある。これらの型はみな極めて汎用的であり、アーキタイプを用いて特定の対象領域に関するモデルを定義する。例えば、アーキタイプのmindmapを参照のこと。

The 6th type is called GENERIC_ENTRY, and is designed for mapping into and out of legacy and integration structures such as CEN EN13606, HL7 CDA, message and relational databases. The UML model of this type is here; it is documented in the openEHR Integration IM. All of these types are extremely generic and archetypes are used to define the specific business/domain content models under each of these types - see archetype mindmap for examples.

近々開催される MedInfo? 2007 で発表する論文(要望があれば、コピーが入手可能である)に記されたように、openEHRのEntryモデルはオントロジーにもとづいた設計になっている。この設計は以下に示す3つの図式に要約されている。1番目の図式はプロセスをモデル化したもの(2つの方法で示されている)である。2番目の図式がプロセスによって情報が生成されるサイクルを示しており、3番目の図式は、診療情報を記録するためにopenEHRにおいて開発された情報に関するオントロジーである。

The openEHR Entry model has an ontological design which is described in a forthcoming MedInfo? 2007 paper (in-press copies are available on request). We can summarise the approach with the following three diagrams. The first is a model of process (shown in two ways); the second shows a cycle of information creation due to the process, and the last shows the information ontology developed in openEHR for recorded clinical information.

openEHRにおけるEntryモデルの基盤となっているオントロジーについては、近々発表される Beale と Heard の論文に説明されている。以下の3つの図式は、現時点におけるopenEHRのEntryモデルを支えるオントロジーを要約している。最初の図式はプロセスのモデルであり、次の図式はこのプロセスによって生成された情報のモデルであり、3番目の図式は情報についてのオントロジーを示している。

The explanation of the ontology behind the openEHR Entry model is in a forthcoming paper by Beale and Heard [ref]. The following 3 diagrams show in a summarised way the ontological underpinning of the openEHR Entry model the way it is today. The first is a model of process, the second a model of information creation due to this process, and the third an ontology of information.

We model health care delivery as two kinds of process: a clinical process, corresponding to the interaction between a ‘clinical investigator system’ and a ‘patient system’, situated within a business process, which is owned by an ‘administrative context’. The clinical process constitutes a sub-process of the business process, i.e. it is the main mechanism for the business process to achieve its goal, which is to satisfy a demand for care on the part of the patient. The administrative context corresponds to the health system as a whole, rather than a single enterprise, since from the patient care point of view, the mobilisation of care delivery is carried out by a network of provider organisations. The model can be illustrated in two equivalent ways, as shown in Figure 3 more....

openEHR Entry process

The terms ‘observation’, ‘evaluation’ etc defined above are not themselves the same as information types, since they refer to a variety of phenomena within the process: information from observations, the activity of evaluation, acts of intervention, and goal statements. To be more precise, we are mainly interested in information created by the investigator system, since this notional agent encompasses any person or device who/which performs any healthcare related task, including the patient herself. The investigator system is therefore the creator of all clinical information in the health record, including patient-entered data. A small amount of administrative information may also end up in the EHR, generally created by non-clinical actors in the organisational context. We can redraw the investigator system in order to more clearly show the types of information created during the care process, as shown in Figure 4. Five types of information are identified, as follows:

  • observation: information created by an act of observation, measurement, questioning, or testing of the patient or related substance (tissue, urine etc), including by the patient himself (e.g. taking own blood glucose measurement), in short, the entire stream of information captured by the investigator, used to characterise the patient system;
  • opinion: thoughts of the investigator about what the observations mean, and what to do about them, created during the evaluation activity, including all diagnoses, assessments, speculative plans, goals;
  • instruction: opinion-based instructions sufficiently detailed so as to be directly executable by investigator agents (people or machines), in order to effect a desired intervention (including obtaining a sample for further investigation, as in a biopsy);
  • action: a record of intervention actions that have occurred, due to instructions or otherwise;
  • administrative event: a record of a business event occurring within the administrative context, such as admission, booking, referral, discharge etc.

openEHR Entry ontology The Clinical Investigator Record (CIR) ontology: An ontology of types of recorded health information. This ontology is probabaly the main starting point for analysing the ontological qualities of openEHR.

Of the biomedical ontologies mentioned above, the OBI and the OCI appear to be closest in purpose. No proper study of these has yet been made with respect to openEHR.

[top] The openEHR Archetypes

In the openEHR approach, most description of the contents of recorded health information is left to archetypes (openEHR FAQ). An archetype can be thought of as a model of some clinical content (e.g. what is recorded in a urinalysis, or an ante-natal visit), expressed in a constraint formalism known as ADL (which has some similarities to OWL). Over 200 archetypes have been defined during NHS projects, Australian GP projects, and openEHR activities (openEHR archetypes page). To go straight to the point, an ontological way of looking at the archetypes that exist is the mindmap view. The structure of each archetype can be viewed by clicking on a node in this view. Another way to view archetypes is with the ADL workbench tool, and with various archetype editors. Example archetypes: Microbial lab observations; Adverse reaction; Examination of named body part.

There are at least two ontological questions with respect to archetypes:

  • what should the ontology of archetypes look like (i.e. what should the mindmap look like)?
  • what are the correct ontological ways of expressing the structures in an archetype, to ensure correct functioning of the information?

The most important thing we can say with respect to archetypes and ontology is probably this: archetypes are not descriptions of real things like biochemical or anatomical phenomena in the textbook sense (e.g. lik a description of how the heart functions in a physiology book); instead they are recordings of something of interest (observation, evaluation etc) during the clinical process, according to the health professional and following typical clinical approaches. In practical terms then, archetypes are practical in their intent, and only capture what health professionals think they need to record.

Of the work mentioned above, the BFO / SNAP / SPAN ontologies may be a good starting point.

[top] Terminologies and openEHR

To be continued

[top] References

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  3. HL7 International. Reference Information Model (RIM). See
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  6. Tange HJ, Dietz JLG, Hasman A, de Vries Robbi PF. A Generic Model of Clinical Practice - A Common View of Individual and Collaborative Care. Methods of Information in Medicine 3/2003. Schattauer GmbH. [complete article]
  7. Bruun-Rasmussen M, Bernstein K, Vingtoft S, Nxhr C, Andersen SK. Quality labelling and certification of Electronic Health Record Systems. Studies in Health Technology and Informatics 2005; 116: p47-52. [pubmed]
  8. Areblad M, Fogelberg M, Karlsson D, Ehlfeldt H. SAMBA - Structured Architecture for Medical Business Activities. In: Engelbrecht R, et al. (editors) Connecting Medical Informatics and Bio-Informatics. MIE 2005: Proceedings of Medical Informatics Europe; 2005 Aug 28-31; Geneva, Switzerland. p. 1225-30.
  9. Beale T, Heard S. The GEHR Object Model - Technical Requirements. 2000. [complete document].
  1. RICHE Consortium. RICHE ESPRIT Project. Final Report. Nov 30 1992.
  2. Ingram D, Lloyd D, Kalra D, Beale T, Heard S, Grubb, P, Dixon R, Camplin D, Ellis J, Maskens A. Deliverable 19,20,24: GEHR Architecture. GEHR Project 30/6/1995. [complete document].
  3. Pierre GRENON, Barry SMITH and Louis GOLDBERG. Biodynamic Ontology: Applying BFO in the Biomedical Domain. From D. M. Pisanelli (ed.), Ontologies in Medicine, Amsterdam: IOS Press, 2004, 20b38. [complete article]
  4. Thomas Beale, Sam Heard. An Ontology-based Model of Clinical Information. Proceedings MedInfo? 2007 (TBA)

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