Giovanni Delussu, Francesca Frexia, Cecilia Mascia, Alessandro Sulis, Vittorio Meloni, Mauro Del Rio, Luca Lianas
{"title":"openEHR 临床数据存储库调查","authors":"Giovanni Delussu, Francesca Frexia, Cecilia Mascia, Alessandro Sulis, Vittorio Meloni, Mauro Del Rio, Luca Lianas","doi":"10.1016/j.ijmedinf.2024.105591","DOIUrl":null,"url":null,"abstract":"<div><p></p><ul><li><span>•</span><span><p>Background: Clinical Data Repositories (CDRs) lie at the core of both primary and secondary utilisation of vast amounts of health information. These data are intricate, heterogeneous and constantly evolving alongside advancements in biomedical sciences. The separation between clinical content and its persistence renders the archetype-based paradigm naturally well-suited to manage this complexity. This approach is adopted by a number of open source and commercial CDR solutions, mostly implementing the openEHR specifications, which also encompass the Archetype Query Language (AQL) to define portable queries independently of the persistence scheme.</p></span></li><li><span>•</span><span><p>Aim: To provide a wide knowledge base and a set of customisable tools as a support in the selection of openEHR CDRs according to a broad ensemble of features relevant to different use cases.</p></span></li><li><span>•</span><span><p>Approach: After conducting an extensive search of the existing openEHR CDRs, a survey consisting of fifty-four questions was administered to all the nineteen identified vendors/developers, covering an ample set of aspects such as licensing, implementation, interoperability and ecosystem. Subsequently, the answers from the eleven responders were processed and analysed, also applying statistical techniques.</p></span></li><li><span>•</span><span><p>Results: Two detailed tables depict the current landscape of openEHR CDRs, presenting a structured view of the most relevant survey answers. Unsupervised clustering led to the categorisation of CDRs into four groups, and a decision-making diagram has been designed to aid the CDR selection according to a restricted set of desired features.</p></span></li><li><span>•</span><span><p>Conclusions: Compared to a similar study conducted in 2013, the results indicate a worldwide rise in the number of openEHR CDRs, marked by a wider adoption of the dedicated query language, to leverage AQL universality across openEHR platforms. The evolution in the last ten years also included an increased attention to exchange data with non-openEHR solutions and to simplify the content creation from clinical models based on Archetypes and Templates. Materials and analytical tools hereby presented are publicly available for further reuse.</p></span></li></ul></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A survey of openEHR Clinical Data Repositories\",\"authors\":\"Giovanni Delussu, Francesca Frexia, Cecilia Mascia, Alessandro Sulis, Vittorio Meloni, Mauro Del Rio, Luca Lianas\",\"doi\":\"10.1016/j.ijmedinf.2024.105591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p></p><ul><li><span>•</span><span><p>Background: Clinical Data Repositories (CDRs) lie at the core of both primary and secondary utilisation of vast amounts of health information. These data are intricate, heterogeneous and constantly evolving alongside advancements in biomedical sciences. The separation between clinical content and its persistence renders the archetype-based paradigm naturally well-suited to manage this complexity. This approach is adopted by a number of open source and commercial CDR solutions, mostly implementing the openEHR specifications, which also encompass the Archetype Query Language (AQL) to define portable queries independently of the persistence scheme.</p></span></li><li><span>•</span><span><p>Aim: To provide a wide knowledge base and a set of customisable tools as a support in the selection of openEHR CDRs according to a broad ensemble of features relevant to different use cases.</p></span></li><li><span>•</span><span><p>Approach: After conducting an extensive search of the existing openEHR CDRs, a survey consisting of fifty-four questions was administered to all the nineteen identified vendors/developers, covering an ample set of aspects such as licensing, implementation, interoperability and ecosystem. Subsequently, the answers from the eleven responders were processed and analysed, also applying statistical techniques.</p></span></li><li><span>•</span><span><p>Results: Two detailed tables depict the current landscape of openEHR CDRs, presenting a structured view of the most relevant survey answers. Unsupervised clustering led to the categorisation of CDRs into four groups, and a decision-making diagram has been designed to aid the CDR selection according to a restricted set of desired features.</p></span></li><li><span>•</span><span><p>Conclusions: Compared to a similar study conducted in 2013, the results indicate a worldwide rise in the number of openEHR CDRs, marked by a wider adoption of the dedicated query language, to leverage AQL universality across openEHR platforms. The evolution in the last ten years also included an increased attention to exchange data with non-openEHR solutions and to simplify the content creation from clinical models based on Archetypes and Templates. Materials and analytical tools hereby presented are publicly available for further reuse.</p></span></li></ul></div>\",\"PeriodicalId\":54950,\"journal\":{\"name\":\"International Journal of Medical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386505624002545\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386505624002545","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Background: Clinical Data Repositories (CDRs) lie at the core of both primary and secondary utilisation of vast amounts of health information. These data are intricate, heterogeneous and constantly evolving alongside advancements in biomedical sciences. The separation between clinical content and its persistence renders the archetype-based paradigm naturally well-suited to manage this complexity. This approach is adopted by a number of open source and commercial CDR solutions, mostly implementing the openEHR specifications, which also encompass the Archetype Query Language (AQL) to define portable queries independently of the persistence scheme.
•
Aim: To provide a wide knowledge base and a set of customisable tools as a support in the selection of openEHR CDRs according to a broad ensemble of features relevant to different use cases.
•
Approach: After conducting an extensive search of the existing openEHR CDRs, a survey consisting of fifty-four questions was administered to all the nineteen identified vendors/developers, covering an ample set of aspects such as licensing, implementation, interoperability and ecosystem. Subsequently, the answers from the eleven responders were processed and analysed, also applying statistical techniques.
•
Results: Two detailed tables depict the current landscape of openEHR CDRs, presenting a structured view of the most relevant survey answers. Unsupervised clustering led to the categorisation of CDRs into four groups, and a decision-making diagram has been designed to aid the CDR selection according to a restricted set of desired features.
•
Conclusions: Compared to a similar study conducted in 2013, the results indicate a worldwide rise in the number of openEHR CDRs, marked by a wider adoption of the dedicated query language, to leverage AQL universality across openEHR platforms. The evolution in the last ten years also included an increased attention to exchange data with non-openEHR solutions and to simplify the content creation from clinical models based on Archetypes and Templates. Materials and analytical tools hereby presented are publicly available for further reuse.
期刊介绍：
International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings.
The scope of journal covers:
Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.;
Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc.
Educational computer based programs pertaining to medical informatics or medicine in general;
Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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