Data Integration for Clinical Decision Support Based on openEHR Archetypes and HL7 Virtual Medical Record

González-Ferrer, Arturo; Peleg, Mor; Verhees, Bert; Verlinden, Jan-Marc; Marcos, Carlos

doi:10.1007/978-3-642-36438-9_5

Cited by 18 publications

(14 citation statements)

References 13 publications

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“…Part of these criteria were derived from Eichelberg et al [57], Kawamoto et al [58], Garde et al [59], and Iakovidis et al [60]. Aspects related to usability, expressiveness and integration were derived from our experience in designing Clinical Decision Support software that needs to be integrated with PHR systems [13,61]. They concern three main aspects addressing the data representation in the PHR as well as its front-end interface to data and knowledge sources and back-end interfaces to the data representation selected as the logical data level of the PHR 5 :…”

Section: Criteria For Comparisonmentioning

confidence: 99%

Understanding requirements of clinical data standards for developing interoperable knowledge-based DSS: A case study

González-Ferrer

Peleg

2015

Computer Standards & Interfaces

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Section: Criteria For Comparisonmentioning

confidence: 99%

Understanding requirements of clinical data standards for developing interoperable knowledge-based DSS: A case study

González-Ferrer

Peleg

2015

Computer Standards & Interfaces

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“…The three dominant medical informatics foci on which the KR4HC community has been developing special representations are clinical guidelines or clinical pathways (computer-interpretable clinical guidelines (CIG 4 ) [102], electronic health/patient records, and medical domain ontologies). In particular, the topic of representing and reasoning with clinical guidelines is intensively studied (e.g., [37,46,54,61,68,73,83,87]). In some years, for instance 2009, 2014, 2015 and 2017, more than half of the papers had to do with clinical guidelines.…”

Section: Medical Informatics-specific Knowledge Representationsmentioning

confidence: 99%

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Riaño

Peleg

Teije

2019

Artificial Intelligence in Medicine

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Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide. Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future. Methods: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future. Results: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively. Conclusions: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patie...

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“…González-Ferrer and Peleg [15,16] compared vMR, CDA and openEHR by evaluating their support of functional and nonfunctional requirements for interoperability. However, that study was limited since no experts in openEHR were involved and there was no analysis of the modeling process and the resulting representations.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record

et al. 2016

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Objectives. Delivering patient-specific decision-support based on computer-interpretable guidelines (CIGs) Methods. Using qualitative research methods, we analyzed the encoding of ten representative clinical statements taken from two CIGs used in real decision-support systems into two health information models (openEHR archetypes and HL7 vMR instances) by four experienced informaticians.Results. Discussion among the modelers about each case study example greatly increased our understanding of the capabilities of these standards, which we share in this educational paper.Differing in content and structure, the openEHR archetypes were found to contain a greater level of representational detail and structure while the vMR representations took fewer steps to complete.Conclusions. The use of openEHR in the encoding of CIG clinical statements could potentially facilitate applications other than decision-support, including intelligent data analysis and integration of additional properties of data items from existing EHRs. On the other hand, due to their smaller size and fewer details, the use of vMR potentially supports quicker mapping of EHR data into clinical statements.

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Data Integration for Clinical Decision Support Based on openEHR Archetypes and HL7 Virtual Medical Record

Cited by 18 publications

References 13 publications

Understanding requirements of clinical data standards for developing interoperable knowledge-based DSS: A case study

Understanding requirements of clinical data standards for developing interoperable knowledge-based DSS: A case study

Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges

Analysis of the process of representing clinical statements for decision-support applications: a comparison of openEHR archetypes and HL7 virtual medical record

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