Content analysis of physical examination templates in electronic health records using SNOMED CT

Gøeg, Kirstine Rosenbeck; Chen, Rong; Højen, Anne Randorff; Elberg, Pia Britt

doi:10.1016/j.ijmedinf.2014.06.006

Cited by 21 publications

(20 citation statements)

References 21 publications

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“…Medical researchers have made increasing use of computer techniques to analyze large datasets of publications and citations in order to characterize and map the structure of scientific fields and their development over time (Jones et al 2011;Armfield et al 2014;Chen et al 2014a, b;Jiang et al 2015), even to model the dissemination of scientific ideas and identify characteristics of publication patterns that suggest whether an idea or innovation has reached a crucial phase transition (Yao et al 2014a, b;Winnink and Tijssen 2014;Kim et al 2014;Chida et al 2014;Cheng and Zhao 2014;Gøeg et al 2014;Ye et al 2014). Analysis of citation data helps define the field at issue by highlighting various areas of research and its future directions (Small 1981).…”

Section: Introductionmentioning

confidence: 99%

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Fang

2015

Scientometrics

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This article aims to analyze and visualize the structure and the emerging trend of digital medicine, a new medical pattern of twenty-first century. Our study objectively explores the document co-citation clusters of 6060 bibliographic records to identify the origin of digital medicine and the hot research specialty of domain. Pivotal point articles, prominent authors, active disciplines and institution have been identified by network analysis theory. CiteSpace was used to visualize the perspective of digital medicine domain. As an interdiscipline which integrated computer science, information engineering with medicine, digital medicine originally and mainly on digital medical imaging technology research for accuracy and speedy clinical diagnosis and therapy. Of 6060 relevant records reviewed, 1719 (28 %) are on radiology, 902 (15 %) are on engineering, 539 (9 %) are on computer science. The largest co-citation cluster is on digital tomosynthesis. The earliest cluster is on medical imaging segmentation and registration. Post-processing imaging technology, detector, phase contrast, reversible watermarking, input, model 3D reconstruct, real-time dynamic imaging, dosimetry have been the hot research topics. The recently cluster is on internet health information. Harvard University of USA is the prominent institution. The coverage of digital medicine research is widely from clinic to laboratory. Recent year, domain research front is thematically on teleradiology, telemedicine and hospital information management system.

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Section: Introductionmentioning

confidence: 99%

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Fang

2015

Scientometrics

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“…The biomedical meaning of electronic health record (EHR) content is organized using disease terminologies such as SNOMED-CT [35,36]. One avenue of semantically bridging the biomedical meaning of Physiome models with that of EHRs is to develop a map between disease terms and variables in process models.…”

Section: Resultsmentioning

confidence: 99%

Requirements for the formal representation of pathophysiology mechanisms by clinicians

et al. 2016

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Knowledge of multiscale mechanisms in pathophysiology is the bedrock of clinical practice. If quantitative methods, predicting patient-specific behaviour of these pathophysiology mechanisms, are to be brought to bear on clinical decision-making, the Human Physiome community and Clinical community must share a common computational blueprint for pathophysiology mechanisms. A number of obstacles stand in the way of this sharing—not least the technical and operational challenges that must be overcome to ensure that (i) the explicit biological meanings of the Physiome's quantitative methods to represent mechanisms are open to articulation, verification and study by clinicians, and that (ii) clinicians are given the tools and training to explicitly express disease manifestations in direct contribution to modelling. To this end, the Physiome and Clinical communities must co-develop a common computational toolkit, based on this blueprint, to bridge the representation of knowledge of pathophysiology mechanisms (a) that is implicitly depicted in electronic health records and the literature, with (b) that found in mathematical models explicitly describing mechanisms. In particular, this paper makes use of a step-wise description of a specific disease mechanism as a means to elicit the requirements of representing pathophysiological meaning explicitly. The computational blueprint developed from these requirements addresses the Clinical community goals to (i) organize and manage healthcare resources in terms of relevant disease-related knowledge of mechanisms and (ii) train the next generation of physicians in the application of quantitative methods relevant to their research and practice.

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“…The structure of SNOMED CT is used as a resource to enrich the search [46]. In the third study by Gøeg et al, SNOMED CT is used to determine similarities and differences in physical examination templates using both full matches and terminology matches deduced from the structure of SNOMED CT [47]. The contribution of the present study compared to these earlier approaches is that intrinsic similarity estimation is introduced to the field of content analysis which makes semantic similarities quantifiable.…”

Section: Strengths and Weaknesses Compared To Other Studiesmentioning

confidence: 96%

“…In the evaluation, we chose to include 15 templates, which is comparable to the related studies where the sample size is 4 [47], 7 [45], and 25 [46] respectively. We chose the relatively limited number of templates to make the analysis transparent, which in our opinion is important in this methodological oriented study.…”

Section: Strengths and Weaknesses Compared To Other Studiesmentioning

confidence: 99%

Clustering clinical models from local electronic health records based on semantic similarity

Gøeg

Cornet

Andersen

2015

Journal of Biomedical Informatics

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Content analysis of physical examination templates in electronic health records using SNOMED CT

Cited by 21 publications

References 21 publications

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Visualizing the structure and the evolving of digital medicine: a scientometrics review

Requirements for the formal representation of pathophysiology mechanisms by clinicians

Clustering clinical models from local electronic health records based on semantic similarity

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