Methods and applications for visualization of SNOMED CT concept sets

Summary

Inconsistent use of SNOMED CT concepts may reduce comparability of information in health information systems. Terminology implementation should be approached by common strategies for navigating and selecting proper concepts. This study aims to explore ways of illustrating common pathways and ancestors of particular sets of concepts, to support consistent use of SNOMED CT and also assess potential applications for such visualizations.

The open source prototype presented is an interactive web-based re-implementation of the terminology visualization tool TermViz that provides an overview of concepts and their hierarchical relations. It provides terminological features such as interactively rearranging graphs, fetching more concept nodes, highlighting least common parents and shared pathways in merged graphs etc.

Four teams of three to four people used the prototype to complete a terminology mapping task and then, in focus group interviews, discussed the user experience and potential future tool usage. Potential purposes discussed included SNOMED CT search and training, consistent selection of concepts and content management.

The evaluation indicated that the tool may be useful in many contexts especially if integrated with existing systems, and that the graph layout needs further tuning and development.

Citation: Højen AR, Sundvall E, Gøeg KR. Methods and applications for visualization of SNOMED CT concept sets. Appl Clin Inf 2014; 5: 127–152

http://dx.doi.org/10.4338/ACI-2013-09-RA-0071

Keywords

Clinical terminology - Implementation - SNOMED CT - Information Visualization - knowledge representation

 

Statement on conflicts of interest

None.

• References

• 1 Stroetmann VN. et al. Semantic interoperability for better health and safer healthcare,. 2009 ISSN 978–92–79–11139-6.
• 2 Ryan A, Eklund EA. Framework for semantic interoperability in healthcare: A service oriented architecture based on health informatics standards. MIE ed. IOS Press; 2008
• 3 Goossen WT. Intelligent semantic interoperability: Integrating knowledge, terminology and information models to support stroke care. Studies in Health Technology and Informatics 2006; 122: 435-439.
• 4 Garde S, Knaup P, Hovenga E JS, Heard S. Towards semantic interoperability for electronic health records -domain knowledge governance for open EHR archetypes. Methods of Information in Medicine 2007; 46: 332-343.
• 5 Chute CG. Clinical classification and terminology some history and current observations. Journal of the American Medical Informatics Association 2000; 7: 298-303.
• 6 Elkin PL. et al. Secondary use of clinical data. Studies in Health Technology and Informatics 2010; 155: 14.
• 7 Cimino JJ. Collect once, use many: Enabling the reuse of clinical data through controlled terminologies. JOURNAL-AHIMA 2007; 78: 24.
• 8 Andrews JE. et al. Comparing heterogeneous SNOMED CT coding of clinical research concepts by examining normalized expressions. Journal of Biomedical Informatics 2008; 41: 1062-1069.
• 9 Bos L. SNOMED-CT: The advanced terminology and coding system for eHealth. Medical and Care Compunetics 2006; 121: 279.
• 10 Wade G. Implementing SNOMED CT for quality Rreporting: Avoiding pitfalls. Applied Clinical Informatics 2011; 2: 534.
• 11 Lee D, Lau F, Quan H. A method for encoding clinical datasets with SNOMED CT. BMC Medical Informatics and Decision Making 2010; 10: 53.
• 12 Wade G, Rosenbloom ST. Experiences mapping a legacy interface terminology to SNOMED CT. BMC Medical Informatics and Decision Making 2008; 8.
• 13 Hansen DP. et al. Building SNOMED CT Reference sets for use as interface terminologies. Electronic Journal of Health Informatics 2011; 6: e1.
• 14 Andrews JE, Richesson RL, Krischer J. Variation of SNOMED CT coding of clinical research concepts among coding experts. Journal of the American Medical Informatics Association 2007; 14: 497-506.
• 15 Højen AR, Goeg KR. SNOMED CT Implementation. Mapping guidelines facilitating reuse of data. Methods of Information in Medicine 2012; 51: 529-538.
• 16 Rasmussen RA, Rosenbeck K. SNOMED CT Implementation: Implications of choosing clinical findings or observable entities. 2011
• 17 Rogers J, Bodenreider O. SNOMED CT: Browsing the browsers. KR-MED 2008; 30-36.
• 18 Sundvall E. et al. Interactive visualization and navigation of complex terminology systems, Exemplified by SNOMED CT. Studies in Health Technology and Informatics 2006; 124: 851.
• 19 The Clinical Information Consultancy Ltd.. CliniClue –Clinical terminology services. 27.08.2012 Available from: http://www.cliniclue.com.
• 20 Dataline Software Ltd.. SNOFLAKE browser. Available from: http://www.snoflake.co.uk#.
• 21 Rubin DL, Noy NF, Musen MA. Protege: A tool for managing and using terminology in radiology applications. Journal of Digital Imaging 2007; 20: 34-46.
• 22 Herman I, Melançon G, Marshall MS. Graph visualization and navigation in information visualization: A survey. Visualization and Computer Graphics, IEEE Transactions On 2000; 6: 24-43.
• 23 Sánchez D, Batet M. Semantic similarity estimation in the biomedical domain: An ontology-based information-theoretic perspective. Journal of Biomedical Informatics 2011; 44: 49-759.
• 24 Bostock M, Ogievetsky V, Heer J. D[three.superior] Data-driven documents. Visualization and Computer Graphics, IEEE Transactions On 2011; 17: 2301-2309.
• 25 Data-driven documents.. Available from: http://d3js.org.
• 26 Scalable vector graphics (SVG).. Available from: http://www.w3.org/Graphics/SVG.
• 27 IHTSDO. SNOMED clinical terms. Technical Implementation Guide. January, 2012–07-31.
• 28 Carecom A/S.. Carecom. 2013 Available from: http://www.carecom.dk.
• 29 Kvale S. Doing interviews. SAGE Publications Limited,; 2008
• 30 Barbour R. Doing focus groups. SAGE Publications Limited,; 2008
• 31 Thomas DR. A general inductive approach for analyzing qualitative evaluation data. American Journal of Evaluation 2006; 27: 237-246.
• 32 Falconer S. OntoGraf,. 2010
• 33 Fu B, Noy NF, Storey M. The semantic web–ISWC 2013 Springer, 2013 Indented tree or graph?. A usability study of ontology visualization techniques in the context of class mapping evaluation 117-134.
• 34 Lieberman MI, Ricciardi TN. The use of SNOMED© CT simplifies querying of a clinical data warehouse. American Medical Informatics Association 2003