Advertisement

Journal of Medical Systems

, 38:75 | Cite as

Integration of an OWL-DL Knowledge Base with an EHR Prototype and Providing Customized Information

  • Xia JingEmail author
  • Stephen Kay
  • Tom Marley
  • Nicholas R. Hardiker
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Systems-Level Quality Improvement

Abstract

When clinicians use electronic health record (EHR) systems, their ability to obtain general knowledge is often an important contribution to their ability to make more informed decisions. In this paper we describe a method by which an external, formal representation of clinical and molecular genetic knowledge can be integrated into an EHR such that customized knowledge can be delivered to clinicians in a context-appropriate manner.

Web Ontology Language-Description Logic (OWL-DL) is a formal knowledge representation language that is widely used for creating, organizing and managing biomedical knowledge through the use of explicit definitions, consistent structure and a computer-processable format, particularly in biomedical fields. In this paper we describe: 1) integration of an OWL-DL knowledge base with a standards-based EHR prototype, 2) presentation of customized information from the knowledge base via the EHR interface, and 3) lessons learned via the process. The integration was achieved through a combination of manual and automatic methods. Our method has advantages for scaling up to and maintaining knowledge bases of any size, with the goal of assisting clinicians and other EHR users in making better informed health care decisions.

Keywords

Electronic health record (EHR) applications Knowledge bases Web ontology language (OWL) Ontology application Intelligent systems Knowledge base integration 

Notes

Acknowledgments

This work was supported by the Overseas Research Students Awards Scheme (UK), University of Salford in the UK, and partially through intramural research funds from the National Library of Medicine and the Clinical Center of the National Institutes of Health in the USA. The authors thank Dr. James J Cimino, Yongsheng Gao for very constructive discussions and suggestions, Dr. Judith Effken for helpful comments and suggestions. The authors thank Ms. Cindy Clark, NIH Library Writing Center, for manuscript editing assistance and Ms. Jennifer White for English editing.

References

  1. 1.
    Deveugele, M., Derese, A., van den Brink-Muinen, A., Bensing, J., and De Maeseneer, J., Consultation length in general practice: cross sectional study in six European countries. BMJ 325:472, 2002.CrossRefGoogle Scholar
  2. 2.
    Bechhofer S, van Harmelen F, Hendler J, Horrocks I, McGuinness D, Patel-Schneider P, et al. OWL Web Ontology Language Reference-W3C Recommendation 10 February 2004. [cited 2006 Oct. 10]; Available from: http://www.w3.org/TR/owl-ref/.
  3. 3.
    W3C. OWL Web Ontology Language Overview-W3C Recommendation 10 February 2004.DL McGuinness,F van Harmelen, editors [cited 2006 Oct. 10]; Available from: http://www.w3.org/TR/owl-features/.
  4. 4.
    Jing X, Kay S, Hardiker N, Marley T.Ontology-based knowledge base model construction-OntoKBCF. MEDINFO 2007; 2007; Brisbane, Australia.785-90.Google Scholar
  5. 5.
    Jing, X., Kay, S., Marley, T., Hardiker, N. R., and Cimino, J. J., Incorporating personalized sequence variants and molecular genetic and health knowledge into an EHR prototype based on the Continuity of Care Record Standard. J Biomed Inform 45(1):82–92, 2012.CrossRefGoogle Scholar
  6. 6.
    W3C. Semantic web. [cited 2006 Oct. 10]; Available from: http://www.w3.org/2001/sw/.
  7. 7.
    Jing X, Kay S, Hardiker N, Marley T, Cimino J. Ontological Knowledge Base Model for Cystic Fibrosis --BioPortal. 2012 [cited 2013 July 12th]; Available from: http://bioportal.bioontology.org/ontologies/3155.
  8. 8.
    Dequeker, E., Stuhrmann, M., Morris, M. A., Casals, T., Castellani, C., Claustres, M., et al., Best practice guidelines for molecular genetic diagnosis of cystic fibrosis and CFTR-related disorders- updated European recommendations. Eur J Hum Genet 17:51–65, 2009.CrossRefGoogle Scholar
  9. 9.
    Seol YH, Kaufman DR, Mendonca EA, Cimino JJ, Johnson SB.Scenario-based assessment of physicians' information needs.In: Fieschi M, editor. MEDINFO 2004; 2004: Amsterdam: IOS Press.306-10.Google Scholar
  10. 10.
    Smith, R., What clinical information do doctors need? BMJ 313:1062–8, 1996.CrossRefGoogle Scholar
  11. 11.
    Lappa, E., Undertaking an information-needs analysis of the emergency-care physician to inform the role of the clinical librarian: a Greek perspective. Health Information and Libraries Journal 22:124–32, 2005.CrossRefGoogle Scholar
  12. 12.
    Davies, K., The information-seeking behaviour of doctors: a review of the evidence. Health Information and Libraries Journal 24:78–94, 2007.CrossRefGoogle Scholar
  13. 13.
    Arroll, B., Pandit, S., Kerins, D., Tracey, J., and Kerse, N., Use of information sources among New Zealand family physicians with high access to computers. J Fam Pract 51(8):706, 2002.Google Scholar
  14. 14.
    Cystic Fibrosis Mutation Database. [cited 2008 May 29th]; Available from: http://www.genet.sickkids.on.ca/cftr/.
  15. 15.
    Zielenski, J., and Tsui, L. C., Cystic fibrosis: genotypic and phenotypic variations. Anu Rev Genetics 29:777–807, 1995.CrossRefGoogle Scholar
  16. 16.
    Foundation CF. Therapies for cystic fibrosis. 2012 [updated Feb 14th, 2012; cited 2013 May 30th]; Available from: http://www.cff.org/treatments/therapies/.
  17. 17.
    Carlyle, B. E., Borowitz, D. S., and Glick, P. L., A review of pathophysiology and management of fetuses and neonates with meconium ileus for the pediatric surgeon. J Pediatr Surg 47(4):772–81, 2012.CrossRefGoogle Scholar
  18. 18.
    Bernonille, S., Nies, J., Pedersen, H., Guillot, B., Maazi, M., Berg, A., et al., Three different cases of exploiting decision support services for adverse drug event prevention. Stud Health Technol Inform 166:180–8, 2011.Google Scholar
  19. 19.
    Zeng, Q., and Cimino, J. J., A knowledge-based, concept-oriented view generation system for clinical data. J Biomed Inform 34:112–28, 2001.CrossRefGoogle Scholar
  20. 20.
    Gurupur, V. P1., Suh, S. C., Selvaggi, R. R., Karla, P. R., Nair, J. S., and Ajit, S., An approach for building a personal health information system using conceptual domain knowledge. J Med Sys 36(6):3685–93, 2012.CrossRefGoogle Scholar
  21. 21.
    Beyan, O. D1., and Baykal, N., A knowledge based search tool for performance measures in health care systems. J Med Sys 36(1):201–21, 2012.CrossRefGoogle Scholar
  22. 22.
    Aronson, S. J., Clark, E. H., Babb, L. J., Baxter, S., Farwell, L. M., Funke, B. H., et al., The GeneInsight Suite: A platform to support laboratory and provider use of DNA-based genetic testing. Hum Mutat 32:532–6, 2011.CrossRefGoogle Scholar
  23. 23.
    Zeng, Q., Cimino, J. J., and Zou, K. H., Providing concept-oriented views for clinical data using a knowledge-based system: an evaluation. J Am Med Inform Assoc 9(3):294–305, 2002 [Evaluation Studies].CrossRefGoogle Scholar
  24. 24.
    Deshmukh, V. G., Hoffman, M., Arnoldi, C., Bray, B. E., and Mitchell, J. A., Efficiency of CYP2C9 genetic test representation for automated pharmacogenetic decision support. Methods Inf Med 3:282–90, 2009.CrossRefGoogle Scholar
  25. 25.
    Sheth A, Agrawal S, Lathem J, Oldham N, Wingate H, Yadav P, et al.Active semantic electronic medical record.In: Cruz I, Decker S, Allemang D, Preist C, Schwabe D, Mika P, et al., editors. 5th International Semantic Web Conference (ISWC 2006); 2006; Athens, GA.913-26.Google Scholar
  26. 26.
    Reichert JC, Glasgow M, Narus SP, Clayton PD.Using LOINC to link an EMR to the pertinent paragraph in a structured reference knowledge base.Proc AMIA Symp. AMIA 2002; 2002.652-6.Google Scholar
  27. 27.
    Cimino, J., An integrated approach to computer-based decision support at the point of care. Trans Am Clin Climatol Assoc 118:273–88, 2007.Google Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA) 2014

Authors and Affiliations

  • Xia Jing
    • 1
    • 5
    Email author
  • Stephen Kay
    • 2
  • Tom Marley
    • 3
  • Nicholas R. Hardiker
    • 4
  1. 1.Laboratory for Informatics Development, NIH Clinical Center and National Library of Medicine, National Institutes of HealthBethesdaUSA
  2. 2.School of Health, Sport & Rehabilitation SciencesUniversity of SalfordSalfordUK
  3. 3.Independent ConsultantSalfordUK
  4. 4.School of Nursing, Midwifery & Social WorkUniversity of SalfordSalfordUK
  5. 5.Grover W357 Department of Social & Public Health College of Health Sciences & ProfessionsOhio UniversityAthensUSA

Personalised recommendations