Sharable Appropriateness Criteria in GLIF3 Using Standards and the Knowledge-Data Ontology Mapper

  • Mor Peleg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5943)


Creating computer-interpretable guidelines (CIGs) requires much effort. This effort would be leveraged by sharing CIGs with more than one implementing institution. Sharing necessitates mapping the CIG’s data items to institutional EMRs. Sharing can be enhanced by using standard formats and a Global-as-view approach to data integration, where a common data model is used to generate standard views of proprietary EMRs. In this paper we demonstrate how generic guideline expressions could be encoded in the GELLO standard using HL7-RIM-based views. We also explain how the Knowledge-Data Ontology Mapper (KDOM) can be used to simplify GELLO expressions. We are aiming to use this approach for computerizing radiology appropriateness criteria and linking them with EMR data from Stanford Hospital. We discuss our initial study to assess whether such computerization would be possible and beneficial.


appropriateness criteria clinical guidelines GLIF GEL GELLO EMR ontology knowledge sharing KDOM 


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  1. 1.
    Boxwala, A.A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q., Wang, D., et al.: GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. Journal of Biomedical Informatics 37(3), 147–161 (2004)Google Scholar
  2. 2.
    Health Level Seven. HL7 Reference Information Model (2006),
  3. 3.
    Peleg, M., Keren, S., Denekamp, Y.: Mapping Computerized Clinical Guidelines to Electronic Medical Records: Knowledge-Data Ontological Mapper (KDOM). J. Biomed. Inform. 41(1), 180–201 (2008)CrossRefGoogle Scholar
  4. 4.
    de-Clercq, P.A., Blom, J.A., Korsten, H.H., Hasman, A.: Approaches for creating computer-interpretable guidelines that facilitate decision support. Artif. Intell. Med. 31(1), 1–27 (2004)Google Scholar
  5. 5.
    Tu, S.W., Campbell, J.R., Glasgow, J., Nyman, M.A., McClure, R., McClay, J.P.C., Hrabak, K.M., Berg, D., Weida, T., Mansfield, J.G., Musen, M.A., Abarbanel, R.M.: The SAGE Guideline Model: achievements and overview. J. Am. Med. Inform. Assoc. 14(5), 589–598 (2007)CrossRefGoogle Scholar
  6. 6.
    Hripcsak, G., Ludemann, P., Pryor, T.A., Wigertz, O.B., Clayton, P.D.: Rationale for the Arden Syntax. Comput. Biomed. Res. 27(4), 291–324 (1994)CrossRefGoogle Scholar
  7. 7.
    Peleg, M., Boxwala, A.A., Tu, S., Zeng, Q., Ogunyemi, O., Wang, D., et al.: The InterMed Approach to Sharable Computer-interpretable Guidelines: A Review. J. Am. Med. Inform. Assoc. 11(1), 1–10 (2004)CrossRefGoogle Scholar
  8. 8.
    Sordo, M., Ogunyemi, O., Boxwala, A.A., Greenes, R.A., Tu, S.: Software Specifications for GELLO: An Object-Oriented Query and Expression Language for Clinical Decision Support: Decision Systems Group Report DSG-TR-2003-02 (2004)Google Scholar
  9. 9.
    Object Management Group. Object Constraint LanguageGoogle Scholar
  10. 10.
    Correndo, G., Terenziani, P.: Towards a flexible integration of clinical guideline systems with medical ontologies and medical information systems. Stud. Health Technol. Inform. 101, 108–112 (2004)Google Scholar
  11. 11.
    German, E., Leibowitz, A., Shahar, Y.: An architecture for linking medical decision-support applications to clinical databases and its evaluation. J. Biomed. Inform. 42(2), 203–218 (2009)CrossRefGoogle Scholar
  12. 12.
    Grosso, W.E., Eriksson, H., Fergerson, R., Gennari, J.H., Tu, S.W., Musen, M.A.: Knowledge Modeling at the Millennium (The Design and Evolution of Protege-2000). In: Gains, B.R., Kremer, R., Musen, M. (eds.) The 12th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop, Banff, Canada, pp. 7-4-1–7-4-36 (1999)Google Scholar
  13. 13.
    Eccher, C., Seyfang, A., Ferro, A., Stankevich, S., Miksch, S.: Bridging an Asbru Protocol to an Existing Electronic Patient Record. In: Workshop on Knowledge Representation for Health-Care: Patient Data, Processes and Guidelines, in conjunction with AIME, Verona, Italy (2009)Google Scholar
  14. 14.
    Peleg, M., Wang, D., Fodor, A., Keren, S., Karnieli, E.: Lessons learned from adapting a generic narrative diabetic-foot guideline to an institutional decision-support system. Studies in Health Technology and Informatics, 243–252 (2008)Google Scholar
  15. 15.
    Peleg, M., Tu, S.W., Bury, J., Ciccarese, P., Fox, J., Greenes, R.A., et al.: Comparing Computer-Interpretable Guideline Models: A Case-Study Approach. J. Am. Med. Inform. Assoc. 10(1), 52–68 (2003)CrossRefGoogle Scholar
  16. 16.
    Peleg, M., Ogunyemi, O., Tu, S., Boxwala, A.A., Zeng, Q., Greenes, R.A., et al.: Using Features of Arden Syntax with Object-Oriented Medical Data Models for Guideline Modeling. In: Proc. AMIA Symp., pp. 523–537 (2001)Google Scholar
  17. 17.
    Wang, D., Shortliffe, E.H.: GLEE – A Model-Driven Execution System for Computer-Based Implementation of Clinical Practice Guidelines. In: Proc. AMIA Symp., pp. 855–859 (2002)Google Scholar
  18. 18.
    Schadow, G., Russler, D., Mead, C., Case, J., McDonald, C.: The Unified Service Action Model: Indianapolis: Regenstrief Institute for Health Care (1999)Google Scholar
  19. 19.
    Johnson, P.D., Tu, S.W., Musen, M.A., Purves, I.: A Virtual Medical Record for Guideline-Based Decision Support. In: Proc. AMIA Symp., pp. 294–298 (2001)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mor Peleg
    • 1
  1. 1.Department of Management Information SystemsUniversity of HaifaIsrael

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