Towards the Automated Calculation of Clinical Quality Indicators

  • Kathrin Dentler
  • Annette ten Teije
  • Ronald Cornet
  • Nicolette de Keizer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6924)

Abstract

To measure the quality of care in order to identify whether and how it can be improved is of increasing importance, and several organisations define quality indicators as tools for such measurement. The values of these quality indicators should ideally be calculated automatically based on data that is being collected during the care process. The central idea behind this paper is that quality indicators can be regarded as semantic queries that retrieve patients who fulfil certain constraints, and that indicators that are formalised as semantic queries can be calculated automatically by being run against patient data. We report our experiences in manually formalising exemplary quality indicators from natural language into SPARQL queries, and prove the concept by running the resulting queries against self-generated synthetic patient data. Both the queries and the patient data make use of SNOMED CT to represent relevant concepts. Our experimental results are promising: we ran eight queries against a dataset of 300,000 synthetically generated patients, and retrieved consistent results within acceptable time.

Keywords

Quality Indicators Clinical Data Formalisation of Clinical Quality Indicators Semantic Web Reasoning SPARQL SNOMED CT 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Besana, P., Cuggia, M., Zekri, O., Bourde, A., Burgun, A.: Using Semantic Web technologies for Clinical Trial Recruitment. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part II. LNCS, vol. 6497, pp. 34–49. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Broekstra, J., Kampman, A., Van Harmelen, F.: Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. In: Horrocks, I., Hendler, J. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 54–68. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Cornet, R., de Keizer, N.: Forty years of SNOMED: a literature review. BMC Medical Informatics and Decision Making 8(suppl 1), S2 (2008)CrossRefGoogle Scholar
  4. 4.
    Dentler, K., Cornet, R., ten Teije, A., de Keizer, N.: Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semantic Web 2, 71–87 (2011)Google Scholar
  5. 5.
    Donabedian, A.: The Quality of Care: How Can It Be Assessed? JAMA (1988)Google Scholar
  6. 6.
    Horridge, M., Bechhofer, S.: The OWL API: A Java API for OWL ontologies. Semantic Web Journal (to appear), http://www.semantic-web-journal.net/
  7. 7.
    Kazakov, Y.: Consequence-driven reasoning for horn SHIQ ontologies. In: Proceedings of the 21st International Workshop on Description Logics, pp. 2040–2045 (2009)Google Scholar
  8. 8.
    Kiryakov, A., Ognyanov, D., Manov, D.: OWLIM – A Pragmatic Semantic Repository for OWL. In: Dean, M., Guo, Y., Jun, W., Kaschek, R., Krishnaswamy, S., Pan, Z., Sheng, Q.Z. (eds.) WISE 2005 Workshops. LNCS, vol. 3807, pp. 182–192. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Lawrence, M., Olesen, F.: Indicators of Quality in Health Care. European Journal of General Practice 3(3), 103–108 (1997)CrossRefGoogle Scholar
  10. 10.
    Lilford, R., Mohammed, M.A., Spiegelhalter, D., Thomson, R.: Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma. Lancet 363(9415), 1147–1154 (2004)CrossRefGoogle Scholar
  11. 11.
    Medlock, S., Opondo, D., Eslami, S., Askari, M., Wierenga, P., de Rooij, S.E., Abu-Hanna, A.: LERM (Logical Elements Rule Method): A method for assessing and formalizing clinical rules for decision support. International Journal of Medical Informatics 80(4), 286–295 (2011)CrossRefGoogle Scholar
  12. 12.
    Palchuk, M.B., Bogdanova, A.A., Jatkar, T., Liu, J., Karmiy, N., Housman, D., Einbinder, J.S.: Automating Quality Reporting with Health Quality Measures Format “eMeasures” and an Analytics Engine. In: AMIA Symposium Proceedings, page 1205 (2010)Google Scholar
  13. 13.
    Patel, C., Cimino, J., Dolby, J., Fokoue, A., Kalyanpur, A., Kershenbaum, A., Ma, L., Schonberg, E., Srinivas, K.: Matching Patient Records to Clinical Trials Using Ontologies. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 816–829. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Stegers, R., ten Teije, A., van Harmelen, F.: From Natural Language to Formal Proof Goal. In: Staab, S., Svátek, V. (eds.) EKAW 2006. LNCS (LNAI), vol. 4248, pp. 51–58. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Tsarkov, D., Horrocks, I.: FaCT ++ Description Logic Reasoner: System DescriptionGoogle Scholar
  16. 16.
    Weng, C., Tu, S.W., Sim, I., Richesson, R.: Formal representation of eligibility criteria: A literature review. Journal of Biomedical Informatics 43(3), 451–467 (2010)CrossRefGoogle Scholar
  17. 17.
    Williams, C.A., Mosley-Williams, A.D., Overhage, J.M.: Arthritis Quality Indicators for the Veterans Administration: Implications for Electronic Data Collection, Storage Format, Quality Assessment, and Clinical Decision Support. In: AMIA Symposium Proceedings, pp. 806–810 (January 2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kathrin Dentler
    • 1
    • 2
  • Annette ten Teije
    • 1
  • Ronald Cornet
    • 2
  • Nicolette de Keizer
    • 2
  1. 1.Dept. of Computer ScienceVrije Universiteit AmsterdamThe Netherlands
  2. 2.Dept. of Medical Informatics, Academic Medical CenterUniversity of AmsterdamThe Netherlands

Personalised recommendations