Advertisement

Improving Knowledge Management in Patient Safety Reporting: A Semantic Web Ontology Approach

  • Chen Liang
  • Yang Gong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9173)

Abstract

Patient safety reporting system is in an imperative need for reducing and learning from medical errors. Presently, a great number of the reporting systems are suffering low quality of data and poor system performance associated with data quality. For improving the quality of data and the system performance towards reducing harm in healthcare, we introduce an ontological approach with the scope of establishing a comprehensive knowledgebase. A semantic web ontology plays a crucial role to facilitate the knowledge transformation ranging from human-to-computer data entry to computer-to-human knowledge retrieval. The paper describes the theoretical foundation, design, implementation, and evaluation of the prototype ontology. Based on W3C open standard Web Ontology Language (OWL), the proposed ontology was designed and implemented in Protégé 4.3. We envision that utilizing semantic web ontology would serve as a uniformed knowledgebase facilitating information retrieval and clinical decision making.

Keywords

Knowledge management Ontology Clinical information system Patient safety 

Notes

Acknowledgement

We thank Drs. Khalid Almoosa, Xinshuo Wu, and Jing Wang for their expertise and participation in data translation and code review. This project is in part supported by a grant on patient safety from the University of Texas System and a grant from AHRQ, grant number 1R01HS022895.

References

  1. 1.
    Gong, Y.: Data consistency in a voluntary medical incident reporting system. J. Med. Syst. 35, 609–615 (2011)CrossRefGoogle Scholar
  2. 2.
    Thompson, D.A., et al.: Integrating the intensive care unit safety reporting system with existing incident reporting systems. Jt. Comm. J. Qual. Patient Saf. / Jt. Comm. Resour. 31, 585–593 (2011)Google Scholar
  3. 3.
    Itoh, K., Andersen, H.B.: Analysing medical incident reports by use of a human error taxonomy. In: Spitzer, C., et al. (eds.) Probabilistic Safety Assessment and Management Probabilistic safety assessment and management, pp. 2714–2719. Springer, London (2004)CrossRefGoogle Scholar
  4. 4.
    Zhan, C., Miller, M.R.: Administrative data based patient safety research: a critical review. Qual. Saf. Heal. Care 12, ii58–ii63 (2003)CrossRefGoogle Scholar
  5. 5.
    Miller, M.R., Elixhauser, A., Zhan, C., Meyer, G.S.: Patient safety indicators: using administrative data to identify potential patient safety concerns. Health Serv. Res. 36, 110–132 (2001)Google Scholar
  6. 6.
    Gong, Y.: Terminology in a voluntary medical incident reporting system: a human-centered perspective. In: Proceedings of the 1st ACM International Health Informatics Symposium. ACM, pp. 2–7 (2010)Google Scholar
  7. 7.
    Pronovost, P.J., Morlock, L.L., Sexton, J.B., Miller, M.R., Holzmueller, C.G., Thompson, D.A., Lubomski, L.H., Wu, A.W.: Improving the value of patient safety reporting systems. In: Advances in Patient Safety: New Directions and Alternative Approaches. Assessment vol. 1, pp. 1–9 (2008)Google Scholar
  8. 8.
    McDonald, C.J.: The barriers to electronic medical record systems and how to overcome them. J. Am. Med. Inform. Assoc. 4, 213–221 (1997)CrossRefGoogle Scholar
  9. 9.
    Sager, N., Friedman, C.: M.S.L.: Review of “medical language processing: computer management of narrative data”. Comput. Linguist. 15, 195–198 (1989)Google Scholar
  10. 10.
    Friedman, C., Johnson, S.B.: Natural language and text processing in biomedicine. In: Shortliffe, E.H., Cimino, J.J. (eds.) Biomedical Informatics: Computer Applications in Health Care and Biomedicine. Health Informatics, pp. 312–343. Springer, New York (2006)CrossRefGoogle Scholar
  11. 11.
    Spigelman, A.D., Swan, J.: Review of the Australian incident monitoring system. ANZ J. Surg. 75, 657–661 (2005)CrossRefGoogle Scholar
  12. 12.
    Battles, J.B., Kaplan, H., Van der Schaaf, T., Shea, C.: The attributes of medical event-reporting systems. Arch. Pathol. Lab. Med. 122, 132–138 (1998)Google Scholar
  13. 13.
    Sherman, H., et al.: Towards an international classification for patient safety: the conceptual framework. Int. J. Qual. Health Care 21, 2–8 (2009)CrossRefGoogle Scholar
  14. 14.
    O’Leary, D.E.: Using AI in knowledge management: knowledge bases and ontologies. IEEE Intell. Syst. Their Appl. 13, 34–39 (1998)CrossRefGoogle Scholar
  15. 15.
    McGuinness, D.L., van Harmelen, F.: OWL Web Ontology Language OverviewGoogle Scholar
  16. 16.
    Maynard, D., Li, Y., Peters, W.: NLP techniques for term extraction and ontology population. In: Proceeding of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge, pp. 107–127 (2008)Google Scholar
  17. 17.
    Ananiadou, S., McNaught, J.: Text Mining for Biology and Biomedicine. Artech House, London (2006)Google Scholar
  18. 18.
    Gilchrist, A.: Thesauri, taxonomies and ontologies – an etymological note. J. Doc. 59, 7–18 (2003)CrossRefGoogle Scholar
  19. 19.
    Burton-Jones, A., Storey, V.C., Sugumaran, V., Ahluwalia, P.: A semiotic metrics suite for assessing the quality of ontologies. Data Knowl. Eng. 55, 84–102 (2005)CrossRefGoogle Scholar
  20. 20.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical owl-dl reasoner. Web Semant. Sci. Serv. Agents World Wide Web. 5, 51–53 (2007)CrossRefGoogle Scholar
  21. 21.
    Tsarkov, D., Horrocks, I.: FaCT++ description logic reasoner: system description. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 292–297. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. 22.
    Zhang, J., Patel, V.L., Johnson, T.R., Shortliffe, E.H.: A cognitive taxonomy of medical errors. J. Biomed. Inform. 37, 193–204 (2004)CrossRefGoogle Scholar
  23. 23.
    Chang, A., Schyve, P.M., Croteau, R.J., O’Leary, D.S., Loeb, J.M.: The JCAHO patient safety event taxonomy: a standardized terminology and classification schema for near misses and adverse events. Int. J. Qual. Heal. Care. 17, 95–105 (2005)CrossRefGoogle Scholar
  24. 24.
    Brixey, J., Johnson, T.R., Zhang, J.: Evaluating a medical error taxonomy. In: Proceedings AMIA Symposium, pp. 71–75 (2002)Google Scholar
  25. 25.
    Suresh, G., et al.: Voluntary anonymous reporting of medical errors for neonatal intensive care. Pediatr. 113, 1609–1618 (2004)CrossRefGoogle Scholar
  26. 26.
    Woods, D.M., Johnson, J., Holl, J.L., Mehra, M., Thomas, E.J., Ogata, E.S., Lannon, C.: Anatomy of a patient safety event: a pediatric patient safety taxonomy. Qual. Saf. Health Care. 14, 422–427 (2005)CrossRefGoogle Scholar
  27. 27.
    Dovey, S.M., Meyers, D.S., Phillips, R.L., Green, L.A., Fryer, G.E., Galliher, J.M., Kappus, J., Grob, P.: A preliminary taxonomy of medical errors in family practice. Qual Saf Health Care 11(3), 233–238 (2002)CrossRefGoogle Scholar
  28. 28.
    Woods, A., Doan-Johnson, S.: Executive summary: toward a taxonomy of nursing practice errors. Nurs. Manage. 33, 45–48 (2002)CrossRefGoogle Scholar
  29. 29.
    Greens, R.A.: Clinical Decision Support: The Road Ahead. Academic Press, San Diego (2006)Google Scholar
  30. 30.
    Tuttle, D., Holloway, R., Baird, T., Sheehan, B., Skelton, W.K.: Electronic reporting to improve patient safety. Qual. Saf. Health Care. 13, 281–286 (2004)CrossRefGoogle Scholar
  31. 31.
    Kaplan, B.: Reducing barriers to physician data entry for computer-based patient records. Top. Health Inf. Manage. 15, 24–34 (1994)Google Scholar
  32. 32.
    Walsh, S.H.: The clinician’s perspective on electronic health records and how they can affect patient care. BMJ 328, 1184–1187 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.The University of Texas Health Science Center at HoustonHoustonUSA

Personalised recommendations