Advertisement

Challenges in Delivering Decision Support Systems: The MATE Experience

  • Dionisio Acosta
  • Vivek Patkar
  • Mo Keshtgar
  • John Fox
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5943)

Abstract

Cancer Multidisciplinary Meeting (MDM) is a widely endorsed mechanism for ensuring high quality evidence-based health care. However, there are shortcomings that could ultimately result in unintended patient harm. On the other hand, clinical guidelines and clinical decision support systems (DSS) have been shown to improve decision-making in various measures. Nevertheless, their clinical use requires seamlessly interoperation with the existing electronic health record (EHR) platform to avoid the detrimental effects that duplication of data and work has in the quality of care. The aim of this work is to propose a computational framework to provide a clinical guideline-based DSS for breast cancer MDM. We discuss a range of design and implementation issues related to knowledge representation and clinical service delivery of the system, and propose a service oriented architecture based on the HL7 EHR functional model. The main result is the DSS named MATE (Multidisciplinary Assistant and Treatment sElector), which demonstrates that decision support can be effectively deployed in a real clinical setting and suggest that the technology could be generalised to other cancer MDMs.

Keywords

Cancer Multidisciplinary Meeting Decision Support System Clinical Guideline HL7 Functional Model Electronic Health Record 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sutton, D.R., Fox, J.: The syntax and semantics of the PROforma guideline modeling language. Journal of the American Medical Informatics Association 10(5), 433–443 (2003)CrossRefGoogle Scholar
  2. 2.
    OpenClinical: Methods and tools for the development of computer-interpretable guidelines: PROforma (2006), http://www.openclinical.org/gmm_proforma.html (accessed 15 October 2009)
  3. 3.
    Sutton, D.R., Taylor, P., Earle, K.: Evaluation of PROforma as a language for implementing medical guidelines in a practical context. BMC Medical Informatics and Decision Making 6(20) (April 2006), doi:10.1186/1472-6947-6-20Google Scholar
  4. 4.
    Patkar, V., et al.: Breast cancer referral guidelines (2008), http://www.cossac.org/projects/credo/applications#guidelines (accessed 15 October 2009)
  5. 5.
    Golbeck, J., Fragoso, G., Hartel, F., et al.: The national cancer institutes thésaurus and ontology. Web Semantics: Science, Services and Agents on the World Wide Web 1(1), 75–80 (2003)CrossRefGoogle Scholar
  6. 6.
    Guyatt, G., Gutterman, D., Baumann, M.H., et al.: Grading strength of recommendations and quality of evidence in clinical guidelines. Chest 129(1), 174–181 (2006)CrossRefGoogle Scholar
  7. 7.
    Ravdin, P.M., Siminoff, L.A., Davis, G.J., et al.: Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. Journal of Clinical Oncology 19(4), 980–991 (2001)Google Scholar
  8. 8.
    Van Zee, K.J., Manasseh, D.M., Bevilacqua, J.L., et al.: A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy. Annals of surgical oncology 10(10), 1140–1151 (2003)CrossRefGoogle Scholar
  9. 9.
    Patkar, V., Fox, J.: Clinical guidelines and care pathways: A case study applying proforma decision support technology to the breast cancer care pathway. In: Ten Teije, A., Miksch, S., Lucas, P. (eds.) Computer-based Medical Guidelines and Protocols: A Primer and Current Trends. Studies in Health Technology and Informatics, vol. 139, pp. 233–242. IOS Press, Amsterdam, doi:10.3233/978-1-58603-873-1-233Google Scholar
  10. 10.
    Groot, P., Hommersom, A., Lucas, P., et al.: Using model checking for critiquing based on clinical guidelines. Artificial Intelligence in Medicine 46, 19–36 (2009)CrossRefGoogle Scholar
  11. 11.
    Perez, B., Porres, I.: Verification of clinical guidelines by model checking. In: 21st IEEE International Symposium on Computer-Based Medical Systems, pp. 114–119. IEEE CS Press, Los Alamitos (2008)CrossRefGoogle Scholar
  12. 12.
    Reenskaug, T.M.H.: The original MVC reports. Technical report, Xerox PARC (December 1979), http://heim.ifi.uio.no/~trygver/2007/MVC_Originals.pdf (accessed 15 October 2009)
  13. 13.
    HL7: Electronic health record functional model (2008), http://www.hl7.org/EHR/ (accessed 15 October 2009)
  14. 14.
    Séroussi, B., Bouaud, J., Gligorov, J., Uzan, S.: Supporting multidisciplinary staff meetings for guideline-based breast cancer management: a study with oncodoc2. In: AMIA Annual Symposium Proceedings, pp. 656–660 (2007)Google Scholar
  15. 15.
    Séroussi, B., Bouaud, J., Antoine, E.C.: Oncodoc: a successful experiment of computer-supported guideline development and implementation in the treatment of breast cancer. Artificial Intelligence in Medicine 22, 43–64 (2001)CrossRefGoogle Scholar
  16. 16.
    Maviglia, S.M., Zielstorff, R.D., Paterno, M., et al.: Automating complex guidelines for chronic disease: Lessons learned. Journal of the American Medical Informatics Association 10(2), 154–165 (2003)CrossRefGoogle Scholar
  17. 17.
    Boxwala, A.A., Peleg, M., Tu, S., et al.: Glif3: a representation format for sharable computer-interpretable clinical practice guidelines. Journal of Biomedical Informatics 37, 147–161 (2004)CrossRefGoogle Scholar
  18. 18.
    Laleci, G.B., Dogac, A.: A semantically enriched clinical guideline model enabling deployment in heterogeneous healthcare environments. IEEE Transactions on Information Technology in Biomedicine 13(2), 263–273 (2009)CrossRefGoogle Scholar
  19. 19.
    Hrabak, K.M., Campbell, J.R., Tu, S.W., et al.: Creating interoperable guidelines: Requirements of vocabulary standards in immunization decision support. In: Klaus, A., Kuhn, J.R., Warren, T.Y.L. (eds.) MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics Building Sustainable Health Systems. Studies in Health Technology and Informatics, vol. 129, pp. 930–934 (2007)Google Scholar
  20. 20.
    Tu, S.W., Campbell, J.R., Glasgow, J., et al.: The sage guideline model: Achievements and overview. Journal of the American Medical Informatics Association 14(5), 589–598 (2007)CrossRefGoogle Scholar
  21. 21.
    Kazemzadeh, R.S., Sartipi, K.: Interoperability of data and knowledge in distributed health care systems. In: Proceedings of the 13th IEEE International Workshop on Software Technology and Engineering Practice, pp. 230–240. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  22. 22.
    Johnson, P.D., Tu, S.W., Musen, M.A., Purves, I.: A virtual medical record for guideline-based decision support. In: AMIA Annual Symposium Proceedings, pp. 294–298 (2001)Google Scholar
  23. 23.
    Schadow, G., Russler, D.C., Mead, C.N., McDonald, C.J.: Integrating medical information and knowledge in the hl7 rim. In: AMIA Annual Symposium Proceedings, pp. 764–768 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dionisio Acosta
    • 1
  • Vivek Patkar
    • 1
  • Mo Keshtgar
    • 2
  • John Fox
    • 3
  1. 1.Cancer InstituteUniversity College LondonUK
  2. 2.Department of SurgeryRoyal Free HospitalLondonUK
  3. 3.Department of Engineering ScienceUniversity of OxfordOxfordUK

Personalised recommendations