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Journal of Medical Systems

, Volume 36, Issue 5, pp 3307–3319 | Cite as

Customer-Centered Careflow Modeling Based on Guidelines

  • Biqing HuangEmail author
  • Peng Zhu
  • Cheng Wu
Original Paper

Abstract

In contemporary society, customer-centered health care, which stresses customer participation and long-term tailored care, is inevitably becoming a trend. Compared with the hospital or physician-centered healthcare process, the customer-centered healthcare process requires more knowledge and modeling such a process is extremely complex. Thus, building a care process model for a special customer is cost prohibitive. In addition, during the execution of a care process model, the information system should have flexibility to modify the model so that it adapts to changes in the healthcare process. Therefore, supporting the process in a flexible, cost-effective way is a key challenge for information technology. To meet this challenge, first, we analyze various kinds of knowledge used in process modeling, illustrate their characteristics, and detail their roles and effects in careflow modeling. Secondly, we propose a methodology to manage a lifecycle of the healthcare process modeling, with which models could be built gradually with convenience and efficiency. In this lifecycle, different levels of process models are established based on the kinds of knowledge involved, and the diffusion strategy of these process models is designed. Thirdly, architecture and prototype of the system supporting the process modeling and its lifecycle are given. This careflow system also considers the compatibility of legacy systems and authority problems. Finally, an example is provided to demonstrate implementation of the careflow system.

Keywords

Careflow Process modeling Customer-centered health care Information system 

Notes

Acknowledgements

This work is supported by the State Key Technology R&D Program, China (2008BAH24B01, 2008BAH24B03).

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Haux, R., Health information systems–past, present, future. Int. J. Med. Inform. 75(3–4):268–281, 2006. doi: 10.1016/j.ijmedinf.2005.08.002.CrossRefGoogle Scholar
  2. 2.
    Shortliffe, E. H., and Cimino, J. J., Biomedical informatics: computer applications in health care and biomedicine. Springer Verlag, 2006.Google Scholar
  3. 3.
    Quinn, G. P., Pratt, C. L., Bryant-George, K., et al., Lung cancer patients’ decisions about clinical trials and the theory of planned behavior. J. Canc. Educ. 26(4):641–648, 2011.CrossRefGoogle Scholar
  4. 4.
    Bundesmann, R., and Kaplowitz, S. A., Provider communication and patient participation in diabetes self-care. Patient Educ. Counsel. 85(2):143–147, 2010. doi: 10.1016/j.pec.2010.09.025.CrossRefGoogle Scholar
  5. 5.
    Clouten, K., and Weber, R., Patient-focused care … playing to win. Nurs. Manag. 25(2):34–36, 1994.Google Scholar
  6. 6.
    Davis, K., Schoenbaum, S. C., and Audet, A. M., A 2020 vision of patient-centered primary care. J. Gen. Intern. Med. 20(10):953–957, 2005. doi: 10.1111/j.1525-1497.2005.0178.x.CrossRefGoogle Scholar
  7. 7.
    Stichler, J. F., Patient-centered healthcare design. J. Nurs. Adm. 41(12):503–506, 2011. doi: 10.1097/NNA.0b013e3182378a3b.CrossRefGoogle Scholar
  8. 8.
    Anyanwu, K., Sheth, A., Cardoso, J., Miller, J., and Kochut, K., Healthcare enterprise process development and integration. J. Res. Pract. Inf. Technol. 35(2):83–98, 2003.Google Scholar
  9. 9.
    Layna, F., and Workflow Management C, Workflow handbook 2005. Future Strategies Inc., 2005.Google Scholar
  10. 10.
    Quaglini, S., Stefanelli, M., Cavallini, A., Micieli, G., Fassino, C., and Mossa, C., Guideline-based careflow systems. Artif. Intell. Med. 20(1):5–22, 2000.CrossRefGoogle Scholar
  11. 11.
    Wang, D., and Shortliffe, E. H., GLEE—a model-driven execution system for computer-based implementation of clinical practice guidelines. Proc. AMIA Symp. 855–859, 2002.Google Scholar
  12. 12.
    Wyatt, J. C., Management of explicit and tacit knowledge. J. R. Soc. Med. 94(1):6–9, 2001.Google Scholar
  13. 13.
    Stefanelli, M., Knowledge and process management in health care organizations. Methods Inf. Med. 43(5):525–535, 2004.Google Scholar
  14. 14.
    Lenz, R., and Reichert, M., IT support for healthcare processes–premises, challenges, perspectives. Data Knowl. Eng. 61(1):39–58, 2007. doi: 10.1016/j.datak.2006.04.007.CrossRefGoogle Scholar
  15. 15.
    Ammon, D., Hoffmann, D., Jakob, T., Finkeissen, E., Detschew, V., and Wetter, T., Management of knowledge-intensive healthcare processes on the example of general medical documentation. In: Ardagna, D., Mecella, M., Yang, J. (Eds.), Business process management workshops, Berlin, 2009. Lecture Notes in Business Information Processing. Springer-Verlag Berlin, 324–335.Google Scholar
  16. 16.
    Hoyt, D. B., Clinical practice guidelines. Am. J. Surg. 173(1):32–34, 1997.CrossRefGoogle Scholar
  17. 17.
    Van Der Aalst, W., and Van Hee, K. M., Workflow management: models, methods, and systems. The MIT Press, 2004.Google Scholar
  18. 18.
    Borst, W. N., Construction of engineering ontologies for knowledge sharing and reuse. University of Twente, Enschede, 1997.Google Scholar
  19. 19.
    Uschold, M., King, M., Moralee, S., and Zorgios, Y., The enterprise ontology. Knowl. Eng. Rev. 13(1):31–89, 1998.CrossRefGoogle Scholar
  20. 20.
    Gruninger, M., and Fox, M. S., The logic of enterprise modelling. Kluwer Academic Publishers, 1995.Google Scholar
  21. 21.
    The Protégé Ontology Editor and Knowledge Acquisition System. http://protege.stanford.edu/
  22. 22.
    OWL Web Ontology Language. http://www.w3.org/TR/owl-features/
  23. 23.
    Panja, S., Starr, B., and Colleran, K. M., Patient knowledge improves glycemic control: is it time to go back to the classroom? 53(5):264, 2005.Google Scholar
  24. 24.
    Lane, D. A., Ponsford, J., Shelley, A., Sirpal, A., and Lip, G. Y. H., Patient knowledge and perceptions of atrial fibrillation and anticoagulant therapy: effects of an educational intervention programme The West Birmingham Atrial Fibrillation Project. Int. J. Cardiol. 110(3):354–358, 2006.CrossRefGoogle Scholar
  25. 25.
    High-level Petri Nets–Concepts, Definitions and Graphical Notation. Final Draft International Standard ISO/IEC 15909, October, 2000.Google Scholar
  26. 26.
    Murata, T., Petri nets–properties, analysis and applications. Proc. IEEE 77(4):541–580, 1989.CrossRefGoogle Scholar
  27. 27.
    XML Process Definition Language. www.wfmc.org/xpdl.html
  28. 28.
    Curbera, F., Goland, Y., Klein, J., Leymann, F., Roller, D., Thatte, S., Weerawarana, S., and Thatte, S., Business process execution language for web services, version 1.0, 2002 http://www.oasis-open.org/committees/download.php/2046/BPEL%20V1-1%20May%205%202003%20Final.pdf
  29. 29.
    Boxwala, A. A., Peleg, M., Tu, S., Ogunyemi, O., Zeng, Q. T., Wang, D., Patel, V. L., Greenes, R. A., and Shortliffe, E. H., GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J. Biomed. Informat. 37(3):147–161, 2004.CrossRefGoogle Scholar
  30. 30.
    Wang, D. W., Peleg, M., Tu, S. W., Boxwala, A. A., Ogunyemi, O., Zeng, Q., Greenes, R. A., Patel, V. L., and Shortliffe, E. H., Design and implementation of the GLIF3 guideline execution engine. J. Biomed. Informat. 37(5):305–318, 2004. doi: 10.1016/j.jbi.2004.06.002.CrossRefGoogle Scholar
  31. 31.
    Dadam, P., and Reichert, M., The ADEPT project: a decade of research and development for robust and flexible process support: challenges and achievements. Comput. Sci. Res. Dev. 23(2):81–97, 2009. doi: 10.1007/s00450-009-0068-6.CrossRefGoogle Scholar
  32. 32.
    Dadam, P., Reichert, M., Rinderle-Ma, S., Lanz, A., Pryss, R., Precleschly, M., Kolb, J., Ly, L. T., Jurisch, M., Kreher, U., and Goser, K., From ADEPT to AristaFlow BPM suite: a research vision has become reality. In: Rinderle Ma, S., Sadiq, S., Leymann, F. (Eds.), Business process management workshops, 2009, vol 43. Lecture Notes in Business Information Processing. Springer-Verlag Berlin, Berlin, pp 529–531.Google Scholar
  33. 33.
    Lanz, A., Reichert, M., and Dadam, P., Making business process implementations flexible and robust: error handling in the AristaFlow BPM suite. http://dbis.eprints.uni-ulm.de/661/
  34. 34.
    van der Aalst, W. M. P., van Dongen, B. F., Herbst, J., Maruster, L., Schimm, G., and Weijters, A., Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47(2):237–267, 2003. doi: 10.1016/s0169-023x(03)00066-1.CrossRefGoogle Scholar
  35. 35.
    van Dongen, B. F., de Medeiros, A. K. A., Verbeek, H. M. W., Weijters, A., and van der Aalst, W. M. P., The ProM framework: a new era in process mining tool support. In: Ciardo, G., and Darondeau, P. (Eds.), Applications and theory of Petri nets 2005, proceedings, vol 3536. Lecture Notes in Computer Science. Springer-Verlag Berlin, Berlin, pp. 444–454, 2005.CrossRefGoogle Scholar
  36. 36.
    HL7, Health Level Seven. http://www.hl7.org/
  37. 37.
    DICOM, Digital Imaging and Communications in Medicine. http://medical.nema.org/
  38. 38.
    Pashalidis, A., and Mitchell, C. J., A taxonomy of single sign-on systems. In: SafaviNaini, R., and Seberry, J. (Eds.), Information security and privacy, proceedings, vol 2727. Lecture Notes in Computer Science. Springer-Verlag Berlin, Berlin, pp. 249–264, 2003.Google Scholar
  39. 39.
    World Health Organization. Worldwide prevalence of anaemia 1993–2005. http://whqlibdoc.who.int/publications/2008/9789241596657_eng.pdf
  40. 40.
    World Health Organization. Iron deficiency anaemia: assessment, prevention, and control. A guide for programme managers, 2001. http://www.who.int/nutrition/publications/micronutrients/anaemia_iron_deficiency/WHO_NHD_01.3/en/index.html

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology, Department of AutomationTsinghua UniversityBeijingChina

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