Skip to main content

Advertisement

Log in

A Data-Mining Framework for Transnational Healthcare System

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Medical resources are important and necessary in health care. Recently, the development of methods for improving the efficiency of medical resource utilization is an emerging problem. Despite evidence supporting the use of order sets in hospitals, only a small number of health information systems have successfully equipped physicians with analysis of complex order sequences from clinical pathway and clinical guideline. This paper presents a data-mining framework for transnational healthcare system to find alternative practices, including transfusion, pre-admission tests, and evaluation of liver diseases. However, individual countries vary with respect to geographical location, living habits, and culture, so disease risks and treatment methods also vary across countries. To realize the difference, a service-oriented architecture and cloud-computing technology are applied to analyze these medical data. The validity of the proposed system is demonstrated in including Taiwan and Mongolia, to ensure the feasibility of our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Kohn, L. T., Corrigan, J. M., and Donaldson, M. S., To err is human: building a safer health system. Institute of Medicine (IOM). National Academies Press, Washington, 1999.

    Google Scholar 

  2. Bobb, A. M., Payne, T. H., and Gross, P. A., Viewpoint: Controversies surrounding use of order sets for clinical decision support in computerized provider order entry. J. Am. Med. Inform. Assoc. 14:41–47, 2007.

    Article  Google Scholar 

  3. Fiol, G. D., Rocha, R. A., Bradshaw, R. L., Hulse, N. C., and Roemer, L. K., An XML model that enables the development of complex order sets by clinical experts. IEEE Trans. Inf. Technol. Biomed. 9(2):216–228, 2005.

    Article  Google Scholar 

  4. Hsieh, S. H., Hou, I. C., Cheng, P. H., Tang, C. T., Shen, P. C., Hsu, K. P., Hsieh, S. L., and Lai, F., Design and implementation of web-based mobile electronic medication administration record. J. Med. Syst. 34(5):947–958, 2010.

    Article  Google Scholar 

  5. Yang, T. H., Cheng, P. H., Yang, C. H., Lai, F., Chen, C. L., Lee, H. H., Hsu, K. P., Chen, C. H., Tan, C. T., and Sun, Y. S., A scalable multi-tier architecture for the National Taiwan University Hospital information system based on HL7 standard. In: Proc. IEEE International Symposium on Computer-Based Medical Systems. Utah, USA, 2006, 99–104.

  6. Hulse, N. C., Fiol, G. D., Bradshaw, R. L., Roemer, L. K., and Rocha, R. A., Towards an on-demand peer feedback system for a clinical knowledge base: a case study with order sets. J. Biomed. Inform. 41(1):152–164, 2008.

    Article  Google Scholar 

  7. Ahmad, A., Teater, P., Bentley, T. D., Kuehn, L., Kumar, R. R., Thomas, A., and Mekhjian, H. S., Key attributes of a successful physician order entry system implementation in a multihospital environment. J. Am. Med. Inform. Assoc. 9(1):16–24, 2002.

    Article  Google Scholar 

  8. Stablein, D., and Drazen, E., Getting the most out of CPOE. Healthcare Informatics Online, 2003.

  9. Schroeder, C. G., and Pierpaoli, P. G., Direct order entry by physicians in a computerized hospital information system. Am. J. Hosp. Pharm. 43(2):355–359, 1986.

    Google Scholar 

  10. Lee, F., Teich, J. M., Spurr, C. D., and Bates, D. W., Implementation of physician order entry: user satisfaction and self-reported usage patterns. J. Am. Med. Inform. Assoc. 3(1):42–55, 1996.

    Article  Google Scholar 

  11. Lin, F. U., Chou, S. C., Pan, S. M., and Chen, Y. M., Mining time dependency patterns in clinical pathways. Int. J. Med. Inform. 62(1):11–25, 2001.

    Article  Google Scholar 

  12. Wright, A., and Sittig, D. F., Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system. In: Proc. AMIA Annual Symposium. 2006, 819–823.

  13. Ram, P., Berg, D., Tu, S., Mandfield, G., Ye, Q., Abarbanel, R., and Beard, N., Executing clinical practice guidelines using the SAGE execution engine. IOS Press, 2004, 251–254.

  14. Tu, S. W., Campbell, J. R., and Glasgow, J., The SAGE guideline model: achievements and overview. J. Am. Med. Inform. Assoc. 14(5):589–598, 2007.

    Article  Google Scholar 

  15. Peleg, M., Boxwala, A. A., Ogunyemi, O., Zeng, Q., Tu, S., Lacson, R., Bernstam, E., Ash, N., Mork, P., Ohno-Machado, L., Shortliffe, E. H., and Greenes, R. A., GLIF3: the evolution of a guideline representation format. In: Proc. AMIA Annual Symposium, 2000, 645–649.

  16. Peleg, M., Ogunyemi, O., Tu, S., Boxwala, A. A., Zeng, Q, Greenes R. A., and Shortliffe E. H., Using features of Arden Syntax with object-oriented medical data models for guideline modeling. In: Proc. AMIA Annual Symposium, 2001, 523–527.

  17. Pryor, T. A., and Hripcsak, G., The Arden Syntax for medical logic modules. J. Clin. Monit. Comput. 10(4):215–224, 1993.

    Article  Google Scholar 

  18. Gennari, J., Fergerson, R., Grosso, W. E., Crubezy, M., Eriksson, H., Noy, N. F., Tu, S. W., and Musen, M. A., The evolution of Protégé: an environment for knowledge-based systems development. Int. J. Human Comput. Stud. 58(1):89–123, 2003.

    Article  Google Scholar 

  19. Ohno-Machado, L., Gennari, J. H., and Murph, S. N., The guideline interchange format: a model for representing guidelines. J. Am. Med. Inform. Assoc. 5(4):357–372, 1998.

    Article  Google Scholar 

  20. Hsieh, S. L., Lai, F., Cheng, P. H., Chen, C. L., Lee, H. H., Tsai, W. N., Weng, Y. C., Hsieh, S. H., Hsu, K. P., Ko, L. F., Chen, C. H., and Yang, T. H., An integrated healthcare enterprise information portal and healthcare information system framework. In: Proc. IEEE Engineering in Medicine and Biology Society. New York, 2006, 4731–4734.

  21. Hsieh, S. H., Cheng, P. H., Chen, C. H., Huang, K. H., and Chen, P. H., A newborn screening system based on service-oriented architecture embedded support vector machine. J. Med. Syst. 34(4):727–733, 2010.

    Article  Google Scholar 

  22. Sequence clustering algorithm is introduced and available at http://msdn.microsoft.com/en-us/library/ms175462.aspx.

  23. Ferreira, D., Zacarias, M., Malheiros, M., and Ferreira, P., Approaching process mining with sequence clustering: experiments and findings. In: Proc. International Conference on Business Process Management, LNCS 4714, 2007, 360–374.

  24. Dempster, A., Laird, N., and Rubin, D., Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B. 39(1):1–38, 1977.

    MathSciNet  MATH  Google Scholar 

  25. Shimizu, Y., Liver in systemic disease. World J. Gastroenterol. 14(26):4111–4119, 2008.

    Article  MathSciNet  Google Scholar 

  26. Dufour, D. R., Lott, J. A., Nolte, F. S., Gretch, D. R., Koff, R. S., and Seeff, L. B., Diagnosis and monitoring of hepatic injury. I. Performance characteristics of laboratory tests. Am. Assoc. Clin. Chem. 46:2027–2049, 2000.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jin-Ming Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shen, CP., Jigjidsuren, C., Dorjgochoo, S. et al. A Data-Mining Framework for Transnational Healthcare System. J Med Syst 36, 2565–2575 (2012). https://doi.org/10.1007/s10916-011-9729-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-011-9729-7

Keywords

Navigation