Journal of Medical Systems

, Volume 34, Issue 5, pp 829–842

A Health Examination System Integrated with Clinical Decision Support System

Original Paper
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Abstract

Health examinations play a key role in preventive medicine. We propose a health examination system named Health Examination Automatic Logic System (HEALS) to assist clinical workers in improving the total quality of health examinations. Quality of automated inference is confirmed by the zero inference error where during 6 months and 14,773 cases. Automated inference time is less than one second per case in contrast to 2 to 5 min for physicians. The most significant result of efficiency evaluation is that 3,494 of 4,356 (80.2%) cases take less than 3 min per case for producing a report summary. In the evaluation of effectiveness, novice physicians got 18% improvement in making decisions with the assistance of our system. We conclude that a health examination system with a clinical decision system can greatly reduce the mundane burden on clinical workers and markedly improve the quality and efficiency of health examination tasks.

Keywords

Decision support systems Clinical preventive medicine Health promotion Preventive health services Diagnostic errors 

Reference

  1. 1.
    Fitzmaurice, J. M., Adams, K., and Eisenberg, J. M., Three decades of research on computer applications in health care medical informatics support at the agency for healthcare research and quality. J. Am. Med. Inform. Assoc. 9:144–160, 2002. doi:10.1197/jamia.M0867.CrossRefGoogle Scholar
  2. 2.
    Fuh, C. S., and Kuo, K. L., The decision support system used in HEALS (Health Examination Automatic Logic System). 2006 American Medical Informatics Association Spring Congress Poster. May 16–18, 2006.Google Scholar
  3. 3.
    Kuo, K. L., and Fuh, C. S., HEALS: Health Examination Automatic Logic System. 2006 American Medical Informatics Association Spring Congress Poster. May 16–18, 2006.Google Scholar
  4. 4.
    Canadian Task Force on Preventive Health Care, Evidence-based clinical prevention. 2000.Google Scholar
  5. 5.
    Canadian Task Force on the Periodic Health Examination: Periodic health examination monograph: report of the Task Force to the Conference of Deputy Ministers of Health. 1980.Google Scholar
  6. 6.
    Agency for Healthcare Research and Quality, Guide to Clinical Preventive Services, 2006: Recommendations of the U.S. Preventive Services Task Force. AHRQ Publication No. 05-0570. 2006.Google Scholar
  7. 7.
    Ohno-Machado, L., Gennari, J. H., Murphy, S. N., et al., The GuideLine interchange format: a model for representing guidelines. J. Am. Med. Inform. Assoc. 5:357–372, 1998.Google Scholar
  8. 8.
    Wang, D., Peleg, M., Tu, S. W., et al., Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models. Int. J. Med. Inform. 68:59–70, 2002. doi:10.1016/S1386-5056(02)00065-5.CrossRefGoogle Scholar
  9. 9.
    Bennett, J. W., and Glasziou, P. P., Computerised reminders and feedback in medication management: a systematic review of randomised controlled trials. Med. J. Aust. 178:217–222, 2003.Google Scholar
  10. 10.
    Casalino, L., Gillies, R. R., Shortell, S. M., et al., External incentives, information technology, and organized processes to improve health care quality for patients with chronic diseases. JAMA. 289:434–441, 2003. doi:10.1001/jama.289.4.434.CrossRefGoogle Scholar
  11. 11.
    Chaudhry, B., Wang, J., Wu, S., et al., Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann. Intern. Med. 144:742–770, 2006.Google Scholar
  12. 12.
    Jiang, S. T., How information technologies have improved both productivity and quality of health care. J. Med. Eng. Technol. 29:38–41, 2005. doi:10.1080/03091900412331271130.CrossRefGoogle Scholar
  13. 13.
    Nobel, J., Bridging the knowledge-action gap in diabetes: information technologies, physician incentives and consumer incentives converge. Chronic Illn. 2:59–60, 2006.Google Scholar
  14. 14.
    Sequist, T. D., Cullen, T., and Ayanian, J. Z., Information technology as a tool to improve the quality of American Indian health care. Am. J. Public Health. 95:2173–2179, 2005. doi:10.2105/AJPH.2004.052985.CrossRefGoogle Scholar
  15. 15.
    Sim, I., Gorman, P., Greenes, R. A., et al., Clinical decision support systems for the practice of evidence-based medicine. J. Am. Med. Inform. Assoc. 8:527–534, 2001.Google Scholar
  16. 16.
    Evans, R. S., Pestotnik, S. L., Classen, D. C., et al., A computer-assisted management program for antibiotics and other antiinfective agents. N. Engl. J. Med. 338:232–238, 1998. doi:10.1056/NEJM199801223380406.CrossRefGoogle Scholar
  17. 17.
    Hunt, D. L., Haynes, R. B., Hanna, S. E., et al., Effects of computer-based clinical decision support systems on physician performance and patient outcomes a systematic review. JAMA. 280:1339–1346, 1998. doi:10.1001/jama.280.15.1339.CrossRefGoogle Scholar
  18. 18.
    Johnston, M. E., Langton, K. B., Haynes, R. B., et al., Effects of computer-based clinical decision support systems on clinician performance and patient outcome: a critical appraisal of research. Ann. Intern. Med. 120:135–142, 1994.Google Scholar
  19. 19.
    Osheroff, J. A., Teich, J. M., Middleton, B., et al., A roadmap for national action on clinical decision support. J. Am. Med. Inform. Assoc. 14:141–145, 2007. doi:10.1197/jamia.M2334.CrossRefGoogle Scholar
  20. 20.
    Teich, J. M., Osheroff, J. A., Pifer, E. A., et al., Clinical decision support in electronic prescribing: recommendations and an action plan report of the joint clinical decision support workgroup. J. Am. Med. Inform. Assoc. 12:365–376, 2005. doi:10.1197/jamia.M1822.CrossRefGoogle Scholar
  21. 21.
    Rector, A. L., Thesauri and formal classifications: terminologies for people and machines. Methods Inf. Med. 37:501–509, 1998.Google Scholar
  22. 22.
    Rosenbloom, S. T., Miller, R. A., Johnson, K. B., et al., Interface terminologies: facilitating direct entry of clinical data into electronic health record systems. J. Am. Med. Inform. Assoc. 13L:277–288, 2006. doi:10.1197/jamia.M1957.CrossRefGoogle Scholar
  23. 23.
    Russell, S. J., and Norvig, P., Artificial intelligence: a modern approach, 2nd edition. Prentice Hall, Englewood Cliffs, NJ, New Jersey, 2003.Google Scholar
  24. 24.
    Kantor, G. S., Wilson, W. D., and Midgley, A., Open-source software and the primary care EMR. J. Am. Med. Informat. Assoc. 10:616, 2003.CrossRefGoogle Scholar
  25. 25.
    The, P.H.P.: Group, PHP: Hypertext Preprocessor. [cited on Dec. 27, 2008]. Available from: http://www.php.net/.
  26. 26.
    Sun Microsystems, Inc.: About the Java Technology. [cited on Dec. 27, 2008]. Available from: http://java.sun.com/docs/books/tutorial/getStarted/intro/definition.html.
  27. 27.
    W3C, Extensible Markup Language (XML). [cited on Dec. 27, 2008]. Available from: http://www.w3.org/XML/.
  28. 28.
    The Apache Software Foundation, The Apache HTTP Server Project.: [cited on Dec. 27, 2008]. Available from: http://httpd.apache.org/.
  29. 29.
    The Apache Software Foundation: Apache Tomcat. [cited on Dec. 27, 2008]. Available from: http://tomcat.apache.org/.
  30. 30.
    Linux Online, Inc.: What is Linux. [cited on Dec. 27, 2008]. Available from: http://www.linux.org/info/index.html.
  31. 31.
    Lhotska, L., Marik, V., and Vlcek, T., Medical applications of enhanced rule-based expert systems. Int. J. Med. Inform. 63:61–75, 2001. doi:10.1016/S1386-5056(01)00172-1.CrossRefGoogle Scholar
  32. 32.
    Achour, S., Dojat, M., Rieux, C., et al., A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development. J. Am. Med. Inform. Assoc. 8:351–360, 2001.Google Scholar
  33. 33.
    Saade, R., Tsoukas, A., and Tsoukas, G., Prototyping a decision support system in the clinical environment: assessment of patients with osteoporosis OSTEODSS. Expert Syst. Appl. 27:427–438, 2004. doi:10.1016/j.eswa.2004.05.019.CrossRefGoogle Scholar
  34. 34.
    Huang, M., and Chen, M., Integrated design of the intelligent web-based Chinese Medical Diagnostic System (CMDS)—systematic development for digestive health. Expert Syst. Appl. 32:658–673, 2007. doi:10.1016/j.eswa.2006.01.037.CrossRefGoogle Scholar
  35. 35.
    DeLone, W. H., and McLean, E. R., Information systems success: the quest for the dependent variable. Inf. Syst. Res. 3:60–95, 1992. doi:10.1287/isre.3.1.60.CrossRefGoogle Scholar
  36. 36.
    Meijden, M. J. v. d., Tange, H. J., Troost, J., et al., Determinants of success of inpatient clinical information systems: a literature review. J. Am. Med. Inform. Assoc. 10:235–243, 2003. doi:10.1197/jamia.M1094.CrossRefGoogle Scholar
  37. 37.
    Balas, E. A., Information systems can prevent errors and improve quality. J. Am. Med. Inform. Assoc. 8:398–399, 2001.Google Scholar
  38. 38.
    McRoy, S. W., and Hirst, G., Misunderstanding and the negotiation of meaning. Knowl.-Based Syst. 8:126–134, 1995. doi:10.1016/0950-7051(95)98374-F.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Family Medicine Department, RenAi BranchTaipei City HospitalTaipei CityRepublic of China
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipei CityRepublic of China

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