An Intelligence e-Risk Detection Model to Improve Decision Efficiency in the Context of the Orthopaedic Operating Room

  • Fatemeh Hoda Mogihim
  • Hossein Zadeh
  • Nilmini Wickramasinghe
Part of the Healthcare Delivery in the Information Age book series (Healthcare Delivery Inform. Age)


Decision making in healthcare is unstructured, complex and critical. Today, given the healthcare professionals are continually under immense time pressure to make appropriate treatment decisions. Moreover, in order to make such decisions it is necessary for them to process large amounts of disparate data and information. We contend that such a context is appropriate for the application of real time intelligent risk detection decision support system. To illustrate the benefits of risk detection to improve decision efficacy in healthcare contexts we focus on the case of the orthopaedic operating room for hip and knee replacements. In the orthopaedic operating room complex high risk decisions must be made which have for reaching implications on the success of the surgery and ongoing quality of life of the patient.


Data mining Decision support system Healthcare systems Intelligent risk detection decision Knowledge discovery 


  1. Ash, A.S., Ellis, R.P., Pope, G.P., Ayanian, J.Z., Bates, D.W., Burstin, H., Iezzoni, L.I., MacKay, E. and Yu, V. (2000) “Using Diagnoses to Describe Populations and Predict Costs,” Health Care Financing Review, 21(3), spring.Google Scholar
  2. Baldwin, J. (2001) Automating patients’ records. MD computing [cited May 2010]; Available
  3. Candelieri, A., Conforti, D., Sciacqua, A., & Perticone, F. (2009). Knowledge discovery approaches for early detection of decompensation conditions in heart failure patients. In Ninth international conference on intelligent systems design and applications. IEEE.Google Scholar
  4. Cios, K. J., Pedrycz, W., Swiniarski, R. W. & Kurgan, R. A. (2007) Date mining and knowledge discovery approach, SpringerGoogle Scholar
  5. Davidson, D., de Steiger, R., Ryan, P., Griffith, L., McDermott, B., Pratt, N. et al. (2008). Hip and knee arthroplasty. In Annual report. Adelaide: Data Management & Analysis Centre and University of Adelaide.Google Scholar
  6. Dijkman, B. A., Kooistra, B. A., Ferguson, T. B., & Bhandari, M. A. C. (2008). Decision making open reduction/internal fixation versus arthroplasty for femoral neck fractures. Techniques in Orthopaedics, 23(4), 288–295.CrossRefGoogle Scholar
  7. Dunn DL. (1998) Applications of health risk adjustment: what can be learned from experience to date? Inquiry. Summer; 35,132–147.Google Scholar
  8. Fieschi, M., Dufour, J. C., Staccini, P., Gouvernet, J., & Bouhaddou, O. (2003). Medical decision support systems: Old dilemmas and new paradigms? Tracks for successful integration and adoption. Methods of Information in Medicine, 42(3), 191–198.Google Scholar
  9. Garg, A. X., Adhikari, N. K. J., McDonald, H., et al. (2005). Effects of computerized clinical decision support systems on practitioner performance and patient outcomes a systematic review. JAMA, 293(10), 1223–1238.PubMedCrossRefGoogle Scholar
  10. Gayet, F. L., Jacobs, J. P., et al. (2005). Performance of surgery for congenital heart disease: Shall we wait a generation or look for different statistics? The Journal of Thoracic and Cardiovascular Surgery, 130, 234.Google Scholar
  11. Hornbrook M.C. & Goodman M.J. (1996) Chronic disease, functional health status, and demographics: a multi-dimensional approach to risk adjustment. Health Service Research Journal. 1996 August; 31(3), 283–307.Google Scholar
  12. Hughes, J.S., Averill, R.F., Eisenhandler, J. (2004) Clinical Risk Groups (CRGs): A Classification System for Risk-Adjusted Capitation-Based Payment and Health Care Management. Medical Care 42, 81–90.PubMedCrossRefGoogle Scholar
  13. Hunt, D. L., Brian Haynes, R., Hanna, S. E., et al. (1998). Effects of computer-based clinical decision support outcomes: A systematic review systems on physician performance and patient. JAMA, 280(15), 1339–1346.PubMedCrossRefGoogle Scholar
  14. Kang, N., Tsang, V., Cole, T., Elliott, M., & de Leval, M. (2004). Risk stratification in paediatric open-heart surgery. European Journal of Cardio-Thoracic Surgery, 26, 3–11.PubMedCrossRefGoogle Scholar
  15. Keenan, P., Buntin Beeuwkes, M., McGuire, T., & Newhouse, J. P. (2001). The prevalence of formal risk adjustment in health plan purchasing. Inquiry, 38, 245–259.PubMedCrossRefGoogle Scholar
  16. Kronick, R., Gilmer, T., Dreyfus, T. and Ganiats, T. (2002)“CDPS-Medicare: The Chronic Illness and Disability Payment System Modified to Predict Expenditures for Medicare Beneficiaries” Final Report to CMS, June 24, 2002.PubMedCrossRefGoogle Scholar
  17. Kumar, A. and Gosain A. (2009) Analysis of Health Care Data Using Different Data Mining Techniques., International Conference on Intelligent Agent & Multi-Agent Systems(IAMA), Chennai, Print ISBN: 978-1-4244-4710-7. P1–6.Google Scholar
  18. Lacour-Gayet, F. (2002). Risk stratification theme for congenital heart surgery. Seminars in Thoracic and Cardiovascular Surgery. Pediatric Cardiac Surgery Annual, 5, 148–152.PubMedCrossRefGoogle Scholar
  19. Larrazabal, L. A., del Nido Pedro, J., Jenkins Kathy, J., & Gauvreau, K. (2007a). Measurement of technical performance in congenital heart surgery: A pilot study. The Annals of Thoracic Surgery, 83, 179–184.PubMedCrossRefGoogle Scholar
  20. Larrazabal, L. A., Jenkins, K. J., Gauvreau, K., Vida, V. L., Benavidez, O. J., Gaitán, G. A., et al. (2007b). Improvement in congenital heart surgery in a developing country: The Guatemalan experience. Circulation, 116, 1872–1877.CrossRefGoogle Scholar
  21. Mavroudis, C. & Jacobs, J. P. (2002) Congenital heart disease outcome analysis: Methodology and rationale. The Journal of Thoracic and Cardiovascular Surgery, 123( 7), 35–47.PubMedCentralPubMedCrossRefGoogle Scholar
  22. Miller, R. A. (1994). Medical diagnostic decision support systems – past, present, and future: A threaded bibliography and brief commentary. Journal of the American Medical Informatics Association, 1, 8–27.PubMedCentralPubMedCrossRefGoogle Scholar
  23. Palaniappan, S. and Awang R. (2008). Intelligent Heart Disease Prediction System Using Data Mining Techniques, International Conference on Computer Systems and Applications (AICCSA). IEEE/ACS, Doha, Print ISBN: 978-1-4244-1967-8, p. 108–115Google Scholar
  24. Pope, G.C., Ellis, R.E, Ash, A.S., (2000) Principal Inpatient Diagnostic Cost Group Model for Medicare Risk Adjustment. Health Care Financing Review 21(3):93–118, Spring.Google Scholar
  25. Reddy, V. M., McElhinney, D. B., Silverman, N. H., & Hanley, F. L. (1997). The double switch procedure for anatomical repair of congenitally corrected transposition of the great arteries in infants and children. European Heart Journal, 18(9), 1470–1477.PubMedCrossRefGoogle Scholar
  26. Roy, C. B., & Brunton, S. (2008). Managing multiple cardiovascular risk factors. The Journal of Family Practice, 57(3), 13–20.Google Scholar
  27. Stamatis, G. (2010). Intensively lowering glucose: Possible benefits must be weighed against risks. ed. University Hospitals Case Medical Center.Google Scholar
  28. Ting, I.-H., Hornbrook Hui-Ju, W. (2009). Web mining applications in e-commerce and e-services. Berlin: Springer.Google Scholar
  29. Wickramasinghe, N., Bali, R. K., Capt. James Choi, J. H., & Schaffer, J. L. (2009a). A systematic approach optimization of healthcare operations with knowledge management. In HIMSS, USA.Google Scholar
  30. Wickramasinghe, N., Bali, R., Gibbons, C., Choi, C., & Schaffer, J. (2009b). Optimization of health care operations with knowledge management. JHIMS, 3(4), 44–50.Google Scholar
  31. Wickramasinghe, N., & Schaffer, J. (2006). Creating knowledge-driven healthcare process with the intelligence continuum. Int J.Electronic Healthcare, 2, 164–174.PubMedGoogle Scholar
  32. Wilson, I. B., & Cleary, P. D. (1995). Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. Journal of the American Medical Association, 273, 59–65. PubMedCrossRefGoogle Scholar
  33. Weiner, J.P., Dobson, A., Maxwell, S.L., (1996) Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses. Health Care Financing Review 17, 77–100, Spring.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Fatemeh Hoda Mogihim
    • 1
  • Hossein Zadeh
    • 1
  • Nilmini Wickramasinghe
    • 1
  1. 1.School of Business IT and LogisticsRMIT UniversityMelbourneAustralia

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