Skip to main content
Log in

Incorporating intelligent risk detection to enable superior decision support: the example of orthopaedic surgeries

  • Original Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Decision making in healthcare is unstructured, complex and critical. Today, healthcare professionals are continually under immense time pressure to make appropriate treatment decisions which in turn have far reaching implications on the quality of outcomes. 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. In this application data mining tools in combination with Knowledge Discovery (KD) techniques are used to score the surgery risk levels, assess surgery risks and help medical professionals to make appropriate and superior complex surgical decisions for each patient. To illustrate the benefits of such intelligent risk detection to improve decision efficacy in healthcare contexts we focus within the context of Orthopaedic Surgeries, specifically on hip and knee surgeries. This paper concludes with a conceptual model to move successfully from idea to design and then implementation.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. A commercial software for data mining

  2. An open source software for data mining

References

  1. Wickramasinghe N, Bali RK, Choi CJHJ, Schaffer JL. A Systematic Approach Optimization of Healthcare Operations with Knowledge Management. USA: HIMSS; 2009.

    Google Scholar 

  2. Wickramasinghe N, Schaffer J. Creating knowledge-driven healthcare process with the Intelligence Continuum. Int J Elec Healthc. 2006;2:164–74.

    Google Scholar 

  3. Davidson D, Steiger RD, Ryan P, Griffith L, Mcdermott B, Pratt N, Miller L, Stanford T. Hip and Knee Arthroplasty. Annual report. In: Data Management & Analysis Centre. Adelaide: University of Adelaide; 2008.

    Google Scholar 

  4. Dijkman BA. Decision making open reduction/internal fixation versus arthroplasty for femoral neck fractures. Tech Orthop. 2008;23:288–95.

    Article  Google Scholar 

  5. Cios KJ, Pedrycz W, Swiniarski RW and Kurgan RA. Date mining and knowledge discovery approach. Springer; 2007.

  6. Miller RA. Medical diagnostic decision support systems- past, present, and future: a threaded bibliography and brief commentary. J Am Med Informatics Assoc. 1994;1:8–27.

    Article  Google Scholar 

  7. Fieschi M, Dufour JC, Staccini P, Gouvernet J, Bouhaddou O. Medical Decision Support Systems: Old Dilemmas and new Paradigms? Tracks for Successful Integration and Adoption. Methods Inf Med. 2003;3(2003):191–8.

    Google Scholar 

  8. Hunt DL, Haynes B, Hanna SE. Effects of Computer-Based Clinical Decision Support Outcomes: A Systematic Review System on Physician Performance and Patient. JAMA. 1998;280:1339–46.

    Article  Google Scholar 

  9. Garg AX, Adhikari NKJ, McDonald H, AL E. Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes A Systematic Review. JAMA. 2005;293:1223–38.

    Article  Google Scholar 

  10. Reddy V, McElhinney D, Silverman N, Hanley F. The double switch procedure for anatomical repair of congenitally corrected transposition of the great arteries in infants and children. Eur Heart J. 1997;18:1470–7.

    Google Scholar 

  11. Stamatis G. Intensively lowering glucose: possible benefits must be weighed against risks. In Centre UHCM, editor. 2010.

  12. Roy CB, Brunton S. Managing multiple cardiovascular risk factors. J Fam Pract. 2008;57:13–20.

    Google Scholar 

  13. Moghimi FH, Wickramasinghe N, Zadeh HS. An Intelligence Risk Detection Framework To Improve The Efficiency Of The Decision Making Process In Healthcare. Hawaii: HICSS; 2011.

    Google Scholar 

  14. Baldwin, J. (2001) Automating patients’ records. MD computing [cited May 2010]; Available from: www.technologyinpractice.com.

  15. Gayet FL. Risk stratification theme for congenital heart surgery. Semin Thorac Cardiovasc Surg Pediatr Card Surg Annu. 2002;5:148–52.

    Article  Google Scholar 

  16. Kang N, Tsang V, Cole T, Elliott M, Leval MD. Risk stratification in paediatric open-heart surgery. Eur J Cardiothorac Surg. 2004;26:3–11.

    Article  Google Scholar 

  17. Gayet FL, Jacobs J. Performance of surgery for congenital heart disease: shall we wait a generation or look for different statistics? J Thorac Cardiovasc Surg. 2005;130:234.

    Article  Google Scholar 

  18. Larrazabal LA, Pedro JDN, Kathy JJ, Gauvreau K. Measurement of Technical Performance in Congenital Heart Surgery: A Pilot study. Ann Thorac Surg. 2007;83:179–84.

    Article  Google Scholar 

  19. Keenan P, Beeuwkes B, Mcguire T, Newhouse J. The Prevalence of Formal Risk Adjustment in Health Plan Purchasing. Inquiry. 2001;38:245–59.

    Article  Google Scholar 

  20. Weiner JP, Dobson A, Maxwell SL. Risk-Adjusted Medicare Capitation Rates Using Ambulatory and Inpatient Diagnoses. Health Care Finan Rev. 1996;17:77–100. Spring.

    Google Scholar 

  21. Dunn DL. Applications of health risk adjustment: what can be learned from experience to date? Inquiry. Summer. 1998;35:132–47.

    Google Scholar 

  22. Ash AS, Ellis RP, Pope GP, Ayanian JZ, Bates DW, Burstin H, Lezzoni LI, MacKay E, Yu V. “Using Diagnoses to Describe Populations and Predict Costs,”. Health Care Finan Rev. 2000;21(3). spring.

  23. Pope GC, Ellis RE, Ash AS. Principal Inpatient Diagnostic Cost Group Model for Medicare Risk Adjustment. Health Care Finan Rev. 2000;21(3):93–118. Spring.

    Google Scholar 

  24. Kronick R, Gilmer T, Dreyfus T and Ganiats T “CDPS-Medicare: The chronic illness and disability payment system modified to predict expenditures for medicare beneficiaries” final report to CMS, June 24, 2002.

  25. Hughes JS, Averill RF, Eisenhandler J. Clinical Risk Groups (CRGs): A Classification System for Risk-Adjusted Capitation-Based Payment and Health Care Management. Med Care. 2004;42:81–90.

    Article  Google Scholar 

  26. Kapur K, Tseng C-W, Rastegar A, Carte G, Keeler E. Medicare Calibration of the Clinically Detailed Risk Information System for Cost. Health Care Finan Rev. 2003;25:37–54.

    Google Scholar 

  27. Hornbrook MC. Development of GRAM to support risk adjusted community rating. HCFA project report. 1996.

  28. Mavroudis C, Jacobs JP. Congenital heart disease outcome analysis: Methodology and rationale. J Thorac Cardiovasc Surg. 2002;123:7.

    Google Scholar 

  29. Kumar A and Gosain A. “Analysis of health care data using different data mining techniques.” JAMA. 2009.

  30. Palaniappan S and Awang R. Intelligent Heart disease prediction system using data mining techniques. IEEE. 2008.

  31. Candelieri A Conforti D. Knowledge discovery approaches for early detection of decompensation conditions in heart failure patients. Ninth International conference on intelligent systems design and applications IEEE. 2009.

  32. Wickramasinghe N, Troshani I, Rao S, Hague W. and Goldberg S. A transaction cost assessment of a pervasive technology solution for gestational diabetes, IJHISI (Intl J Healthc Inf Syst Informatics, 2011 in press).

  33. Zwicker M, Seitz J, Wickramasinghe N. Critical People considerations when designing e-Health solutions: the importance of Barrier-free e-Kiosk systems, IJBET (Intl J Biomed Eng Technol). 2011.

  34. Wickramasinghe N, Geisler E, Schaffer J. “Realizing The value Proposition for Healthcare By Incorporating KM strategies and Data Mining Techniques with the use of information communication technologies”. Int J Healthc Technol Manag. 2006;7(3).

  35. Porter M, Teisberg EO. Redefining Health Care: Creating Value-Based Competition on Results. Boston: Harvard Business School Press; 2006.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nilmini Wickramasinghe.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moghimi, H., Zadeh, H., Schaffer, J. et al. Incorporating intelligent risk detection to enable superior decision support: the example of orthopaedic surgeries. Health Technol. 2, 33–41 (2012). https://doi.org/10.1007/s12553-011-0014-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-011-0014-z

Keywords

Navigation