Journal of the Korea Safety Management & Science (대한안전경영과학회지)

Korea Safety Management & Science (대한안전경영과학회)
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Application of Quality Statistical Techniques Based on the Review and the Interpretation of Medical Decision Metrics

의학적 의사결정 지표의 고찰 및 해석에 기초한 품질통계기법의 적용

  • Choi, Sungwoon (Department of Industrial Engineering, Gachon University)
  • 최성운 (가천대학교 산업공학과)
  • Received : 2013.01.09
  • Accepted : 2013.03.13
  • Published : 2013.06.30
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Abstract

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of ${\alpha}$ and ${\beta}$. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

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References

  1. Campbell M.J., Machin D., Walters S.J.(2007), Medical Statistics : A Textbook for the Health Sciences, 4 Edition, Wiley.
  2. Felder S., Mayrhofer T.(2011), Medical Decision Making : A Health Economic Primer, Springer.
  3. Hauschke D., Steinijans V., Pigeot I.(2007), Bioequivalence Studies in Drug Development : Methods and Applications, Wiley.
  4. Kanane J.B.(2008), Statistics in the Law : A Practioner's Guide, Cases, and Materials, Oxford University Press.
  5. Lee B.(2006), "Medical Decision Making Tools: Bayesian Analysis and ROC Analysis", Imaging Science in Dentistry 36, 1-5.
  6. Levinson W.A.(2010), Statistical Process Control for Real-World Applications, CRC Press.
  7. Montgomery D.C.(2012), Statistical Quality Control, 7 Edition, Wiley.
  8. Mukherjee A.(2010), Credit Scoring Model Using Data Mining Techniques : A Pragmatic Approach, VDM Verlag.
  9. Patterson S., Jones B.(2005), Bioequivalence and Statistical in Clinical Pharmacology, Chapman and Hall/CRC.
  10. Peat J., Barton B.(2005), Medical Statistics : A Guide to Data Analysis and Critical Appraisal, BMJ Books.
  11. Ryan T.P.(2011), Statistical Methods for Quality Improvement, 3 Edition, 2011.
  12. Shmueli G.(2011), Practical Acceptance Sampling : A Hands-On Guide, 2 Edition, Createspace Independent Publishing Platform.
  13. Sox H., et al.(2006), Medical Decision Making, The American College of Physicians.
  14. Stephens K.S.(2001), The Handbook of Applied Acceptance Sampling : Plans, Procedures & Principles, American Society for Quality.
  15. Thomas L.C., Edelman D.B, Crook J.N.(2002), Credit Scoring & Its Applications, Society for Industrial and Applied Mathematics.
  16. Walker J.T., Maddan S.(2008), Statistics in Criminology and Criminal Justice : Analysis and Interpretation, Jones & Bartlett Learning.
  17. Wheele D.J.(2010), Understanding Statistical Process Control, 3 Edition, SPC Press.