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Statistical Quality Control Methods in Infection Control and Hospital Epidemiology, Part II: Chart Use, Statistical Properties, and Research Issues

Published online by Cambridge University Press:  02 January 2015

David Birnbaum  and
James C. Benneyan
David Birnbaum
Affiliation:
Northeastern University, Boston, Massachusetts
James C. Benneyan*
Affiliation:
Northeastern University, Boston, Massachusetts
*
Mechanical, Industrial, and Manufacturing Department, 334 Snell Engineering Center, Northeastern University, Boston, MA 02115
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Abstract

This is the second in a two-part series discussing and illustrating the application of statistical process control (SPC) in hospital epidemiology. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand the mutual understanding and cross-fertilization between these two disciplines. Part I provided an overview of the philosophy and general approach of SPC, illustrated common types of control charts, and provided references for further information or statistical formulae. Part II now discusses alternate possible SPC approaches, statistical properties of control charts, chart-design issues and optimal control limit widths, some common misunderstandings, and more advanced issues. The focus of both articles is mostly nonmathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 19 , Issue 4 , April 1998 , pp. 265 - 283
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1998

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References

REFERENCES

1. Benneyan, JC. Statistical quality control methods in infection control and hospital epidemiology, part I: introduction and basic theory. Infect Control Hosp Epidemiol 1998;19:194–214.Google Scholar
2. Duncan, AJ. Quality Control and Industrial Statistics. 5th ed. New York, NY: Richard D. Irwin, Inc; 1986.Google Scholar
3. Montgomery, DC. Introduction to Statistical Quality Control. 2nd ed. New York, NY: John Wiley and Sons, Inc; 1991.Google Scholar
4. Gitlow, H, Gitlow, S, Oppenheim, A, Oppenheim, R. Tools and Methods for the Improvement of Quality. New York, NY: Richard D. Irwin, Inc; 1989.Google Scholar
5. Deming, WE. Quality, Productivity, and Competitive Position. Cambridge, MA: Massachusetts Institute of Technology Center for Advanced Engineering Studies; 1982.Google Scholar
6. Neave, HR. The Deming Dimension. Knoxville, TN: SPC Press Inc; 1990.Google Scholar
7. Juran, JM, Gryna, FM, eds. Juran's Quality Control Handbook. 4th ed. New York, NY: McGraw-Hill Book Co; 1988.Google Scholar
8. Benneyan, JC. Using statistical process control (SPC) to improve health care. Quality Management in Health Care. In press.Google Scholar
9. Al-Assaf, AF, Schmele, JA, eds. The Textbook of Total Quality in Healthcare. Delray Beach, FL: St Lucie Press; 1993.Google Scholar
10. Berwick, DM, Godfrey, AB, Roessner, J. Curing Health Care: New Strategies for Quality Improvement. San Francisco, CA: Jossey-Bass; 1990.Google Scholar
11. Shewhart, WA. Statistical Method From the Viewpoint of Quality Control. Washington, DC: Lancaster Press, Inc; 1939.Google Scholar
12. Shewhart, WA. The Economic Control of Quality of Manufactured Product. New York, NY: D. Van Nostand and Co; 1931.Google Scholar
13. Burnett, L, Chesher, D. Applications of CQI tools to the reduction in risk of needlestick injury. Infect Control Hosp Epidemiol 1995;16:503–505.CrossRefGoogle Scholar
14. Pohlen, T. Statistical thinking_a personal application. ASQC Statistics Division Newsletter 1996;18–23.Google Scholar
15. Woodall, WH, Crowder, SV, Wade, MR. Discussion: ‘geometric Q charts for high quality processes’. Journal of Quality Technology 1995;27:328–332.CrossRefGoogle Scholar
16. Zimmerman, SM, Brown, LD, Brown, SS, Alexander, L. Human body function control charts for the physician. ASQC Annual Quality Congress Transactions 1990;408–412.Google Scholar
17. Benneyan, JC. Measuring health care quality: a statistical quality management perspective. Total Quality: Creating Individual and Corporate Success In: Ahluwalia, JS, ed. New Delhi, India: Excel Books; 1996.Google Scholar
18. Benneyan, JC. Design of statistical gcontrol charts for nosocomial infection and other alternatives. International Applied Statistics in Medicine Conference Transactions. In press.Google Scholar
19. McGuckin, MB, Abrutyn, E. A surveillance method for early detection of nosocomial outbreaks. Am J Infect Control 1979;7:18–21.Google Scholar
20. Birnbaum, DW. Analysis of hospital surveillance data. Infect Control 1984;5:332–338.CrossRefGoogle Scholar
21. Al-Salti, M, Statham, A. A review of the literature on the use of spc in batch production. Quality and Reliability Engineering International 1994;10:49–61.Google Scholar
22. Quesenberry, CP. Q charts for high quality processes. Journal of Quality Technology 1995;27:304–315.Google Scholar
23. Burr, IW. Short runs. Industrial Quality Control 1954;9(2):16–22.Google Scholar
24. Yang, CH, Hillier, FS. Mean and variance control chart limits based on a small number of subgroups. Journal of Quality Technology 1970;2:9–16.Google Scholar
25. Wheeler, DJ. Short Run SPC. Knoxville TN: SPC Press, Inc; 1991.Google Scholar
26. Nelson, EC, Batalden, PB, Plume, SK, Mihevec, NT, Swartz, WG. Report cards or instrument panels: who needs what? Journal on Quality Improvement 1995;21(16):155–166.Google Scholar
27. Finison, LJ, Spencer, M, Finison, KS. Total quality measurement in health care: using individuals charts in infection control. ASQC Annual Quality Congress Transactions 1993;349–359.Google Scholar
28. Craig, CC. Note on the use of fixed numbers of defectives and variable sample sizes in sampling attributes. Industrial Quality Control 1953;9:43–45.Google Scholar
29. Benneyan, JC. Statistical Control Charts Based on Geometric and Negative Binomial Distributions. Amherst, MA: University of Massachusetts; 1992. Thesis.Google Scholar
30. Lucas, JM. Counted data Cusums. Technometrics 1985;27:129–144.CrossRefGoogle Scholar
31. Bourke, PD. Detecting a shift in fraction nonconforming using run-length control charts with 100% inspection. Journal of Quality Technology 1991;23:225–238.Google Scholar
32. Xie, M, Goh, TN. Improvement detection by control charts for high yield processes. International Journal of Quality and Reliability Management 1993;10:24–31.CrossRefGoogle Scholar
33. Kaminsky, FC, Benneyan, JC, Davis, RB, Burke, RJ. Statistical control charts based on a geometric distribution. Journal of Quality Technology 1992;24:63–69.CrossRefGoogle Scholar
34. Benneyan, JC, Kaminsky, FC. Modeling discrete data in SPC: the gand h control charts. ASQC Annual Quality Congress Transactions 1994;32–42.Google Scholar
35. Benneyan, JC. The importance of modeling discrete data in SPC. Proceedings of the Tenth International Conference of the Israel Society for Quality Jerusalem, Israel: Israel Society for Quality. 1994;640–646.Google Scholar
36. Jacquez, GM, Waller, LA, Grimson, R, Wartenberg, D. On disease clustering, part I: state of the art. Infect Control Hosp Epidemiol 1996;17:319–327.CrossRefGoogle ScholarPubMed
37. Sheaffer, RL, Leavenworth, RS. The negative binomial model for counts in units of varying size. Journal of Quality Technology 1976;8:158–163.Google Scholar
38. Duncan, AJ. A chi-square chart for controlling a set of percentages. Industrial Quality Control 1950;6(11):11–15.Google Scholar
39. Birnbaum, DW. CQI tools: sentinel events, warning, and action limits. Infect Control Hosp Epidemiol 1993;14:537–539.CrossRefGoogle ScholarPubMed
40. Weiler, H. The use of runs to control the mean in quality control. Journal of the American Statistical Association 1953;48:816–825.CrossRefGoogle Scholar
41. Walker, E, Philpot, JW, Clement, J. False signal rates for the Shewhart control chart with supplementary runs tests. Journal of Quality Technology 1991;23:247–252.Google Scholar
42. Charnes, JM. Tests for special causes with multivariate autocorrelated data. Computers in Operations Research 1995;22:443–453.Google Scholar
43. Alemi, F, Rom, W, Eisenstein, E. Risk adjusted control charts for health care assessment. Annals of Operations Research 1996;67:45–60.Google Scholar
44. Birnbaum, D. Analysis of infection control surveillance data in a long-term-care facility: use of threshold settings. Infect Control Hosp Epidemiol 1996;17:348–349.Google Scholar
45. Mylotte, JM. Analysis of infection control surveillance data in a long-term care facility: use of threshold settings. Infect Control Hosp Epidemiol 1996;17:101–107.Google Scholar
46. Childress, JA, Childress, JD. Statistical test for possible infection outbreaks. Infect Control 1981;2:247–249.Google Scholar
47. Weiler, H. On the most economic sample size for controlling the mean of a population. Annals of Mathematical Statistics 1952;23:247–254.CrossRefGoogle Scholar
48. McConnell, CR. Frequency of process sampling under constraints. ASQC Annual Quality Congress Transactions 1984;438–444.Google Scholar
49. Duncan, AJ. The economic design of X-charts used to maintain current control of a process. Journal of the American Statistical Association 1956;51:228–242.Google Scholar
50. Montgomery, DC. Economic design of an X control chart. Journal of Quality Technology 1982;14:40–43.Google Scholar
51. Lorenzen, TJ, Vance, LC. The economic design of control charts: a unified approach. Technometrics 1986;28:3–10.CrossRefGoogle Scholar
52. Duncan, AJ. The economic design of X-charts when there is a multiplicity of assignable causes. Journal of the American Statistical Association 1971;66:107–121.Google Scholar
53. Chiu, WK, Cheng, KC. An economic study of X-charts with warning limits. Journal of Quality Technology 1977;9:166–171.Google Scholar
54. Montgomery, DC. The economic design of control charts: a review and literature survey. Journal of Quality Technology 1980;12:75–87.Google Scholar
55. Gibra, IN. Optimal control of processes subject to linear trends. Journal of Industrial Engineering 1967;18:35–41.Google Scholar
56. Gordon, GG, Weindling, JI. A cost model for economic design of warning limit control chart schemes. AIIE Transactions 1975;7:319–329.Google Scholar
57. Koska, MT. Using CQI methods to lower post-surgical wound infection rate. Hospitals 1992;66:62–64.Google Scholar
58. Duncan, AJ. The economic design of p-charts to maintain current control of a process: some numerical results. Technometrics 1978;20:235–244.Google Scholar
59. Gibra, IN. Economically optimal determination of the parameters of np-control charts. Journal of Quality Technology 1978;10:12–19.Google Scholar
60. Chiu, WK. Economic design of attribute control charts. Technometrics 1975;17:81–88.Google Scholar
61. Montgomery, DC, Heikes, RG, Mance, JF. Economic design of fraction defective control charts. Management Science 1975;21:1272–1284.Google Scholar
62. Chiu, WK. Economic design of np-charts for processes subject to a multiplicity of assignable causes. Management Science 1976;23:404–411.CrossRefGoogle Scholar
63. King, EP. The operating characteristics of the control chart for sample means. Annals of Mathematical Statistics 1952;23:384–395.CrossRefGoogle Scholar
64. Olds, EG. The Power to Detect a Single Slippage and the Probability of a Type I Error of the Upper Three-Sigma Limit Control Chart for Fraction Defective: No Standard Given. Pittsburgh, PA: Carnegie Institute of Technology, Department of Mathematics, Technical Report No. 3, 1956. Published by the American Society for Quality Control as General Publication No. 4. Milwaukee, WI: American Society for Quality, Inc. Google Scholar
65. Roberts, SW. Control chart tests based on geometric moving averages. Technometrics 1959;1:239–250.CrossRefGoogle Scholar
66. Hunter, JS. The exponentially weighted moving average. Journal of Quality Technology 1986;18:203–210.CrossRefGoogle Scholar
67. Crowder, SV. Design of exponentially weighted moving average schemes. Journal of Quality Technology 1989;21:155–162.Google Scholar
68. Case, KE, Ng, CH. Development and evaluation of control charts using exponentially weighted moving averages. Journal of Quality Technology 1989:21:242–250.Google Scholar
69. Lucas, JM, Saccucci, MS. Exponentially weighted moving average control schemes: properties and enhancements. Technometrics 1990;32:1–12.Google Scholar
70. Wald, A. Sequential Analysis. New York, NY: John Wiley and Sons, Inc; 1947.Google Scholar
71. Page, ES. Continuous inspection schemes. Biometrika 1954;41:100–114.Google Scholar
72. Page, ES. Cumulative sum charts. Technometrics 1961;3:1–9.Google Scholar
73. Lucas, JM. The design and use of v-mask quality control schemes. Journal of Quality Technology 1976;8(1):1–12.Google Scholar
74. Johnson, NL, Leone, FC. Cumulative sum control charts—mathematical principles applied to their construction and use, part I. Industrial Quality Control 1962;18(12):15–21. Part II, 1962;19(1):29-36. Part III, 1962;19(2):22-28.Google Scholar
75. Ewan, WD. When and how to use cu-sum charts. Technometrics 1963;5(1):1–32.Google Scholar
76. Hunter, JS. Part II: just what does an EWMA do? ASQC Statistics Division Newsletter 1995;16(1):4–12.Google Scholar
77. Hawkins, DM. Cumulative sum control charts: an underutilized SPC tool. Quality Engineering 1993;5(3):463–477.Google Scholar
78. Benneyan, JC. Adaptive Statistical Process Control Using Sequential Hypothesis Tests. Technical Report. Amherst, MA: University of Massachusetts; 1993.Google Scholar
79. Dessau, DB, Steenberg, P. Computerized surveillance in clinical microbiology with time series analysis. J Clin Microbiol 1993;31:857–860.Google Scholar
80. Page, ES. Control charts with warning lines. Biometrika 1955;42:243–257.CrossRefGoogle Scholar
81. Benneyan, JC. Multivariate and Nonparametric Approaches to Statistical Process Control. Technical Report. Amherst, MA: University of Massachusetts; 1993.Google Scholar
82. Rocke, DM. Robust control charts. Technometrics 1989;31:173–184.CrossRefGoogle Scholar
83. Willemain, TR, Runger, GC. Designing control charts using an empirical reference distribution. Journal of Quality Technology 1996;28:31–38.Google Scholar
84. Seppala, T, Moskowitz, Plante R, Tang, J. Statistical process control via the subgroup bootstrap. Journal of Quality Technology 1995;27:139–153.CrossRefGoogle Scholar
85. Wu, Z, Wang, Q. Bootstrap control charts. Quality Engineering 1996;9:143–150.Google Scholar
86. Alwan, LC, Roberts, HV. Time series modeling for statistical process control. Journal of Business and Economic Statistics 1988;6:87–95.Google Scholar
87. Vasilopoulas, AV, Stamboulis, AP. Modification of control chart limits in the presence of data correlation. Journal of Quality Technology 1978;10:20–30.Google Scholar
88. Montgomery, DC, Mastrangelo, CM. Some statistical process control methods for autocorrelated data. Journal of Quality Technology 1991;23:179–193.Google Scholar
89. Runger, GC, Willemain, TR. Model-based and model-free control of auto-correlated processes. Journal of Quality Technology 1995;27:283–292.Google Scholar
90. Yashchin, E. Performance of cusum control schemes for serially correlated observations. Technometrics 1993;35:37–52.Google Scholar
91. Alloway, JH, Raghavachari, M. An introduction to multivariate control charts. ASQC Annual Quality Congress Transactions. Milwaukee, WI: American Society for Quality, Inc; 1991:773–783.Google Scholar
92. Hicks, CR. Some applications of Hotelling's T Industrial Quality Control 1955;11(6):23–26.Google Scholar
93. Patel, HI. Quality control methods for multivariate binomial and Poisson distributions. Technometrics 1973;15:103–112.Google Scholar
94. Anderson, EA, Diaz, J. Using process control chart techniques to analyze crime rates in Houston, Texas. Journal of the Operational Research Society 1996;47:871–881.Google Scholar
95. Garnerin, PH, Saidi, Y, Valleron, AJ. The French communicable diseases computer network. Extended Clinical Consulting by Hospital Computer Networks. In: Parsons, DF, Fleisher, CM, Greens, RA, eds. New York, NY: Ann N Y Acad Sci; 1992;670:29–42.Google Scholar
96. Jackson, JE. Quality control methods for two related variables. Industrial Quality Control 1956;12:4–8.Google Scholar
97. Jackson, JE. Quality control methods for several related variables. Technometrics 1959;1:359–377.Google Scholar
98. Mandel, BJ. The regression control chart. Journal of Quality Technology 1969;1:1–9.Google Scholar
99. Latzko, WJ. Control charts in the board room. ASQC Annual Quality Congress Transactions Milwaukee, WI: American Society for Quality, Inc; 1989:731–736.Google Scholar