1 / 18

Process Improvement in Healthcare: Volunteer Clinic Case Study Nonparametric Statistics

Process Improvement in Healthcare: Volunteer Clinic Case Study Nonparametric Statistics. ISE 491 Fall 2009 Dr. Joan Burtner Associate Professor, Department of Industrial Engineering and Industrial Management. Case Study Description. Volunteer Clinic Utilization Study Methods

sarah-fields
Télécharger la présentation

Process Improvement in Healthcare: Volunteer Clinic Case Study Nonparametric Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Process Improvement in Healthcare: Volunteer Clinic Case Study Nonparametric Statistics ISE 491 Fall 2009 Dr. Joan Burtner Associate Professor, Department of Industrial Engineering and Industrial Management

  2. Case Study Description • Volunteer Clinic Utilization Study • Methods • Prediction of Future Demand (Forecasting) • Interviews with Key Clinical Personnel • On-site Observations (Time Studies) • Review of Policies and Procedures • Process Mapping • Retrospective Data Analysis ISE 491 Dr. Burtner ~ Clinic Case Study

  3. Does Volunteerism Vary By Month? • Factor: Month (3 levels, A B C) • Sample: Random selection of 9 physicians per month • Balanced design (3x9=27 observations) • Outcome: Patient contact hours • Interval level data • With an assumption that the underlying distribution for each month is normal, the appropriate hypothesis test is a one-way ANOVA • Statistical package: Minitab 14 or 15 ISE 491 Dr. Burtner ~ Clinic Case Study

  4. Raw Data and Minitab Format Demo Demo Number Month 70 A 30 A 26 A 60 A 34 A 26 A 57 A 39 A 44 A 53 B 39 B 27 B 29 B 23 B 28 B 25 B 23 B 22 B 36 C 23 C 29 C 34 C 16 C 21 C 23 C 25 C 20 C ISE 491 Dr. Burtner ~ Clinic Case Study

  5. One Way ANOVA - Stacked One-way ANOVA: DemoNumber versus DemoMonth Source DF SS MS F P DemoMonth 2 1509 754 5.63 0.010 Error 24 3217 134 Total 26 4726 S = 11.58 R-Sq = 31.92% R-Sq(adj) = 26.25% Individual 95% CIs For Mean Based on Pooled StDev Level N Mean StDev ---+---------+---------+---------+------ A 9 42.89 16.04 (-------*-------) B 9 29.89 10.07 (-------*-------) C 9 25.22 6.59 (-------*-------) ---+---------+---------+---------+------ 20 30 40 50 Pooled StDev = 11.58 ISE 491 Dr. Burtner ~ Clinic Case Study

  6. Simultaneous Confidence Intervals Tukey 95% Simultaneous Confidence Intervals All Pairwise Comparisons among Levels of DemoMonth Individual confidence level = 98.02% DemoMonth = A subtracted from: DemoMonth Lower Center Upper -+---------+---------+---------+-------- B -26.62 -13.00 0.62 (--------*--------) C -31.29 -17.67 -4.04 (--------*--------) -+---------+---------+---------+-------- -30 -15 0 15 DemoMonth = B subtracted from: DemoMonth Lower Center Upper -+---------+---------+---------+-------- C -18.29 -4.67 8.96 (--------*--------) -+---------+---------+---------+-------- -30 -15 0 15 ISE 491 Dr. Burtner ~ Clinic Case Study

  7. Initial interpretation • Null hypothesis: The mean number of patient contact hours (in the population) is the same for each month • Alternate hypothesis: The mean number of patient hours (in the population) differs for at least one month • Assumed significance level = 0.05 • P-value reported = 0.010 • Decision: Reject null hypothesis • Conclusion: Physician volunteer hours vary by month ISE 491 Dr. Burtner ~ Clinic Case Study

  8. Interpretation of Tukey CIs • The means for Month A and B are not significantly different • The means for Month A and C are significantly different; Month A mean contact hours is significantly greater than Month C mean contact hours • The means for Month B and C are not significantly different ISE 491 Dr. Burtner ~ Clinic Case Study

  9. Validation of Assumptions ISE 491 Dr. Burtner ~ Clinic Case Study

  10. Does Volunteerism Vary By Month? Part 2 • Outcome: Total number of patient contact hours • Factor: Month (3 levels, A B C) • Random selection of 9 physicians per month • Balanced design • Interval level data • No assumption that the underlying distribution for each month is normal • Appropriate analysis is a Kruskal-Wallis or Mood Median Test • Statistical package: Minitab 14 or 15 ISE 491 Dr. Burtner ~ Clinic Case Study

  11. Mood Median Results Mood Median Test: DemoNumber versus DemoMonth Mood median test for DemoNumber Chi-Square = 4.75 DF = 2 P = 0.093 Individual 95.0% CIs DemoMonth N<= N> Median Q3-Q1 ---+---------+---------+---------+--- A 2 7 39.0 30.5 (----------*---------------) B 6 3 27.0 11.0 (---*-------) C 6 3 23.0 11.0 (-*-------) ---+---------+---------+---------+--- 24 36 48 60 Overall median = 28.0 ISE 491 Dr. Burtner ~ Clinic Case Study

  12. Source: Minitab Help Guide – Mood’s Median Test • Stat > Nonparametrics > Mood's Median Test • Mood's median test can be used to test the equality of medians from two or more populations and, like the Kruskal-Wallis Test, provides an nonparametric alternative to the one-way analysis of variance. Mood's median test is sometimes called a median test or sign scores test. Mood's median test tests: • H0: the population medians are all equal versus H1: the medians are not all equal • An assumption of Mood's median test is that the data from each population are independent random samples and the population distributions have the same shape. Mood's median test is robust against outliers and errors in data and is particularly appropriate in the preliminary stages of analysis. Mood's median test is more robust than is the Kruskal-Wallis test against outliers, but is less powerful for data from many distributions, including the normal. ISE 491 Dr. Burtner ~ Clinic Case Study

  13. Kruskal – Wallis Results Kruskal-Wallis Test: DemoNumber versus DemoMonth Kruskal-Wallis Test on DemoNumber DemoMonth N Median Ave Rank Z A 9 39.00 20.1 2.83 B 9 27.00 12.7 -0.59 C 9 23.00 9.2 -2.24 Overall 27 14.0 H = 8.91 DF = 2 P = 0.012 H = 8.95 DF = 2 P = 0.011 (adjusted for ties) ISE 491 Dr. Burtner ~ Clinic Case Study

  14. Source: Minitab Help Guide – Kruskal-Wallis • You can perform a Kruskal-Wallis test of the equality of medians for two or more populations. • This test is a generalization of the procedure used by the Mann-Whitney test and, like Mood's Median test, offers a nonparametric alternative to the one-way analysis of variance. The Kruskal-Wallis hypotheses are: • H0: the population medians are all equal versus H1: the medians are not all equal • An assumption for this test is that the samples from the different populations are independent random samples from continuous distributions, with the distributions having the same shape. The Kruskal-Wallis test is more powerful than Mood's median test for data from many distributions, including data from the normal distribution, but is less robust against outliers. ISE 491 Dr. Burtner ~ Clinic Case Study

  15. Modified Design and Analysis • The experimenter did not assume that the number of volunteer hours follows a normal distribution • The experimenter collected data for the same nine physicians for three different 31-day months • Since the design includes three groups (months) blocked by physicians, the appropriate hypothesis test would be the Friedman • Response: Load (Hours) Treatment: Month31 Blocks: Physician • Load Month31 Physician • 70 January Allen • 30 January Brown • 26 January Cook • 60 January Dodd • 34 January Ellis • 26 January Frank • 57 January Grey • 39 January Howard • 44 January Ingle • 53 May Allen • 39 May Brown • 27 May Cook • 29 May Dodd • 23 May Ellis • 28 May Frank • 25 May Grey • 23 May Howard • 22 May Ingle • 36 July Allen • 23 July Brown • 29 July Cook • 34 July Dodd • 16 July Ellis • 21 July Frank • 23 July Grey • 25 July Howard • 20 July Ingle ISE 491 Dr. Burtner ~ Clinic Case Study

  16. Source: Minitab Help Guide – Friedman Test • Stat > Nonparametrics > Friedman • Friedman test is a nonparametric analysis of a randomized block experiment, and thus provides an alternative to the Two-way analysis of variance. The hypotheses are: • H0: all treatment effects are zero versus H1: not all treatment effects are zero • Randomized block experiments are a generalization of paired experiments, and the Friedman test is a generalization of the paired sign test. ISE 491 Dr. Burtner ~ Clinic Case Study

  17. Friedman Test Results Friedman Test: Load versus Month31 blocked by Physician S = 5.56 DF = 2 P = 0.062 Sum Est of Month31 N Median Ranks January 9 41.00 23.0 July 9 23.00 13.0 May 9 25.00 18.0 Grand median = 29.67 ISE 491 Dr. Burtner ~ Clinic Case Study

  18. Interpretation of Friedman • P = 0.062 • For an assumed alpha level of 0.05, there is insufficient evidence to reject H0 because the p-value is greater than the alpha level. • Therefore we conclude that the data do not support the hypothesis that any of the treatment effects are different from zero. • Physician volunteerism, in terms of patient contact hours, does not vary significantly by month. ISE 491 Dr. Burtner ~ Clinic Case Study

More Related