1 / 21

Journal Club Notes

Journal Club Notes. Martha A. Wojtowycz, PhD November 2, 2018. Learning Objectives. Compare and contrast QA, QI, and evaluation Describe the PDSA cycle for quality improvement Apply appropriate parametric and non-parametric tests to compare proportions and means/medians of two or more groups

malloryr
Download Presentation

Journal Club Notes

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. Journal Club Notes Martha A. Wojtowycz, PhD November 2, 2018

  2. Learning Objectives • Compare and contrast QA, QI, and evaluation • Describe the PDSA cycle for quality improvement • Apply appropriate parametric and non-parametric tests to compare proportions and means/medians of two or more groups • Choose appropriate regression methods for a given dependent variable and time frame • Design a QI study that controls for threats to validity

  3. QA vs. QI: There is a Difference Quality Assurance • Reactive • Works on problems after they occur • Regulatory usually by State or Federal Law • Led by management • Periodic look-back • Responds to a mandate or crisis or fixed schedule • Meets a standard (Pass/Fail) Quality Improvement • Proactive • Works on processes • Seeks to improve (culture shift) • Led by staff • Continuous • Proactively selects a process to improve • Exceeds expectations

  4. Evaluation vs. QI: They are Not the Same Evaluation • Assess a program at a moment in time • Static • Does not include identification of the source of a problem or potential solutions • Does not measure improvements • Program-focused • A step in the QI process Quality Improvement • Understand the process that is in place • Ongoing • Entails finding the root cause of a problem and interventions targeted to address it • Focused on making measurable improvements • Customer-focused • Includes evaluation

  5. PDSA Cycle • Model for accelerating the quality improvement process • Plan-Do-Study-Act (PDSA) Cycle • Plan– plan the change to improve quality of care, including the data collection plan • Do– try the intervention on a small scale • Study– analyze data and interpret the results • Act– act on what is learned from the data

  6. http://www.ihi.org/resources/Pages/HowtoImprove/ScienceofImprovementSettingAims.aspxhttp://www.ihi.org/resources/Pages/HowtoImprove/ScienceofImprovementSettingAims.aspx

  7. Steps in the PDSA Cycle • PLAN: • Prioritize problem – based on data and needs in the unit, division or organization • Assemble a team – with various types of expertise • Director/supervisor • Members with expertise in the service, knowledge about the processes, i.e., how things work • Data/evaluation expertise • QI expertise

  8. Steps in the PDSA Cycle • PLAN: • Examine the current approach • List the steps in the process • Identify potential solutions to the problem • Develop an intervention • Create a measurable AIM statement • Contains information on the Who? What? And When?

  9. Examples of AIM Statements • By xx/xx/xxxx, reduce waiting times to see a provider in our ob/gyn outpatient clinic to less than 15 minutes • By xx/xx/xxxx, the proportion of pregnant or postpartum women enrolled in WIC receiving standardized family planning information in the third trimester or within 2 weeks postpartum will increase from xx% to xx%

  10. Steps in the PDSACycle • DO: • Carry out the change on a small scale • Example: Multiple provider groups at your hospital (PNC, WHS, Suite 600) -- start with one group • Collect and display the data on the change • Document problems, unexpected observations, unintended consequences

  11. Steps in the PDSACycle • Study: • Determine if your interventionwas successful • Compare to baseline data or measures of success identified in the problem statement • Describe and report what you learned

  12. Steps in the PDSACycle • Act: • If the interventionwas successful, try it on a larger scale, e.g., second clinic • If interventionsuccessful, standardize the improvements • If the change was not an improvement, develop a new intervention and test it • Communicate results • Conduct more PDSA cycles

  13. Easter SR, et al. Evaluation of a Quality Improvement Intervention to Increase Vaginal Birth for Twins, Obstetrics and Gynecology, vol. 1321, no. 1, July 2018.

  14. Statistical Tests • Cochran Mantel-Haenszel test for trend • Used to compare proportions over time • Not limited to assessing one pair at a time • For example: Compare proportions of patients in each race category over six years • From Table 1 Percent White: Compares 68.0% vs. 66.4% vs. 70.1% vs. 69.6% vs. 68.5% vs. 68.5% • Traditional Chi-square test would compare only one pair at a time, e.g., 68.0% vs. 66.4%

  15. Statistical Tests • Kruskal-Wallis test • Non-parametric test used to compare means or medians of continuous variables for two or more groups • Used when data are not normally distributed • Can be considered an extension of the non-parametric Mann Whitney U test that only compares continuous variables for two groups • ANOVA can be used to compare means of two or more continuous variables but they should be normally distributed

  16. Regression Analysis • Linear regression is used to show the relationship between a continuous dependent variable, e.g., cesarean section rate, and one or more independent variables, e.g., year of study, maternal age, parity • For a binary or dichotomous dependent variable use logistic regression, e.g. cesarean section (yes/no)

  17. Linear Autoregression • Linear autoregression model often used with times series analysis because a variable in one time period may be correlated with itself in the previous time period, e.g., cesarean section rate in 2015 is correlated with cesarean section rate in 2014 • Corrects for autocorrelation, which may result in t-statistics being more significant than they are • Could also be used with panel data – e.g., data collected on the same patients over time

  18. Did the Intervention Work? • Increase in adjusted vaginal delivery rate but not in the rate of change • Important considerations: • Did the study have enough power to detect a difference in rate of change? • Change takes time – Did they allow for adequate time after the intervention? • In Figure 1, look at rate of increase between 2014 and 2015 – Is it higher? • Was the trend in the 3 previous years linear? • Check Figure 1

  19. Rate should be 28.6%

  20. Possible RCT • Multiple academic medical centers with similar patients and providers • Randomize centers to either the intervention group which receives education, simulation training and access to backup support system or the control group which does not receive the education, does not have access to simulations or to backup support system • Centers with overlapping providers are all in the same arm of the study

  21. Control Threats to Validity • Controls for history– Don’t have to worry about other changes that might occur over time that might affect the intervention since both the intervention and control group assessed during the same time period • New guidelines, different training, change in patient attitudes to cesarean section for twins • Reduces contamination – By keeping centers with overlapping providers in same study arm, reduce likelihood that those in the control group will be exposed to the intervention

More Related