1. Describe the study design you will employ in order to determine if your intervention has had an effect on the outcome variable of interest.
We will measure pre-intervention mean turnover time, and use this as a baseline to compare to our post-intervention mean turnover time. It likely makes the most sense to do an interrupted time series, using each site as its own control using pre-intervention data as the baseline comparison.
To accommodate for autoregulation, we can evaluate the monthly mean turnover time over the last year to see if there is a trend – if so, we will need to incorporate autocorrelation into the effect size, recognizing that we may need a larger effect size to account for trends in pre-intervention data.
2. Define the unit-of-analysis for your main outcome evaluation, the minimum meaningful effect size, and the sample size necessary to detect this effect size.
Unit of analysis for outcome: monthly mean turnover time between scheduled cases (in minutes). This unit of analysis provides some smoothing effect for daily and weekly variations in turnover times.
Minimum meaningful effect size and necessary sample size:
Pre-intervention turnover time mean 52.2 minutes
Parnassus 99.2 scheduled turnover times per month
|
Group 0: Pre-Intervention Control Group |
Group 1: Post-Intervention Group |
Group 0 sample size |
Group 1 sample size |
80% Power Effect Size |
Mean Turnover Time (min) |
Turnover Time Reduction (min) |
|
1 year |
1 month |
1200 |
100 |
29.2% |
37.1 min |
15.1 min |
|
1 month |
1 month |
100 |
100 |
40.0% |
31.3 min |
20.9 min |
|
4 months |
4 months |
400 |
400 |
19.8% |
41.9 min |
10.3 min |
|
1 year |
4 months |
1200 |
400 |
16.2% |
43.7 min |
8.5 min |
|
6 months |
6 months |
600 |
600 |
16.2% |
43.7 min |
8.5 min |
|
1 year |
1 year |
1200 |
1200 |
11.5% |
46.2 min |
6.0 min |
We will need to do separate calculations for Mt. Zion, given that it likely has different mean pre-intervention turnover times and fewer scheduled turnover times per month (62.4 vs 99.2).
We could also consider using OR pods as clusters and/or looking at changes in outliers or other measures than just mean times. To do an analysis using OR pods as clusters, we would need data on mean turnover times for each pod as well as the denominator for number of scheduled turnover times for each specific pod.