Analysis of interventions influencing or reducing patient waiting while stratifying by surgical procedure

Facilitation of the coordination of presurgical care is desirable not only from the patients' perspective, but also for increasing operating room productivity of surgeons and anesthesiologists. Times from each patient's first referral to a surgeon until surgery will be available on a vast scale from regional health information exchanges. Treatments (interventions) can include, for example, case management and use of health system networks with common electronic medical records. We developed a method to compare waiting times between treatment (intervention) groups, while stratifying by procedure, despite (1) highly skewed but non-lognormally distributed data, (2) highly heterogeneous sample sizes among groups and procedures, and (3) many combinations of groups and procedures with small sample sizes, resulting in estimated means and variances having substantial uncertainty. Corresponding results obtained by analyzing data from a health system were as follows. (1) The method uses a random-effects model to accommodate procedure heterogeneity and is otherwise distribution free. Log transformation reduced the skewness in waiting time data, making the distribution-free first-order Taylor series approximation analysis of proportional changes between treatments (interventions) reasonable. However, when instead of the random-effects distribution-free analysis, the assumption was made of lognormal distributions, the estimate of treatment effect was biased. (2) Repeating the analysis without stratification by procedure also resulted in biased estimates. (3) There are nearly an unlimited number of different procedures, most rare, so that considering procedure as a random effect was appropriate. Over the ranges of estimated parameter values, prior Monte-Carlo simulation studies showed that meta-analysis using the simple method of moments was appropriate. However, because many treatment/procedure combinations have small sample sizes, confidence interval coverage for the treatment effect was too narrow other than when the degrees of freedom were corrected. Nevertheless, the resulting statistical methodology is straightforward to apply because the data needed are just the summary statistics and the method involves just a series of equations to be followed in a stepwise manner (e.g., in a spreadsheet program such as Microsoft Office Excel).