In this case we made use of a paired t-test to establish that there was a statistically significant change in the mean number of veterans waiting 31-60 days for an appointment from a sample of Southeastern US veterans medical centers. A paired comparison was the appropriate analysis since there is a natural pairing between each hospital’s 2015 and 2016 backlogs and the VMCs are not homogeneous.

Stakeholders are hoping for improvement in the performance of VMCs with respect to delays in treating veterans in need of care. However, it is important to formulate the problem statement and hypothesis test in a way that will detect a change in performance, either improvement or degradation, over the course of a year. While measures were taken to reduce the backlogs, they may not have been effective. Other possible reasons for the increased backlog could be increased demand for service or decreased staffing levels (a problem that has also be reported in the VA medical system).

The test of hypothesis established the statistically significant increase, but examining the graphs showed that Fayetteville, NC was the only VMC that achieved a substnatial reduction in backlog. Outliers can influence the outcome of a hypothesis test. A statistically significant difference can be obtained with the outlier included in the analysis, but excluding the outlier results in a difference that is not significantly different and vice versa. In small sample sizes, outliers can be particularly influential. From a statistical viewpoint, outliers should be investigated to look for possible causes such as a data collection error. From the perspective of the problem domain, investigating an outlier that is not found to be in error may provide valuable insights. For example, Fayetteville may be doing something differently than the other VMCs that is effective in reducing the backlogs.

In this case we analyzed only one facet of the problem, the change in the 31-60 backlog. The VA provides information on the number of veterans waiting both shorter and longer periods of time for appointments. While there appears to be an increase across the Southwest US, the data set contains additional variables that can be analyzed. For example, are there differences between VISNs or states? How does bed capacity affect the backlog? More detailed analysis should be conducted so that a complete understanding of the problem is obtained. To improve performance it is important to understand if problems are systematic or related to another factor such as VISN, state, or bed capacity. This will enable targeted improvement actions to be developed.