Discussion

Abstract

Now that the data are more readily available for outcomes research and the techniques to analyze that data are available, we need to use the tools to investigate the total complexity of patient care. We should no longer rely upon basic tools while ignoring sequential treatments for patients with chronic diseases or the issue of patient compliance, and we can start investigating treatments from birth to death. It is no longer possible, with these large datasets, to rely on t-tests, chi-square statistics and simple linear regression. Without the luxury of clinical trials and randomizing patients into treatment versus control, there will always be confounding factors that should be considered in the data. In addition, large datasets almost guarantee that the p-value in a standard regression is statistically significant, so other methods of model adequacy must be used. If we do not start using outcomes data, we are missing crucial knowledge that can be used to improve patient outcomes while simultaneously reducing the cost of care. If we continue to use inferential statistical methods that were not designed to work with large datasets, we will not extract the information that is readily available in the outcomes datasets.