Using cluster analysis to understand complex data sets- experience from a national nursing consortium

Cluster analysis is a type of exploratory data analysis for classifying observations and identifying distinct groups. It may be useful for complex data sets where commonly used regression modeling approaches may be inadequate due to outliers, complex interactions or violation of assumptions. In health care, the complex effect of nursing factors (including staffing levels, experience, and contract status), hospital size, and patient characteristics on patient safety (including pressure ulcers and falls) has not been well understood. In this presentation, I will explore the use of use Stata cluster analysis (cluster) to describe five groups of hospital units which have distinct characteristics to predict patient pressure ulcers and hospital falls in relationship to employment of supplemental registered nurses (SRNs) in a national nursing database. The use of SRNs is a common practice among hospitals to fill gaps in nurse staffing. But the relationship between the use of SRNs and patient outcomes varies widely, with some groups reporting a positive relationship while other groups report an adverse relationship. The purpose of this presentation is to identify the advantages and disadvantages of cluster analysis and other methods when analyzing non-normally distributed, non-linear data that have unpredictable interactions.