ABSTRACT
Visualization is a Big Data method for detecting and validating previously unknown and hidden patterns within large data sets. This study used visualization techniques to discover and test novel patterns in public health nurse (PHN)-client-risk-intervention-outcome relationships. To understand the mechanism underlying risk reduction among high risk mothers, data representing complex social interventions were visualized in a series of three steps, and analyzed with other important contextual factors using standard descriptive and inferential statistics. Overall, client risk decreased after clients received personally tailored PHN services. Clinically important and unique PHN-client-risk-intervention-outcome patterns were discovered through pattern detection using streamgraphs, heat maps, and parallel coordinates techniques. Statistical evaluation validated that PHN intervention tailoring leads to improved client outcomes. The study demonstrates the importance of exploring data to discover ways to improve care quality and client outcomes. Further research is needed to examine additional factors that may influence PHN-client-risk-intervention-outcome patterns, and to test these methods with other data sets.
