Discovering Client and Intervention Patterns in Home Visiting Data

Family home visiting is a widely accepted strategy used with disadvantaged families to mitigate the effects of poverty. However, gaps persist in knowledge of effective intervention approaches for home visiting relative to specific client risks such as parenting and psychosocial problems. The purpose of this study was to inductively create clusters from electronic health records of 484 public health nursing clients, using client characteristics and intervention data. Four clinically relevant client clusters were generated using Mixed Membership Naïve Bayes methods. Fourteen distinct intervention clusters were generated using KMETIS, a graph partitioning method. The content of the intervention clusters illustrates the complexity of public health nursing practice. This study leverages current nursing documentation technology capacity to advance nursing knowledge. Future research is needed to explore relationships between client and intervention clusters and their associations with client outcomes, with the end goals of improving home visiting practice and client outcomes.