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.

[1]  L. Tiedje.  Thirty Years of Maternal - Child Health Policies in the Community , 2005, MCN. The American journal of maternal child nursing.

[2]  Madeleine J Kerr,et al.  Data management for intervention effectiveness research: comparing deductive and inductive approaches. , 2009, Research in nursing & health.

[3]  Arindam Banerjee,et al.  Bayesian cluster ensembles , 2011, Stat. Anal. Data Min..

[4]  Amy B. Lytton,et al.  A Public Health Nursing Informatics Data-and-Practice Quality Project , 2006, Computers, informatics, nursing : CIN.

[5]  Karen A. Monsen,et al.  Comparing Maternal Child Health Problems and Outcomes Across Public Health Nursing Agencies , 2009, Maternal and Child Health Journal.

[6]  Karen S. Martin,et al.  The Omaha system : a key to practice, documentation, and information management , 2005 .

[7]  Arindam Banerjee,et al.  Latent Dirichlet Conditional Naive-Bayes Models , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[8]  K. Martin,et al.  Developing an outcomes management program in a public health department. , 2002, Outcomes management.

[9]  Anne M Berger,et al.  Data Mining as a Tool for Research and Knowledge Development in Nursing , 2004, Computers, informatics, nursing : CIN.

[10]  Karen S. Martin,et al.  The Omaha system : applications for community health nursing , 1992 .

[11]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[12]  D. Olds.  Prenatal and Infancy Home Visiting by Nurses: From Randomized Trials to Community Replication , 2002, Prevention Science.

[13]  Arindam Banerjee,et al.  Mixed-membership naive Bayes models , 2011, Data Mining and Knowledge Discovery.

[14]  J. Dennis,et al.  Home-based support for disadvantaged adult mothers. , 2007, The Cochrane database of systematic reviews.

[15]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[16]  J. Drummond,et al.  Home visitation practice: models, documentation, and evaluation. , 2002, Public health nursing.