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Published on: July 27, 2018

Discovering client and intervention patterns in home visiting data.

Karen A Monsen1, Arindam Banerjee, Puja Das

  • 1University of Minnesota School of Nursing, Minneapolis, MN 55455, USA. mons0122@umn.edu

Western Journal of Nursing Research
|August 13, 2010
PubMed
Summary
This summary is machine-generated.

This study identified distinct client and intervention groups within family home visiting programs. Findings highlight the complexity of public health nursing and inform future outcome-focused research.

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Area of Science:

  • Public Health Nursing
  • Health Services Research
  • Family Support Interventions

Background:

  • Family home visiting is a key strategy for supporting disadvantaged families and mitigating poverty's effects.
  • Existing knowledge gaps hinder the tailoring of home visiting interventions to specific client risks like parenting and psychosocial issues.

Purpose of the Study:

  • To develop data-driven clusters of clients and interventions from electronic health records.
  • To enhance understanding of public health nursing practice complexity and inform future research.

Main Methods:

  • Utilized electronic health records from 484 public health nursing clients.
  • Applied Mixed Membership Naïve Bayes for client clustering and KMETIS (graph partitioning) for intervention clustering.

Main Results:

  • Generated four distinct, clinically relevant client clusters based on characteristics and needs.
  • Identified fourteen unique intervention clusters reflecting the diverse nature of public health nursing practice.

Conclusions:

  • The study demonstrates leveraging electronic health records to advance nursing knowledge.
  • Further research is recommended to link client and intervention clusters with client outcomes to improve home visiting practices.