ABSTRACT
OBJECTIVES: In this study we aimed to describe and compare groups formed by a rules-based algorithm to prospectively identify clients at risk of poor outcomes in order to guide tailored public health nursing (PHN) intervention approaches. DESIGN: Data-driven methods using standardized Omaha System PHN documentation. SAMPLE: Clients ages 13-40 who received PHN home visiting services for both the Caretaking/parenting and Mental health problems (N = 4109). MEASUREMENT: We applied a theory-based algorithm consisting of six rules using existing Omaha System data. We examined the groups formed by the algorithm using standard descriptive, inferential statistics, and Latent Class Analysis. RESULTS: Clients (N = 4109) were 25.1 (SD = 5.9) years old and had an average of 7.3 (SD = 3.2) problems, 250 (SD = 319) total interventions, and 32 (SD = 44) Mental health interventions. Overall outcomes improved after PHN interventions (p < .001 for all) and having more Mental health signs/symptoms was negatively associated with outcome scores (p < .001 for all). CONCLUSIONS: This algorithm may be helpful in identifying high-risk clients during a baseline assessment who may benefit from more intensive mental health interventions. Findings show there is value using the Omaha System for PHN documentation and algorithm clinical decision support development. Future research should focus on algorithm implementation in PHN clinical practice.
