Implementing nursing diagnostics effectively: cluster randomized trial

Authors


M. Müller-Staub: e-mail: muellerstaub@bluewin.ch

Abstract

Title. Implementing nursing diagnostics effectively: cluster randomized trial.

Aim.  This paper is a report of a study to investigate the effect of guided clinical reasoning. This method was chosen as a follow-up educational measure (refresher) after initial implementation of standardized language.

Background.  Research has demonstrated nurses’ need for education in diagnostic reasoning to state and document accurate nursing diagnoses, and to choose effective nursing interventions to attain favourable patient outcomes.

Methods.  In a cluster randomized controlled experimental study, nurses from three wards received guided clinical reasoning, an interactive learning method. Three wards, receiving classic case discussions, functioned as control group. Data were collected in 2004–2005. The quality of 225 randomly selected nursing records, containing 444 documented nursing diagnoses, corresponding interventions and outcomes was evaluated by applying 18 Likert-type items with a 0–4 scale of the instrument Quality of Nursing Diagnoses, Interventions and Outcomes. The effect of guided clinical reasoning was tested against classic case discussions using T-tests and mixed effects model analyses.

Findings.  The mean scores for nursing diagnoses, interventions and outcomes increased significantly in the intervention group. Guided clinical reasoning led to higher quality of nursing diagnosis documentation; to aetiology-specific interventions and to enhanced nursing-sensitive patient outcomes. In the control group, the quality was unchanged.

Conclusion.  Guided clinical reasoning supported nurses’ abilities to state accurate nursing diagnoses, to select effective nursing interventions and to reach and document favourable patient outcomes. The results support the use of the North American Nursing Diagnosis Association, Nursing Interventions Classification and Nursing Outcomes Classification classifications and demonstrate implications for the electronic nursing documentation.

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