Acceso usuarios
Gail M. Keenan, Karen Dunn Lopez, Yingwei Yao, Vanessa E. C. Sousa, Janet Stifter, Alessandro Febretti, Andrew Johnson, Diana J Wilkie
Background Clinical decision support (CDS) tools—with easily understood and actionable information, at the point of care—are needed to help registered nurses (RNs) make evidence-based decisions. Not clear are the optimal formats of CDS tools. Thorough, preclinical testing is desirable to avoid costly errors associated with premature implementation in electronic health records.
Objective The aims of this study were to determine feasibility of the protocol designed to compare multiple CDS formats and evaluate effects of numeracy and graph literacy on RN adoption of best practices and care planning time in a simulated environment.
Methods In this pilot study, 60 RNs were randomly assigned to one of four CDS conditions (control, text, text + graph, and text + table) and asked to adjust the plan of care for two patient scenarios over three shifts. Fourteen best practices were identified for the two patients and sent as suggestions with evidence to the three CDS groups. Best practice adoption rates, care planning time, and their relationship to the RN's numeracy and graph literacy scores were assessed.
Results CDS groups had a higher adoption rate of best practices ( p < .001) across all shifts and decreased care planning time in shifts 2 ( p = .01) and 3 ( p = .02) compared with the control group. Higher numeracy and graph literacy were associated with shorter care planning times under text + table ( p = .05) and text + graph ( p = .01) conditions. No significant differences were found between the three CDS groups on adoption rate and care planning time.
Discussion This pilot study shows the feasibility of our protocol. Findings show preliminary evidence that CDS improves the efficiency and effectiveness of care planning decisions and that the optimal format may depend on individual RN characteristics. We recommend a study with sufficient power to compare different CDS formats and assess the impact of potential covariates on adoption rates and care planning time.
