Comparison of nursing diagnostic accuracy when aided by Knowledge-Based Clinical Decision Support Systems with Clinical Diagnostic Validity and Bayesian Decision Models for psychiatric care plan formulation among nursing students: a quasi-experimental study

Abstract

Background: The most suitable and reliable inference engines for Clinical Decision Support Systems in nursing clinical practice have rarely been explored.

Purpose: This study examined the effect of Clinical Diagnostic Validity-based and Bayesian Decision-based Knowledge-Based Clinical Decision Support Systems on the diagnostic accuracy of nursing students during psychiatric or mental health nursing practicums.

Methods: A single-blinded, non-equivalent control group pretest-posttest design was adopted. The participants were 607 nursing students. In the quasi-experimental design, two intervention groups used either a Knowledge-Based Clinical Decision Support System with the Clinical Diagnostic Validity or a Knowledge-Based Clinical Decision Support System with the Bayesian Decision inference engine to complete their practicum tasks. Additionally, a control group used the psychiatric care planning system without guidance indicators to support their decision-making. SPSS, version 20.0 (IBM, Armonk, NY, USA) was used for data analysis. chi-square (χ2) test and one-way analysis of variance (ANOVA) used for categorical and continuous variables, respectively. Analysis of covariance was done to examine the PPV and sensitivity in the three groups.

Results: Results for the positive predictive value and sensitivity variables indicated that decision-making competency was highest in the Clinical Diagnostic Validity group, followed by the Bayesian and control groups. The Clinical Diagnostic Validity and Bayesian Decision groups significantly outperformed the control group in terms of scores on a 3Q model questionnaire and the modified Technology Acceptance Model 3. In terms of perceived usefulness and behavioral intention, the Clinical Diagnostic Validity group had significantly higher 3Q model and modified Technology Acceptance Model 3 scores than the Bayesian Decision group, which had significantly higher scores than the control group.

Conclusion: Knowledge-Based Clinical Decision Support Systems can be adopted to provide patient-oriented information and assist nursing student in the rapid management of patient information and formulation of patient-centered care plans.

Keywords: Data science applications in education; Human–computer interface; Knowledge-based clinical decision support system; Nursing student.

Fig. 1

Interface design of the typical psychiatric care planning system used by the control group

Fig. 2

Interface design of the knowledge-based clinical decision support system

Fig. 3

Consolidated Standards of Reporting Trials flow diagram