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.

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.

Design and evaluation of a knowledge-based clinical decision support system for the psychiatric nursing process.

BACKGROUND AND OBJECTIVES: The nursing assessment in the psychiatric department differ from those used in other departments considerably. We developed a psychiatric knowledge-based clinical decision support system (Psy-KBCDSS), which may aid nurses in solving patients' problems in the psychiatric department. In addition, we compared the sensitivity and specificity for the nursing diagnoses between the psychiatric nursing process system (Psy-NPS) and Psy-KBCDSS to determine that the Psy-KBCDSS can assist nurses in performing the nursing assessment and diagnosis. METHODS: Visual Studio 2019 was adopted as the primary software development tool, and C# as the main development language. The concept of the nursing process was applied to develop the Psy-KBCDSS user interface. We developed a clinical diagnostic validity inference engine to calculate the frequencies of the nursing assessment items and nursing diagnoses in clinical tasks in the Psy-NPS for generating a knowledge-based database of the Psy-KBCDSS. The sensitivity and specificity for nursing diagnoses formulated by senior and junior nurses were used to determining the effectiveness of adopting Psy-NPS and Psy-KBCDSS. RESULTS: This study include 22 nursing diagnoses commonly encountered in psychiatric wards. The top eight most common diagnoses in the Psy-NPS and Psy-KBCDSS were altered thought processes, ineffective coping, sensory and perceptual alterations, insomnia, risk for other-directed violence, anxiety, impaired social interaction, and risk for suicide. Compared with the Psy-NPS, the Psy-KBCDSS had significantly higher sensitivity for sensory and perceptual alterations, ineffective coping, and insomnia and significantly higher specificity for ineffective coping. CONCLUSIONS: Considering its high sensitivity and specificity for various nursing diagnoses, the Psy-KBCDSS, as an empirical patient-oriented nursing clinical decision-making support system, can assist nurses in clinical nursing tasks including nursing process-based patient assessment and nursing diagnosis.