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.

Factors Influencing Self-Management Behaviors among Hemodialysis Patients.

AIM: To investigate the factors affecting hemodialysis patients' self-management ability at a dialysis center in Taiwan. BACKGROUND: Taiwan has the highest incidence and prevalence of end-stage kidney disease (ESKD) in the world. Over 90% of patients with ESKD receiving hemodialysis (HD) and self-management behaviors are critical among these patients. Failure to adhere to self-managed care increases the cost of medical care and the risk of morbidity and mortality. METHODS: In this cross-sectional study, a total of 150 HD patients were observed for their self-management behaviors and the factors influencing these behaviors including education level, comorbid conditions, biochemical analysis, depression, and social support, etc., were analyzed. RESULTS: Self-management behaviors in HD patients were significantly impaired in the presence of diabetes mellitus, hypertension, anemia, hypoalbuminemia, and depression. The major predictor of patients' self-management was depression, explaining 14.8% of the total variance. Further addition of social support, hypertension, and diabetes mellitus into the regression model increased the total explained variance to 28.6%. Of the various domains of self-management, the partnership domain received the highest score, whereas emotional processing received the lowest score. CONCLUSIONS: This study found the important factors influencing self-management behaviors; through this acknowledgement and early correction of these factors, we hope to improve HD patients' individual life quality and further decrease their morbidity and mortality.

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.

Determining Factors Affecting Nurses' Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data.

BACKGROUND: Health information technology is used in nursing practice worldwide, and holistic patient care planning can serve as a guide for nursing practice to ensure quality in patient-centered care. However, few studies have thoroughly analyzed users' acceptance of care plan systems to establish individual plans. OBJECTIVE: Based on the technology acceptance model 3 (TAM3), a user technology acceptance model was established to explore what determines the acceptance of care plan systems by users in clinical settings. METHODS: Cross-sectional quantitative data were obtained from 222 nurses at eight hospitals affiliated with public organizations in Taiwan. Using the modified TAM3, the collected data were employed to analyze the determinants of user acceptance of a care plan system through structural equation modeling (SEM). We also employed moderated multiple regression analysis and partial least squares-SEM to test the moderating effects. RESULTS: We verified all significant effects from the use of a care plan system among bivariate patterns in the modified TAM3, except for moderating effects. Our results revealed that the determinants of perceived usefulness and perceived ease of use significantly influenced perceived usefulness and perceived ease of use, respectively. The results also indicated that nurses' perceptions of subjective norm (path coefficient=.25, P<.001), perceived ease of use (path coefficient=.32, P<.001), and perceived usefulness (path coefficient=.31, P<.001) had significantly positive effects on their behavioral intention to use the care plan system, accounting for 69% of the total explained variance. CONCLUSIONS: By exploring nurses' acceptance of a care plan system, this study revealed relationships among the variables in TAM3. Our results confirm that the modified TAM3 is an innovative assessment instrument that can help managers understand nurses' acceptance of health information technology in nursing practice to enhance the adoption of health information technology.

Theoretical integration of user satisfaction and technology acceptance of the nursing process information system.

BACKGROUND: The nursing process system (NPS) is used to establish the nursing process involving assessment, diagnosis, planning, intervention, and evaluation in solving the health problems of patients. OBJECTIVES: The factors influencing the use of the NPS by nurses were analyzed based on user satisfaction and technology acceptance within the 3Q (service quality, information quality, and system quality) model. METHODS: In this cross-sectional quantitative study, the valid responses of 222 nurses to a questionnaire were obtained; these nurses worked at eight hospitals affiliated with public organizations in Taiwan. Structural equation modeling was used to analyze information quality, system quality, service quality, user satisfaction, perceived usefulness, perceived ease of use, perceived enjoyment, behavioral attitude, and intention after the nurses had used the NPS system for more than 1 month. RESULTS: Information quality, service quality, and system quality influenced user satisfaction. User satisfaction affected perceived usefulness, perceived ease of use, and perceived enjoyment and had the highest explanatory power (R2 = 0.75). Furthermore, perceived usefulness, perceived ease of use, and perceived enjoyment influenced behavioral attitude and intention to use the system. The proposed model explained 53% of the variance in the intention to use the NPS. CONCLUSIONS: The relationships between the variables of the 3Q model were successfully used to examine the intention of nurses toward using the NPS. Using the findings of this study, designers and programmers can comprehensively understand the perceptions of nurses and further improve the performance of the NPS.