With increasing electronic medical data and the development of artificial intelligence, clinical decision support systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSSs provide valid rationales and explainable results. These systems ensure trustworthiness and transparency by showing the recommendation and prediction result process using explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduces resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSSs in the future.

Kim SY, et al. J Korean Acad Nurs Adm. 2013 Dec;19(5):555-564. http://dx.doi.org/10.11111/jkana.2013.19.5.555

The purpose of this study was to calculate the combined effect size of nurse support programs on job satisfaction among hospital nurses. The PICO framework was used in this methodological study of systematic review and meta-analysis. Using nine electronic databases of four international and five Korean databases and applying the eligibility criteria, articles published from database inception through October 2022 were collected. A total of 24 Korean and international articles were selected following the PRISMA guidelines. The keywords of nurse, mentoring (preceptorship, internship, or residency) program, and job satisfaction were selected based on the PICO. The checklists for RCTs and quasi-experimental studies provided in the Joanna Briggs Institute of Critical Appraisal Tool were used for the quality assessment. The coded data were analyzed using MIX 2.0 statistical software. We analyzed the combined effect sizes, heterogeneity, funnel plot, Egger’s regression test, Begg’s test, subgroup analyses, and univariate meta-regression. The overall effects of the program on job satisfaction and organizational behavior such as organizational commitment, interpersonal relationships, self-efficacy, motivation, burnout, and turnover intention for hospital nurses were statistically significant. The results of this study may explain the effect of the nurse support program on job satisfaction and organizational behaviors for hospital nurses.

In the current healthcare landscape, nurses frequently encounter various ethical dilemmas, necessitating situation-specific ethical judgments. It is crucial to thoroughly understand the factors that shape the hospital ethical climate and the elements that are influenced by this climate. This study aims to identify the variables associated with the hospital ethical climate perceived by Korean nurses. A literature search was conducted using the core database, and the effect sizes of relevant variables were analyzed using a comprehensive meta-analysis. The overall effect size analysis incorporated 56 variables, and a meta-analysis was performed on 7 variables. This study found correlations between ethical sensitivity (ESr = 0.48), moral distress (ESr = −0.30), empathy (ESr = 0.27), ethical leadership (ESr = 0.72), job satisfaction (ESr = 0.64), and intention to leave (ESr = −0.34) with the hospital ethical climate. Both personal and organizational attributes were moderately related to the hospital ethical climate. Enhancing the hospital ethical climate could positively affect both individuals and the organization. The protocol for this study has been registered with PROSPERO (CRD42022379812).

Jung MS, et al. J Korean Acad Nurs. 2019 Oct;49(5):526-537. https://doi.org/10.4040/jkan.2019.49.5.526

Background: Hemodialysis patients experience physical and psychosocial symptoms caused by comorbid illnesses, treatment-related side effects, lifestyle alteration, and the psychosocial impacts of living with end-stage renal disease. The Dialysis Symptom Index (DSI) is the most frequently used instrument to assess symptoms of chronic kidney disease and end-stage renal disease in patients. Purpose: The aims of this study were to develop the Korean version of the DSI, to verify the reliability and validity of the developed tool, and to use the tool to evaluate the symptoms of hemodialysis patients in Korea. Methods: A methodological study was performed on 230 hemodialysis patients from three hospitals who agreed to participate in the study from October 1 to 30, 2015. The Symptom Checklist-90-Revised was used to verify the convergent validity, whereas the total score of Kidney Disease Quality of Life Questionnaire-Short Form, Version 1.3, was used to verify the divergent validity. Data were analyzed using descriptive analysis, Cronbach’s alpha, confirmatory factor analysis, and Pearson’s correlation analysis. Results: The internal consistency reliability of the Korean DSI assessed by Cronbach’s alpha was .90. The Korean DSI showed a high positive correlation coefficient of .80 with symptom severity using the Symptom Checklist-90-Revised, which confirmed the convergent validity. Finally, the correlation coefficient with the quality of life of the participants using the Kidney Disease Quality of Life Questionnaire-Short Form, Version 1.3, was −.68, which confirmed the divergent validity. Conclusions/Implications for Practice: This study developed the Korean version of the DSI and verified its validity and reliability. The Korean version of the DSI showed good properties and may be used to assess the symptoms of hemodialysis patients. Nurses may use this self-report tool to better understand and help relieve the prevalence and severity of symptoms in hemodialysis patients in Korea.