Purpose: This study was done to examine the effects of auricular acupressure (AA) on blood pressure (BP) and stress responses in adults with prehypertension. Methods: This single-blind, randomized, placebo-controlled study was conducted from September 2019 to February 2020. Participants were recruited through bulletin board notices in D city. The experimental group (n=27) received 8 weeks of AA intervention on specific acupoints (shenmen, kidney, heart, occiput, adrenal gland) to reduce blood pressure and stress, whereas the control group (n=25) received AA unspecific acupoints (helix 1-5). The outcomes were measured using BP, perceived stress scale (PSS), cortisol, and heart rate variability (HRV). Collected data were analyzed using Fisher’s exact test, chi-square, Shapiro-Wilk normality test, paired t-test, independent t-test, Mann-Whitney U test, repeated measures ANOVA and Friedman test with the SPSS/WIN 24.0. Results: Statistical differences were found between the groups for systolic BP (x2=85.64, p<.001), diastolic BP (x2=27.76, p=.001), PSS (F=9.439, p=.003), low frequency (F=5.22, p=.027), heart rate (F=3.208, p=.045), and HRV index (F=3.579, p=.035). Cortisol did not differ significantly between the experimental group and control group. Conclusion The findings show that AA leads to improvements in BP and stress responses in adults with prehypertension. Therefore, AA can be used as an alternative nursing intervention for hypertension prevention and stress management.

PURPOSE: The purpose of this study was to explore perioperative nurses' communication experience within the surgical team with regard to patient safety. METHODS: Data were collected from December 2015 to September 2016, through in-depth individual interviews with 14 perioperative nurses. Individual interviews, once or twice, lasted from 40 minutes to one hour for each session. The main interview question was “How do you describe your communication experience with surgical team members as a perioperative nurse?” Collected data were analyzed using a conventional content analysis. RESULTS: Two categories of the perioperative nurses' experience of communication were identified: communication contributing to patient safety, communication hindering patient safety. Communication in the surgical team reflected on the unique features of the operating room, such as urgency and a hierarchical organizational culture. However, the nurses recognized ineffective communication could impact on patient safety, and endeavored to overcome communication failures. CONCLUSION: The results indicate that sharing responsibility, open communication, assertiveness on safety issues, and interprofessional collaboration in the operating room are necessary to ensure effective communication. Thus, respectful culture and an open communication climate based on interprofessional understanding are required to improve communication. Training programs to enhance communication skills should be implemented.
Assertiveness ; Climate ; Cooperative Behavior ; Education ; Humans ; Operating Rooms ; Organizational Culture ; Patient Safety ; Qualitative Research

Purpose: This study aimed to develop an educational informatics system for nursing faculty to improve their competencies in teaching patient safety and to evaluate the effectiveness of the system. Methods: We developed a system called, ‘Resource to Enhance Safety Competency and Utilize for Education’ (RESCUE) based on the World Health Organization Multi-professional Patient Safety Curriculum Guide, and it was implemented with full-time nursing faculty in 4-year nursing schools. A one-group pretest-posttest design was used for evaluation. A total of 46 nursing faculty members used the system during a 3-month period. The effects of the RESCUE were measured using a survey including patient safety teaching competency, system usability and user satisfaction. Data were analyzed using descriptive statistics and the Wilcoxon signed-rank test. Results: After using the RESCUE, participants showed a significant increase in self-confidence in teaching patient safety during lectures (Z=-3.61, p<.001) and practica (Z=-3.14, p=.002). Conclusion The developed informatics system was shown to be effective in improving the self-confidence of nursing faculty in teaching patient safety. To effectively integrate patient safety topics into the curriculum, it can be helpful to utilize the educational materials provided in this study with various clinical cases.

PURPOSE: The purpose of this study was to investigate patient safety teaching competency of nursing faculty and the extent of teaching patient safety topics in the nursing curriculum. METHODS: A national survey was conducted with full-time nursing faculty in 4-year nursing schools. Regional quota sampling method was used. An online survey was sent to 1,028 nursing faculty and 207 of them were completed. Among the 207, we analyzed data from 184 participants. The revised Health Professional Education in Patient Safety Survey was used. Data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, Pearson's correlation analysis, and multiple linear regression analyses. RESULTS: The faculty's self-confidence was lower than their perceived importance of patient safety education. The mean score of teaching patient safety was 3.52±0.67 out of 5, and the contents were mostly delivered through lectures. The extent of faculty's teaching varied depending on faculty's clinical career, teaching subjects, participation in practicum courses, and previous experience of patient safety education. The significant predictors of the extent of teaching patient safety were the faculty's self-confidence in teaching patient safety (β=.39) during clinical practicum, their perceived importance of patient safety education during lectures (β=.23), and the teaching subject (β=.15). CONCLUSION: To enhance the competency of nursing faculty for effective patient safety education, a patient safety education program tailored to faculty characteristics should be developed and continuously provided for faculty. In addition, it is necessary to improve patient safety curriculum, strengthen clinical and school linkages, and utilize various education methods in patient safety education.
Curriculum ; Education ; Education, Nursing ; Faculty, Nursing* ; Health Occupations ; Humans ; Lectures ; Linear Models ; Methods ; Nursing* ; Patient Safety* ; Preceptorship ; Professional Competence ; Schools, Nursing

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

Cyberlindnera fabianii (previously known as Hansenula fabianii, Pichia fabianii, and Lindnera fabianii) is a yeast species that forms a biofilm, allowing it to resist azole drugs. In this study, we report a case of fungemia with C. fabianii that was successfully treated with anidulafungin. In this case, the organism was initially misidentified as Candida utilis (with a high probability of 93%, suggesting good identification) using the VITEK 2 yeast identification card (YST ID; bio-Merieux, USA). The species responsible for the patient's fungemia was correctly identified after sequencing the internally transcribed spacer region and the D1/D2 domain of the large subunit (26S) rDNA gene. The CLSI M27-A3 broth microdilution method was used to determine the in vitro antifungal activity of anidulafungin and fluconazole against C. fabianii. The MICs of anidulafungin and fluconazole were found to be 0.03 microg/mL and 2 microg/mL, respectively. The patient recovered after 14 days of anidulafungin treatment.
Biofilms ; Candida ; Danazol ; DNA, Ribosomal ; Fluconazole ; Fungemia* ; Humans ; Pichia ; Yeasts