Background: In situ simulation is the practice of using simulated scenarios to improve skill implementation, train critical thinking and problem-solving abilities, and enhance self-efficacy. This study aimed to enhance nursing knowledge, skills, and attitudes toward clinical work by applying in situ simulation training to improve the healthcare of critically ill patients. Methods: This study was conducted from a medical center in northern Taiwan and included 86 trainees who received intensive care training courses from 1 June 2017 to 31 May 2019. The self-report knowledge assessment, empathetic self-efficacy scale, skill assessment, and attitudes of instructors before and after training were collected. The statistical analysis used the Wilcoxon test for knowledge and attitudes, and chi-square tests were used for skills to evaluate the learning effect. Results: The results showed a statistically significant improvement in knowledge, skills, attitudes, and empathy in nursing care. Conclusions: In situ simulation learning can be an accepted method for nursing skills in the intensive care unit. Through this study, we understood that the in situ simulation method was beneficial to nurses’ care and care thinking processes. It is worth developing and evaluating integrated simulation education to enhance learning, change behavior, and promote holistic care in the nursing field.

Background: Cancer-related fatigue is one of the most common and persistent issues experienced by cancer patients. Cancer-related fatigue is a distinct form of fatigue that is subjective, long-lasting and unalleviated by rest or sleep. Studies have shown that almost all cancer patients experience severe fatigue that disrupts the quality of life and physical function, but cancer-related fatigue remains under-addressed in clinical care, and only about half of all patients receive treatment. Methods: To increase the awareness of cancer-related fatigue and improve current management, the Taiwan Society of Cancer Palliative Medicine and the Taiwan Oncology Nursing Society convened a consensus committee to develop recommendations for the screening, assessment and treatment of cancer-related fatigue. Results: Thirteen consensus recommendations were subsequently developed based on the best available evidence and the clinical experience of committee members. Conclusions: These recommendations are expected to facilitate the standardization of cancer-related fatigue management across Taiwan and may also serve as a reference for other clinicians.

(1) Background: Patients who are critically ill or undergo major surgery are admitted to intensive care units (ICUs). Prolonged immobilization is the most likely cause of pressure injuries (PrIs) in the ICU. Previous studies of Western populations found that effective protocols could reduce the incidence of PrIs, and the efficacy of systemic targeted intervention protocols in preventing PrIs in the Chinese population needs to be surveyed. (2) Methods: We reviewed cases of PrIs in the ICUs of Taipei Veterans General Hospital from 2014 to 2019. The ICU nurses at the hospital began to implement targeted interventions in January 2017. The incidence density of PrIs was calculated by dividing the number of PrIs by person days of hospitalizations in the pre-bundle (2014–2016) and post-bundle (2017–2019) stages. Poisson regression was performed to compare the trend of incidence densities. (3) Results: The incidence density of PrIs was 9.37/1000 person days during the pre-bundle stage and 1.85/1000 person days during the post-bundle stage (p < 0.001). The relative risk (RR) was 0.197 (95% confidence interval: 0.149–0.26). The incidence densities of iatrogenic PrIs and non-iatrogenic PrIs decreased as the RRs decreased. (4) Conclusions: Targeted interventions could significantly reduce the incidence of PrIs. Healthcare providers must follow the bundle care protocol for PrI prevention to improve the quality of healthcare and promote patient health.

Background: As outbreak of COVID-19 infection, on April 03, 2020, it is stipulated that the number of inpatient companions is limited to one in Taiwan. All companions are required to register their real personal data with 14 days of travel history, occupation, contact history and cluster history. We would like to evaluate the impact of the new regulations to the accompanying and visiting culture in Taiwan, via analyzing the appearance and characteristics of inpatients companions in this period. Methods: Using intelligent technology, we designed a novel system in managing the inpatients companions (InPatients Companions Management System, IPCMS), and the IPCMS was used to collect data about characteristics of inpatients and companions between April 27 and May 3, 2020. The database is built using MySQL software. Microsoft Excel 2016 and SPSS version 20.0 statistical software were used for data analysis, including the basic data of the companions, differential analysis of companians' gender, person-days and cumulative time, differential analysis of accompaniment-patient relationship, and frequency of accompaniment and cumulative hours. Results: During study period, daily inpatient admissions ranged from 2242-2514, the number of companions per day ranged from 2048-2293, and the number of companions for one inpatient is 1-9 per day, with an average of 1.20-1.26. The companions were mostly family members, and most of them were the inpatients’ children (32.9%), and spouse (26.13%). More females than males were noted in all categories of companionship with statistical significance. Conclusion: The data got in this study could be an important basis for the transformation and reform of the companions culture in Taiwan's hospitals, and will also provide a glimpse into the attitudes and culture of companions who have long been ignorant and neglected. The experience gained in our IPCMS could also serve as a reference for other hospitals in Taiwan and worldwide.

Background: Accurate transcription of pathology gross examination dictation is important for clinical documentation, but multilingual dictation remains challenging in settings where clinicians mix Chinese and English while final pathology reports are written in English. Objective: This study aimed to evaluate whether a Whisper-based automatic speech recognition (ASR) pipeline guided by contextual system messages and combined with open-source large language models (LLMs; Qwen2:72b, Llama3.1:70b, Gemma2:27b) could improve multilingual (Chinese-English) pathology dictation transcription accuracy and generate clinically appropriate English gross description reports. Methods: We conducted a controlled proof-of-concept study using 125 simulated mixed Chinese-English pathology gross examination audio recordings created by physicians or pathologists. Audio recordings were transcribed using Whisper ASR with and without a contextual system message. The ASR transcripts were then converted into English gross description reports using 3 open-source LLMs: Qwen2:72b, Llama3.1:70b, and Gemma2:27b. Outcomes included character error rate, Bilingual Evaluation Understudy, Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, ROUGE-L, Metric for Evaluation of Translation with Explicit Ordering, pathologist Win-Tie-Lose rankings, report-level error categories, inference time, and interrater agreement. Results: The ASR contextual system message reduced the mean character error rate from 0.344 (SD 0.176; 95% CI 0.313‐0.375) to 0.066 (SD 0.100; 95% CI 0.048‐0.084; P <.001). Qwen2:72b achieved the highest automated metric scores, including a Bilingual Evaluation Understudy of 0.644 (SD 0.307), ROUGE-1 of 0.866 (SD 0.163), ROUGE-2 of 0.771 (SD 0.235), ROUGE-L of 0.842 (SD 0.178), and Metric for Evaluation of Translation with Explicit Ordering of 0.805 (SD 0.214). Pathologist-coded total error rates were 16.8% (21/125) for Qwen2:72b, 45.6% (57/125) for Llama3.1:70b, and 92.8% (116/125) for Gemma2:27b. The exact agreement between the 2 pathologists across full ranking categories was 76.8% (96/125; Cohen κ=0.668), and agreement on the top-ranked model or tied top group was 81.6% (102/125; Cohen κ=0.722). Conclusions: In this proof-of-concept evaluation, contextual prompting improved ASR transcription accuracy, and Qwen2:72b generated the most accurate English pathology reports among the evaluated LLMs. However, the study used simulated audio recordings, a local vocabulary prompt, and report-level rather than term-level clinical annotation. LLM-generated reports should therefore be considered draft documentation requiring pathologist verification, and prospective validation in real clinical workflows is needed before clinical deployment.