Cin-Computers Informatics Nursing最新文献

This study was conducted to determine the effect of video-based simulation education on nursing students' motivation and academic achievement. The research was designed in a mixed model. A quasi-experimental method with a pretest-posttest control group was used for the quantitative part, and the descriptive phenomenology approach was used as the qualitative research method. The sample of the study consisted of second-year nursing students in two state universities in eastern Turkey. The data were collected with the Student Information Form, the Academic Achievement Test, and the Motivation Resources and Problems Scale using Google Forms Web application. Qualitative data were collected through online semistructured interview forms and focus group interviews. According to the results, the posttest academic achievement and Motivation Resources and Problems Scale mean scores of the students in the intervention group were significantly higher than those of the control group ( P < .05). In the analysis of the qualitative, three main themes emerged: We felt fortunate that it increased information retention," "We felt like we were in real practice environment," and "It made us feel that we were nurses." The results showed the use of video-based simulation can be suggested as a strategy to promote classroom teaching and engage students.

Patients with head and neck cancer undergoing radiotherapy encounter physical and psychosocial challenges, indicating unmet needs. Mobile health technology can potentially support patients. This single-armed feasibility study included 30 patients with head and neck cancer undergoing radiotherapy. Patients were asked to use the Health Enjoy System, a mobile health support system that provides a disease-related resource for 1 week. We assessed the usability of the system and its limited efficacy in meeting patients' health information needs. The result showed that the system was well received by patients and effectively met their health information needs. They also reported free comments on the system's content, backend maintenance, and user engagement. This study supplies a foundation for further research to explore the potential benefits of the Health Enjoy System in supporting patients with head and neck cancer.

As a result of rapid advancements in health information technology, uploading health-related information and records onto an electronic health record system has become a common practice. Photographs of patients' wounds have been uploaded electronically, but widespread acceptance by nurses has been prevented owing to issues such as file size and equipment. This research explores the attitude and satisfaction toward using an electronic health record for uploading wound photos. Through the integration of the Technology Acceptance Model, Information System Success Model, and other study results, this research aims to explore the impact of the following variables: system quality, information quality, perceived usefulness, perceived ease of use, user attitude, user satisfaction, and net benefits. We also tested nurses' understanding regarding the process of taking photographs and explored the photograph quality and the photography uploading rates. The results revealed that users were satisfied with the wound-photography system, but some believed that the system stability, processing time, and image resolution should be improved. In addition, more than 80% of the nurses correctly answered photo-taking questions, the study photos reached 70% of the quality standards, and the average uploading rate was 74%. The results could serve as guidelines for system design in the future.

This study aimed to develop a Monte Carlo simulation model to forecast the number of ICU beds needed for COVID-19 patients and the subsequent nursing complexity in a French teaching hospital during the first and second pandemic outbreaks. The model used patient data from March 2020 to September 2021, including age, sex, ICU length of stay, and number of patients on mechanical ventilation or extracorporeal membrane oxygenation. Nursing complexity was assessed using a simple scale with three levels based on patient status. The simulation was performed 1000 times to generate a scenario, and the mean outcome was compared with the observed outcome. The model also allowed for a 7-day forecast of ICU occupancy. The simulation output had a good fit with the actual data, with an R2 of 0.998 and a root mean square error of 0.22. The study demonstrated the usefulness of the Monte Carlo simulation model for predicting the demand for ICU beds and could help optimize resource allocation during a pandemic. The model's extrinsic validity was confirmed using open data from the French Public Health Authority. This study provides a valuable tool for healthcare systems to anticipate and manage surges in ICU demand during pandemics.

Telehealth appointments in the healthcare sector have increased since the COVID-19 pandemic, increasing patients' access to services. However, research exploring nurse perceptions of implemented telehealth services in the community sector is limited. Within the context of quality improvement, the current study aimed to understand child health nurses' acceptance and use of a novel telehealth platform using mixed methods. A total of 38 child health nurses completed an online survey that included multiple-choice questions based on an expanded Technology Acceptance Model and open-ended questions exploring barriers and facilitators to use. Results demonstrated that despite 70% of nurse users having completed less than three sessions with parents, perception and acceptance scores were high. Overall, 85% of variance in satisfaction with the platform and 46% of variance in intention to use the platform were predicted by perception scores. Three consistent themes generated from data were facilitators for use and five as barriers, which provided further understanding to findings. To ensure telehealth is adapted into routine clinical care, facilitators and barriers for implementation need to be identified and addressed. Nurses need to be engaged in implementation and ongoing maintenance to ensure the uptake and optimal use of technology within nursing care.

The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management.