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The aim of this study was to explore the digital health technology readiness of nurses, nursing students, nurse-academics, and nurses in leadership roles. Workforce digital readiness impacts the adoption of digital health technologies and quality and safety outcomes. This study sought to identify key factors affecting nurses' readiness for specific digital health technologies and provide recommendations to accelerate readiness levels in alignment with rapidly advancing digital health technologies.
Cross-sectional multi-method study.
An online survey was followed by semi-structured interviews. Survey data (N = 160) were analysed using descriptive and inferential statistics, whereas qualitative responses (N = 8 interviews, 43 open-ended responses) were thematically analysed.
Participants were confident regarding openness to innovation, reporting highest confidence Levels around telehealth, wearable devices, and information technology. The lowest confidence scores were seen in health smart homes technology, followed by health applications, social media, patient online resources, and EHRs. Four themes were developed from the qualitative interviews including ‘opportunities for efficient ways of working’, ‘digital technology turning experts into novices’, ‘disillusionment between expectation and reality’ and ‘shared responsibility for development of digital expertise’. Open-ended data was focused on the need for comprehensive education, ongoing support, and infrastructure improvements to prepare healthcare professionals for digital health environments.
Notable findings include age-related differences, the need for shared responsibility in workforce preparation, and a link between problem-solving ability and help-seeking.
Low confidence among nurses around the use of digital health technologies such as electronic health records, in-home monitoring technology, and other wearable technologies could impact adoption readiness. Because patient safety is increasingly and inextricably linked to digital health technologies, nurses must not only be digital health literate but also included in the design and implementation process of these technologies.
This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for the reporting of cross-sectional survey research, and the Consolidated Criteria for Reporting Qualitative (COREQ) research guidelines.
Limited patient and public involvement was incorporated, focusing on feedback from digital health researchers and practitioner-academics during the academic peer review process. Their insights informed the clarity and relevance of the survey design and data interpretation, ensuring alignment with real-world workforce development priorities in nursing.
Huge advances in rheumatoid arthritis (RA) treatment mean an increasing number of patients now achieve disease remission. However, long-term treatments can carry side effects and associated financial costs. In addition, some patients still experience painful and debilitating disease flares, the mechanisms of which are poorly understood. High rates of flare and a lack of effective prediction tools can limit attempts at treatment withdrawal. The BIOlogical Factors that Limit sustAined Remission in rhEumatoid arthritis (BIO-FLARE) experimental medicine study was designed to study flare and remission immunobiology. Here, we present the clinical outcomes and predictors of drug-free remission and flare, and develop a prediction model to estimate flare risk.
BIO-FLARE was a multicentre, prospective, single-arm, open-label experimental medicine study conducted across seven National Health Service Trusts in the UK. Participants had established RA in clinical remission (disease activity score in 28 joints with C reactive protein (DAS28-CRP)
The intervention was disease-modifying anti-rheumatic drug cessation, followed by observation for 24 weeks or until flare, with clinical and immune monitoring.
The primary outcome measure was the proportion of participants experiencing a confirmed flare, defined as DAS28-CRP≥3.2 or DAS28-CRP≥2.4 twice within 2 weeks, and time to flare. Exploratory predictive modelling was also performed using multivariable Cox regression to understand risk factors for flare.
121 participants were recruited between September 2018 and December 2020. Flare rate by week 24 was 52.3% (95% CI 43.0 to 61.7), with a median (IQR) time to flare of 63 (41–96) days. Female sex, baseline methotrexate use, anti-citrullinated peptide antibody level and rheumatoid factor level were associated with flare. An exploratory prediction model incorporating these variables allowed estimation of flare risk, with acceptable classification (C index 0.709) and good calibration performance.
The rate of flare was approximately 50%. Several baseline clinical parameters were associated with flare. The BIO-FLARE study design provides a robust experimental medicine model for studying flare and remission immunobiology.
ISRCTN registry 16371380
To compare contextual factors influencing discharge practices in three intensive care units (ICUs).
A prospective observational study.
Data were collected using a discharge process report form (DPRF) between May and September 2023. Descriptive statistics were performed to analyse demographic and clinical data. One-way analysis of variance (ANOVA) was used to test the time interval differences among the three sites.
Overall, 69 patients' discharge processes were observed. Among them, 41 (59%) experienced discharge delay, and 1 in 5 patients experienced after-hours discharge. There were statistically significant differences in mean hours in various time intervals during the discharge processes among the three sites. Patients in Hospital C waited the longest time (mean = 31.9 h) for the ward bed to be ready after the bed was requested and for being eventually discharged after ICU nurses to get them ready for discharge (mean = 26.7 h) compared to Hospital A and Hospital B.
We found that discharge delay and after-hours discharge were common and there were significant differences in mean hours of various time intervals during the discharge processes occurred among the three sites. The influence of contextual factors in different hospitals/ICU needs to be considered to improve the ICU discharge process.
Researchers and clinicians should consider targeted context-specific interventions and strategies to optimise patient discharge process from ICUs.
The study findings will inform the development of tailored interventions to reduce the discharge delay and after-hours discharge and, in turn, improve the quality and safety of patient care and health service efficiency.
The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.
Patients' discharge processes were observed, and consumer representatives were involved in the study design.
FreshRSS 