Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 7 appraisals
JMIR formative research
An Interactive Digital Dashboard for Patient Monitoring and Management in a Continuity of Care Centre: Development and Preliminary Usability Evaluation Study
BACKGROUND: Clinical dashboards are becoming important tools for managing and monitoring hospitalized patients across different wards. Moreover, careful attention to design, usability, and user interaction is essential for developing effective support tools for clinicians. OBJECTIVE: This study aimed to describe the development, implementation, and preliminary evaluation of an interactive dashboard for patient monitoring and management in the Continuity of Care Centre (CCC). METHODS: We developed a dashboard according to clinicians' requests and the daily workflow of case managers. First, a CCC Data Mart was created to collect all patients' information automatically extracted from the hospital's data warehouse. However, case managers had the possibility to enter additional patient information in the dashboard using a dedicated form. Moreover, CCC physicians, nurses, and administrative staff were surveyed using 2 validated questionnaires, the System Usability Scale and the Questionnaire for User Interaction Satisfaction. The Situation Awareness Index was proposed to evaluate user awareness and task efficiency. RESULTS: The first version of the CCC dashboard presented 4 panels with different types of information, both on the individual patient and on metrics related to the overall patient population. The first panel focused on patients' data, such as demographic factors, admission, transfer, discharge wards (and their dates), etc. The importance of this panel was the possibility of viewing information collected from different sources within a single interface. The other 3 panels displayed different key performance indicators for the overall patient population and presented data both in the form of tables in the second panel and graphs in the third and fourth panels. After 3 months of daily use, a total of 15 participants, 10 nurses, 2 administrative staff members, and 3 physicians, were recruited for the dashboard evaluation. The average System Usability Scale score of the dashboard was 61.5 (SD 15.7) points, which indicates "OK to good" usability, and the median score obtained with the Questionnaire for User Interaction Satisfaction was 5.77 (IQR 4-7), with the highest results in usability (mean 6.33, SD 0) and learning (mean 6.01, SD 0.39). The overall Situation Awareness Index score was 4 points, with the highest score in "familiarity of dashboard" (mean 4.73, SD 1.66 points) and "arousal support" (mean 4.6, SD 1.8 points). CONCLUSIONS: We developed an interactive dashboard for patient monitoring and management, with positive evaluations from users across different questionnaires.
14 June 2026
Read appraisal →JMIR nursing
Association Between Electronic Health Record-Based Nursing Workload and Turnover: Retrospective Cohort Study
BACKGROUND: Nurse turnover remains a major challenge for health systems, yet objective, scalable measures of workload that predict turnover are limited. Electronic health record (EHR) audit logs offer a potential data source to quantify nursing work patterns. OBJECTIVE: This study aimed to evaluate the association between EHR-derived measures of nursing workload and turnover among inpatient nurses. METHODS: We analyzed work-related activities of staff nurses from medical and surgical inpatient units at a large academic medical center from January 1, 2022, to December 31, 2022. Data included deidentified demographics (age, sex, years since licensure, and service group), shift characteristics (number of shifts worked, proportion of night shifts, timing, and location), and measures of work activities derived from EHR audit logs. Audit logs were used to develop measures related to nurse workload, including (1) nurse activities (information review, medication administration, alert management, navigation, documentation, and communication), (2) patient load (based on the number of unique patient charts accessed), and (3) cognitive load (based on the number of patient switches). For nurses who left, we excluded the 6 weeks immediately preceding termination (washout period) and measured workload during the preceding 6-week period; for those who remained, a random 6-week working period was selected. Associations between workload measures and turnover were assessed using mixed-effects logistic regression, adjusting for demographics and shift-related characteristics. RESULTS: Among 432 nurses (n=363, 84% female; median age 27, IQR 23-36 years), contributing 6812 shifts and approximately 13 million audit log actions, 84 (19%) left the institution in 2022. A higher proportion of medication administration actions was associated with higher odds of turnover (odds ratio [OR] 2.20, 95% CI 1.36-3.54), whereas greater alert engagement (OR 0.48, 95% CI 0.32-0.72) and more years since licensure (OR 0.57, 95% CI 0.38-0.84) were associated with lower odds of turnover. CONCLUSIONS: EHR-derived workload measures, particularly greater medication administration burden and lower alert engagement, were independently associated with the risk of nurse turnover. Patterns of EHR use may help identify nurses at higher risk of leaving an institution and can potentially inform targeted workforce retention strategies.
13 June 2026
Read appraisal →JMIR nursing
Simulation-Based Training for Nursing Students to Improve Patient Safety: Systematic Review
BACKGROUND: Patient safety is a fundamental pillar of health care quality. Simulation-based training provides a controlled environment for nursing students to develop safety competencies and error-recognition skills before clinical practice. OBJECTIVE: This systematic review aimed to describe and characterize the simulation-based education features and modalities used to address patient safety outcomes in undergraduate nursing students, identifying the strategies that contribute to improvements in safety-related competencies. METHODS: A systematic review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines across PubMed, Web of Science, Scopus, CINAHL, Cochrane, and Lilacs (2019-2024). Inclusion criteria focused on original studies involving undergraduate nursing students and simulation interventions measuring patient safety outcomes. Studies in languages other than English, Spanish, or Portuguese were excluded. Two reviewers independently performed study selection and data extraction. Methodological quality was assessed using Joanna Briggs Institute tools, applying a 60% quality threshold for inclusion. Results were synthesized through a narrative approach. RESULTS: A total of 20 studies from 12 countries were included. The methodological quality was high (n=14) and moderate (n=6). Findings revealed that high-fidelity simulation and virtual reality are the primary strategies used. Simulation proved effective in enhancing both technical skills (medication administration accuracy) and nontechnical skills (communication via SBAR [Situation, Background, Assessment, Recommendation] and ISBAR [Identification, Situation, Background, Assessment, Recommendation] tools, teamwork, and adverse event reporting). Key strategies contributing to safety included repetitive practice and interprofessional simulation, which significantly improved error detection and clinical judgment. CONCLUSIONS: Simulation is an essential pedagogical strategy for preparing nursing students to deliver safe care. Practical implications include the need to integrate structured simulation into nursing curricula to bridge the theory-practice gap. Future research should prioritize longitudinal designs to assess the retention of these safety skills in clinical settings and develop standardized metrics for measuring patient safety outcomes.
28 May 2026
Read appraisal →JMIR nursing
From Concept to Practice: Lessons From the Balanced Nursing Teams Decision-Support System
The global nursing workforce crisis demands a shift from reactive staffing to strategic workforce optimization through data-driven decision-support systems. This viewpoint paper reflects on the development and attempted implementation of the balanced nursing teams system, a decision-support tool integrating approximately 250 data points-of which roughly 150 are extracted from existing organizational systems (human resources, scheduling, electronic health records, quality registries) through flexible import mechanisms, and the remainder collected through a built-in 360-degree staff survey with automated analysis-across 10 domains to evaluate nursing team balance between capacity, performance, and outcomes. Following crowdfunding by 18 Belgian health care organizations, balanced nursing teams were implemented across 8 diverse settings (home health care, general hospitals, academic centers) between 2019 and 2023. Using the Human-Organization-Technology fit framework, we analyze why evidence-informed, organization-endorsed digital innovations struggle to achieve adoption. Our analysis reveals 3 interdependent barrier categories: technological fragmentation (vendor lock-in, legacy systems, prohibitive integration costs), organizational siloing (Chief Nursing Officers [CNOs] lacking budgetary authority, nursing framed as peripheral to strategic priorities), and managerial hesitance (fear of punitive data use, cognitive overload from staffing crises). These barriers were worsened by the substantial data-integration burden that the system's breadth imposed on organizations with limited digital maturity. Critically, only one site (ie, a nurse-led home health care organization where leadership held both strategic authority and resource control) achieved sustained implementation. This contrast demonstrates that workforce optimization through data depends not on software maturity alone, but on achieving simultaneous fit across human, organizational, and technological domains. We argue that the persistent marginalization of nursing leadership within hospital governance structures represents the fundamental barrier to digital transformation in nursing workforce management. The urgency paradox is striking: while nursing represents health care organizations' highest operational cost and most direct patient interface, workforce optimization tools are consistently deprioritized in favor of regulatory compliance systems and billing infrastructure. Bridging this gap requires systemic investment in nursing leadership authority, data interoperability standards, and recognition that data-driven workforce decisions are strategic imperatives rather than operational luxuries.
14 May 2026
Read appraisal →International journal of nursing studies
The potential of digital health technologies in saving nursing resources: A scoping review
BACKGROUND: The global nursing shortage crisis presents a significant challenge to healthcare systems. Digital health technologies, such as communication tools, automation systems, monitoring devices, and information platforms have been proposed as one solution to alleviate the issue by optimizing nursing resources. However, a comprehensive overview of the use and potential of these technologies in optimizing nurses' work and resources is still lacking. OBJECTIVES: The aim of this review is to provide an overview of (a) the digital health technologies used in nursing that may have potential to save nursing resources, (b) which indicators are used to measure the effectiveness of these technologies, and (c) which technologies are found to be effective in terms of saving nursing resources. METHODS: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews in accordance with the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. The databases PubMed, CINAHL, and Web of Science were searched. Studies were included if they were peer-reviewed, quantitative, addressed nursing professionals, and considered digital health technologies that specifically aimed to save nursing resources. Relevant data were extracted and synthesized narratively, using an emerging framework. RESULTS: A total of 115 studies were included in this review. Digital health technologies fell into four categories: communication, automation, monitoring, and information technologies. All showed potential for saving resources, with monitoring technologies most often reported as having high potential. Indicators used to measure effectiveness focused on temporal, workforce, and workload resources, and related outcomes including quality of care, patient safety, and cost efficiency. Workforce resources were the most frequently optimized, followed by improvements in patient safety and quality of care. While positive effects were predominant, some studies reported adverse and insignificant effects, indicating variability in effectiveness. CONCLUSIONS: Digital health technologies offer promising opportunities to alleviate nursing resource shortages, but their potential seem to vary by type. Monitoring technologies showed the most consistent benefits, while communication and information technologies had mixed effects and automation technologies require further research to clarify their potential. Given the wide range of indicators used to measure potential resource savings, defining common, standardized indicators is essential for systematically assessing the impact of digital health technologies on nursing work and resources. This would enable comparability across studies, strengthen evidence-based decision-making and guide implementation strategies.
9 May 2026
Read appraisal →JMIR nursing
Application of the Technology Acceptance Model to Predict Nursing Students' Intention to Use Informatics: Cross-Sectional Study
BACKGROUND: Nursing informatics is essential for digital health transformation; however, the technology acceptance of undergraduate nursing students in Saudi Arabia remains underexplored. OBJECTIVE: This study examined factors influencing nursing students' intention to use informatics technologies using the technology acceptance model. METHODS: A cross-sectional survey was conducted with 132 undergraduate nursing students. Data were analyzed using descriptive, correlational, and hierarchical regression analyses. RESULTS: Perceived usefulness (mean 3.68, SD 1.22) and perceived ease of use (mean 3.64, SD 1.32) were the strongest predictors of acceptance, together explaining 87% of the variance (R²=0.87; β=0.323 for usefulness, P<.001; β=0.195 for ease of use, P=.032). Only 25.8% (n=34) of the students often used electronic health records, while 31.8% (n=42) had no electronic health record experience, indicating a clear gap in practical informatics exposure. CONCLUSIONS: Nursing students' acceptance of informatics is primarily driven by its perceived usefulness and perceived ease of use. These findings highlight the urgent need to integrate practical, user-centered informatics training and clinical simulation into undergraduate nursing curricula to better prepare students for technology-based practice.
16 Apr 2026
Read appraisal →International journal of nursing studies
The use of large language models in clinical documentation: A scoping review
BACKGROUND: Clinical documentation is essential for safe, high-quality care but has become increasingly complex, contributing to clinician burnout. Large language models offer potential to ease documentation by generating summaries, structuring data, and ensuring compliance. However, concerns remain regarding accuracy, bias, privacy, and regulatory risks. OBJECTIVE: To map current literature on large language models applications in clinical documentation, evaluating their benefits, limitations, and ethical considerations. INFORMATION SOURCES: Five electronic databases (i.e., PubMed, Scopus, CINAHL, Cochrane Library, and IEEE Xplore) covering peer-reviewed literature published in English between January 2009 and August 2025. METHODS: This scoping review followed Arksey and OMalleys framework and was reported in accordance with PRISMA-ScR guidelines. Screening, data extraction, and quality appraisal were conducted independently by multiple reviewers using Joanna Briggs Institute tools. Findings were synthesized using descriptive and narrative approaches. RESULTS: Forty-one studies met inclusion criteria, most originating from the United States. Large language models were primarily applied to clinical note generation, discharge summaries, and provider-patient encounter documentation. Key evaluation metrics included content accuracy, linguistic quality, and summarization performance. Large language models demonstrated potential to improve documentation efficiency and readability, with some studies reporting up to 40 % time savings. However, concerns about factual inaccuracies, hallucinations, and reduced performance in complex cases were common. Clinician perceptions were mixed. Some found notes generated by large language models helpful and well-structured, while others raised concerns about reliability, liability, and loss of clinical nuance. Ethical challenges included data privacy, security, and algorithmic bias, with varying levels of compliance across settings. CONCLUSIONS: Large language models hold significant promise for enhancing clinical documentation by improving efficiency, standardization, and clarity. However, their safe and effective use requires rigorous attention to accuracy, ethical safeguards, and clinician trust. Integration must support, rather than supplant, clinical reasoning and patient-centered care. Co-design with clinicians, real-world evaluation, and artificial intelligence literacy are essential to ensure that these technologies augment, not erode, professional judgment and care quality. REGISTRATION: Open Science Framework Registries (https://osf.io/m4h3q).
2 Apr 2026
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