Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 7 appraisals
Journal of critical care
Artificial intelligence and computerized decision support in adult intensive care: A systematic review of randomized controlled trials
BACKGROUND: Artificial intelligence (AI) and computerized clinical decision support systems (CDSS) are increasingly applied in intensive care, yet their clinical impact remains uncertain, as most studies focus on model development rather than prospective evaluation. OBJECTIVES: To identify randomized controlled trials (RCTs) evaluating AI-based or CDSS interventions intended to influence real-time decision-making in adult intensive care units (ICUs) and to assess their effects on process and patient-centered outcomes. METHODS: We conducted a systematic review of randomized controlled trials (RCTs). PubMed, Embase, Cochrane CENTRAL, ScienceDirect, and IEEE Xplore were searched from inception to November 2025. Eligible studies evaluated AI-based or CDSS interventions in adult ICU patients. Study quality was assessed using RoB 2 and CONSORT-AI criteria. RESULTS: Ten RCTs were included, enrolling approximately 100,000 adult ICU patients. Five trials evaluated AI-based interventions and five evaluated CDSS. Eight trials showed improvements in at least one process measure, including earlier recognition of deterioration, improved protocol adherence, and physiological stability. However, patient-centered benefits were uncommon. Two trials reported reductions in mortality (a sepsis prediction model and a machine-learning-based early-warning system), while most studies showed no consistent effects on clinical outcomes. Reporting of AI-specific elements-dataset provenance, algorithm versioning, and human-AI interaction-was frequently incomplete. CONCLUSIONS: AI and CDSS interventions in adult ICUs are associated with improvements in process-related outcomes but show limited and inconsistent effects on patient-centered endpoints. These findings highlight a persistent gap between algorithmic innovation and clinical validation and underscore the need for pragmatic randomized trials with improved reporting and integration into clinical workflows.
3 Aug 2026
Read appraisal →Scientific reports
Safety design guidelines for clinician-AI interaction in computer-aided diagnosis systems using system-theoretic framework with explainability validation
Ensuring the safety of Artificial Intelligence-enabled Computer-Aided Diagnosis systems is critical because diagnostic errors can have serious consequences for patient care. However, existing regulatory and risk management frameworks often do not sufficiently address the complex socio-technical interactions between clinicians and Artificial Intelligent systems, leaving key human-centered safety challenges underexplored. This paper presents a systematic and human-centered approach to deriving safety design guidelines for clinician-Artificial Intelligence interaction in Computer-Aided Diagnosis systems using System-Theoretic Process Analysis. Through this analysis, we identify critical hazards associated with clinician-Artificial Intelligence collaboration, including automation bias on system recommendations, and misinterpretation of explanations. Based on the identified unsafe control actions, we formulate a set of actionable and traceable safety design guidelines that promote transparency, coherent explanations, and calibrated trust in Artificial Intelligence-assisted decision-making. To bridge safety analysis and system design, the proposed guidelines are operationalized within a Computer-Aided Diagnosis interaction framework. The framework includes a safety-oriented Graphical User Interface that integrates multiple explanation methods and interactive mechanisms to promote clinician engagement. Furthermore, we introduce a safety-oriented evaluation approach that uses consistency across multiple explanation methods as a quantitative indicator of potentially unreliable or ambiguous explanations. By linking System-Theoretic Process Analysis, interaction design, and explainability evaluation, this work provides a unified and reusable framework for improving the safety and reliability of Artificial Intelligence-driven Computer-Aided Diagnosis systems.
31 July 2026
Read appraisal →PloS one
A dual-stream deep learning architecture for business impact scoring and alert escalation
Modern network monitoring systems generate massive volumes of telemetry data, yet most existing anomaly detection models fail to prioritize alerts according to their operational urgency and business impact. This limitation results in delayed incident responses and inefficient alert management in Network Operations Centers. To address this gap, this study proposes a Dual-Stream Predictive Alert Escalation Framework that integrates temporal failure pattern learning with business impact-aware alert prioritization. The proposed architecture consists of two key components: a bidirectional temporal encoder for modeling multivariate Key Performance Indicator (KPI) time-series data, and an auxiliary severity encoder that captures contextual metadata related to operational risk and service criticality. The outputs of these two learning streams are combined through an attention-based fusion mechanism, and a Business Impact Scoring (BIS) layer generates impact-weighted escalation decisions for proactive incident management. Experimental evaluations using real-world KPI datasets and the AI4I_2020 predictive maintenance dataset demonstrate the superior performance of the proposed framework compared to baseline methods such as LSTM, GRU, CNN-LSTM, and BiLSTM-VAE. On the combined multivariate KPI dataset, the model achieved a precision of 0.94, a recall of 0.92, and an F1-score of 0.93, along with a PR-AUC of 0.95 and a ROC-AUC of 0.94. Under impact-aware evaluation, the framework attained the highest Impact-Weighted F1 (IW-F1) of 0.85 and BIS accuracy of 0.88, resulting in an estimated 31.6% reduction in operational costs through earlier and more accurate escalation of critical events. The suitability of the selected datasets is justified by their complementary roles: publicly available KPI time-series datasets represent real-world network telemetry behavior, while the AI4I_2020 dataset provides structured severity and operational context, enabling joint evaluation of failure prediction accuracy, escalation timeliness, and business impact modeling. By prioritizing alerts based on impact-aware severity rather than raw anomaly scores, the proposed framework directly supports operational cost reduction through earlier mitigation and improved decision-making in network operations. The proposed approach bridges the gap between anomaly detection and intelligent alert management by incorporating business relevance into predictive modeling. This dual-stream architecture offers a scalable and proactive solution for AIOps-driven network reliability and automated service resilience.
14 July 2026
Read appraisal →PloS one
Artificial Intelligence in emergency department triage: A scoping review
BACKGROUND: Triage in emergency departments (ED) is a critical process for prioritizing care and ensuring clinical safety. However, current triage systems often exhibit vulnerabilities that compromise the efficiency and quality of healthcare delivery. Artificial Intelligence (AI) has emerged as a promising innovation to support decision-making and optimize patient flow in these high-pressure environments. OBJECTIVE: To map the available evidence regarding the implementation and performance of artificial intelligence in emergency department triage. METHOD: This scoping review followed the Joanna Briggs Institute (JBI) methodology and the PRISMA-ScR guidelines. A comprehensive search was conducted across 13 databases (CINAHL, Cochrane Library, PubMed Central, SciELO, Web of Science, SCOPUS, Science Direct, VHL, Embase, and several regional dissertation repositories), with no language or time restrictions. Two independent reviewers performed the selection process using the Rayyan platform, with discrepancies resolved by a third evaluator. Data were synthesized using the PAGER framework, categorizing findings into Patterns, Advances, Gaps, Evidence for practice, and Recommendations for research. RESULTS: Nineteen studies met the inclusion criteria. AI was primarily implemented through Machine Learning (ML) algorithms, including Deep Learning architectures. Natural Language Processing (NLP) was frequently employed to process unstructured clinical data, with recent studies exploring the potential of Large Language Models (LLMs). Overall, ML-based models consistently outperformed traditional triage systems in predictive accuracy. These techniques were mainly utilized for automated classification, predicting clinical severity, and enhancing patient prioritization by integrating both objective and subjective assessment data. CONCLUSIONS: The findings indicate that AI has significant potential to enhance emergency triage by streamlining service flows and providing robust clinical decision support. However, the current evidence remains heterogeneous and largely exploratory. Key challenges include variability in model performance, a lack of external validation, and studies often limited to specific populations. Consequently, many current tools still lack the necessary reliability for safe, large-scale clinical implementation.
27 June 2026
Read appraisal →European journal of hospital pharmacy : science and practice
Unit dose drug dispensing systems in hospitals: a systematic review of medication error reduction and cost-effectiveness
BACKGROUND: Medical errors pose significant risks to patient safety and public health. Automated unit dose drug dispensing systems (UDDSs) have emerged as valuable tools to reduce medication errors while optimising economic and logistical resources. OBJECTIVES: This systematic review aims to evaluate studies specifically focused on the impact of automated UDDSs in reducing medication errors and streamlining processes. METHODS: A literature search was performed on PubMed, Scopus, and Web of Science, focusing on peer-reviewed articles published between 2019 and 2024. The search, concluded on 24 September 2024, included studies conducted in inpatient hospital settings that assessed automated UDDS effects on medication errors, therapy management and inventory control. Outcomes examined included effects on patient safety, cost-effectiveness and inventory management. Results were synthesised qualitatively. RESULTS: From 3346 references, four studies met the inclusion criteria: a cost-effectiveness analysis, an uncontrolled before-and-after study, and two observational studies. UDDS improved medication processes, reducing drug-related problems, medication handling and dispensing time by 50% per patient per day. Integrated with barcode scanning, UDDS lowered medication administration errors (MAEs) from 19.5% to 15.8% and harmful MAEs from 3.0% to 0.3%. Overall, medication errors dropped by 45-70%, enhancing safety and reducing manual handling risks. UDDS demonstrated cost-effectiveness by significantly reducing MAEs. The study estimated a reduction in MAEs, with a cost-effectiveness ratio of €17.69 per avoided MAE. For potentially harmful MAEs, the cost-effectiveness ratio was estimated at €30.23 per avoided error. These findings suggest substantial long-term savings potential, though the exact magnitude may vary depending on hospital size and implementation specifics CONCLUSIONS: Automated UDDSs improve patient safety by significantly reducing medication errors and delivering cost savings through better inventory management. Challenges such as high initial costs and workflow adjustments can be mitigated through gradual implementation and staff training. Further integration with other healthcare technologies, such as barcoding, real-time tracking, artificial intelligence (AI)-driven error prevention tools and fully automated restocking systems could enhance UDDS benefits and further support hospital processes.
25 June 2026
Read appraisal →Current opinion in anaesthesiology
Preoperative considerations for nonoperating room anesthesia
PURPOSE OF THE REVIEW: This review aims to address the unique challenges in nonoperating room anesthesia (NORA) locations, emphasizing the importance of patient selection, risk stratification, and comprehensive preoperative evaluation to ensure safe anesthetic care for increasingly complex patients. RECENT FINDINGS: The volume of NORA procedures has risen significantly, with patients often presenting higher comorbidity burdens and advanced age. Standardized protocols and validated assessment tools, such as the STOP-Bang questionnaire and a simple frailty questionnaire (e.g., FRAIL), can aid anesthesiologists in effectively stratifying risk and tailoring anesthesia plans. Challenges in NORA include logistical constraints, personnel dynamics, and environmental factors that can compromise patient safety. The integration of telemedicine and artificial intelligence into preoperative assessments shows promise in improving efficiency and safety by allowing for remote evaluations and tailored care. SUMMARY: Adequate patient selection and preoperative optimization are essential for enhancing outcomes in NORA locations. Adherence to national safety guidelines and multidisciplinary collaboration is crucial for navigating the complexities of remote anesthesia care. Future research should focus on refining preoperative screening methods and utilizing artificial intelligence to better address the unique needs of patients undergoing procedures outside traditional operating rooms.
23 June 2026
Read appraisal →The Journal of nursing administration
Virtual Nursing Programs in Acute Care Settings: A Scoping Review of Patient, Nurse, and System-Level Outcomes
OBJECTIVE: To synthesize the literature on the influence of virtual nursing (VN) on patient, nurse, and system-level outcomes in acute care. BACKGROUND: Persistent nursing workforce challenges, including staff shortages and turnover, have been intensified by the COVID-19 pandemic, rising patient acuity, and increasing documentation demands. Many health systems have adopted VN programs, where remote nurses support bedside staff using audiovisual technology. Although these programs are rapidly expanding, evidence of their effectiveness remains limited. METHODS: These authors conducted a scoping review following PRISMA guidelines. Eleven studies reporting patient, nurse, or system-level outcomes were included. RESULTS: Most studies were cross-sectional pilots. Evidence was strongest for nurse and patient satisfaction, with reports of improved discharge efficiency, reduced administrative burden, and higher patient satisfaction. Findings for other outcomes, including safety indicators and financial metrics, were inconsistently reported. CONCLUSIONS: Virtual nursing shows promise for enhancing patient satisfaction and workflow efficiency, but cannot replace investments in sufficient bedside staff and resources.
21 June 2026
Read appraisal →