Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 27 appraisals
JMIR nursing
Nurses' Experiences Using AI in Clinical Practice: Systematic Review
BACKGROUND: Artificial intelligence (AI) tools are increasingly used in clinical settings, yet most syntheses focus on nurses' attitudes or readiness rather than experiences after direct use in practice. OBJECTIVE: This study aimed to synthesize registered nurses' experiences of using AI in clinical practice and to identify perceived benefits, barriers, and implementation implications. METHODS: We conducted a systematic literature review of empirical studies reporting nurses' experiences of AI use in clinical settings. Searches were performed in CINAHL, Embase, MEDLINE, PsycINFO, and PubMed (last search: September 13, 2023). Moreover, 2 reviewers (AJSS and QZ) independently screened titles, abstracts, and full texts and appraised included studies using the Mixed Methods Appraisal Tool. We used thematic synthesis with a primarily deductive framework based on the Technology Acceptance Model 2, with the addition of facilitating conditions from the unified theory of acceptance and use of technology. RESULTS: In total, 20 studies met the inclusion criteria. Perceived usefulness and facilitating conditions were most frequently reported; nurses described AI as supporting decision-making, workflow efficiency, and confidence when implementation included adequate training, interoperability, and technical infrastructure. Ease of use was closely tied to interface design and training. Job relevance and output quality were rated more positively when nurses described AI as aligning with nursing tasks and as producing interpretable, reliable outputs. Common barriers included usability issues, limited integration into workflows and electronic systems, privacy and trust concerns, and inconsistent or poorly contextualized outputs. Across studies, nurses often described adoption as conditional on organizational readiness and meaningful involvement of nurses in design and implementation. CONCLUSIONS: Nurses' accounts suggest that AI may augment clinical work, but the perceived benefits reported in the included literature were contingent on workflow alignment, usable interfaces, training, and supportive infrastructure. These findings suggest potential value in involving nurses in the co-design and iterative refinement of AI tools, grounded in the consistent evidence that facilitating conditions, such as training, interoperability, and organizational readiness, are the primary determinants of whether AI is experienced positively. Prospective evaluation using nursing-relevant outcomes, such as usability, workflow integration, and trust, is needed to move beyond post hoc experiential accounts and establish what implementation conditions reliably produce benefit.
26 June 2026
Read appraisal →International journal of medical informatics
An AI-assisted framework for the ethical use of machine learning in healthcare
This study develops and evaluates ETHICS, a concise, clinician-facing ethical protocol for the routine use of machine learning (ML) in healthcare. Using ChatGPT for first-stage drafting, we generated six actionable principles - Equity and Fairness, Transparency and Patient-Centered Care, Human Oversight and Clinical Integrity, Information Privacy and Data Governance, Continuous Improvement and Sustainability, and Support and Education for Professionals - structured as a mnemonic to support uptake in time-constrained clinical settings. Outputs were treated as provisional and refined through mandatory human source verification, iterative readability optimization, multidisciplinary expert review, and scenario-based stress testing. Readability analysis showed substantial improvement from high complexity to clinician-accessible language. Expert ratings indicated strong endorsement with excellent inter-rater reliability. Implementation readiness was confirmed across five clinical scenarios, all passing predefined adequacy thresholds. The findings suggest generative AI can accelerate ethical protocol drafting, but validity and practicality depend on structured human oversight and real-world testing.
16 June 2026
Read appraisal →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 →BMC health services research
Anticipated barriers and facilitators of implementing a digital behavioral treatment for chronic pain: a qualitative interview study
BACKGROUND: In digital health innovations, sustainable implementation should be considered from the initiation of the development to ensure that the solution fits into the existing healthcare system. This study aims at identifying barriers and facilitators for the prospective implementation of a digital behavioral intervention for chronic pain in the Swedish healthcare system, as perceived by key stakeholders. METHODS: Eight semi-structured individual interviews were conducted with stakeholders (n = 2 IT developers, n = 4 healthcare managers, n = 2 healthcare professionals). Qualitative data were analyzed using a framework analysis following the Consolidated Framework for Implementation Research (CFIR). RESULTS: A total of 86 facilitators and 38 barriers to prospective implementation were identified across stakeholder interviews. Facilitators included perceived relevance of the digital intervention and organizational readiness. Identified barriers primarily related to insufficient training and supervision, unclear decision-making processes (e.g., specific actions, responsibilities of stakeholders), and the lack of systematic needs assessment to guide implementation planning. CONCLUSION: Overall, findings indicate a high level of openness toward digital interventions within the implementation context. While implementation context largely demonstrates readiness, successful integration will depend on proactively addressing identified barriers. Awareness of these factors enhances the likelihood of successful and sustainable implementation.
1 June 2026
Read appraisal →Journal of medical Internet research
Beyond Time Saved: Implementation, Equity, and the Utility Threshold for Nursing AI Scribes
Schwabe et al's pre-post time-motion study of a domain-specific artificial intelligence (AI) speech assistant used by nurses in German long-term care provides one of the few real-world, full-shift evaluations of an AI scribe deployed to a nonphysician workforce, with paired objective observation and self-reported outcomes. This commentary points to the implications of these findings that extend well beyond the time savings headline. The study reports substantial reduction in self-reported documentation time and increased satisfaction with the documentation system, yet workplace satisfaction and the perception that AI scribes are "a good idea to implement" did not improve. Taken together, these findings show three undertheorized issues for AI scribe implementation in nursing and long-term care. First, postimplementation increases in time spent reviewing entries and retrieving information indicate that AI scribes redistribute cognitive effort from authoring to verification, with unknown consequences for satisfaction, mastery, and error detection. Second, the apparent paradox of rising documentation satisfaction alongside falling expectations of AI quality represents user calibration. Third, the substantial equity considerations of automatic speech recognition documentation reflect a broader trend of AI scribe studies that treat equity as a caveat, rather than treating equitable performance as empirically measurable and testable across variations in linguistic styles, dialects, and social linguistic dimensions. To advance the field, the next generation of nursing AI scribe research must treat documentation as a heterogeneous bundle of authoring, reviewing, retrieving, and verifying activities with distinct satisfaction and error profiles; specify and validate end-user-defined anchor utilities, rather than having a narrow focus on diffuse improvement; and treat equity testing and reporting of both automatic speech recognition systems and workforce adoption as standard reporting expectations, rather than caveats.
29 May 2026
Read appraisal →Journal of medical Internet research
Artificial Intelligence in Patient-Centered Care and Macro-, Meso-, and Micro-Level Determinants of Rehumanization and Dehumanization: Qualitative Interview Study
BACKGROUND: Patient-centered care remains a foundational principle of modern health care. The digital transformation of health systems has accelerated the adoption of artificial intelligence (AI) across diagnostic, predictive, and communicative functions, with implications for efficiency and clinical workflows. At the same time, AI integration raises concerns regarding transparency, equity, accountability, and trust, positioning it as a potential driver of both rehumanizing and dehumanizing dynamics in health care practice. OBJECTIVE: This study examines how the adoption of AI in health care may influence patient-centered care, exploring its potential to promote rehumanization or contribute to dehumanization. The objective is to identify the factors that shape these outcomes at the macrolevel (policy and infrastructure), mesolevel (institutional practices), and microlevel (individual behaviors and interactions). METHODS: This study adopts an exploratory qualitative design informed by grounded theory principles, drawing on 20 semistructured interviews with health care leaders, clinicians, researchers, legal experts, and industry consultants who have substantial professional experience across European health care systems, with some participants also contributing experience from the US health care context. To enhance analytical rigor and transparency, the study applied the Gioia methodology, enabling inductive coding from first-order concepts to second-order themes and aggregate dimensions. This multistakeholder approach facilitated a nuanced examination of how AI integration is perceived and experienced across macro-, meso-, and microlevels of health care. RESULTS: The analysis identified key system-level factors shaping rehumanizing or dehumanizing outcomes of AI integration. At the macrolevel, 8 factors-including regulatory frameworks, policy priorities, and infrastructure-were identified as influencing whether efficiency pressures outweigh patient-centered values. At the mesolevel, 5 factors related to institutional strategies, workflows, and leadership shape how AI tools are embedded into care delivery. At the microlevel, 7 factors related to individual behaviors, trust, and doctor-patient interaction dynamics influence whether AI supports empathy and engagement or diminishes them. Rehumanizing potentials include reduced administrative burden, improved care pathways, clearer health communication, and enhanced decision-making, while risks include shorter consultations, reduced empathy, overreliance on automation, and erosion of professional identity. Without deliberate alignment with patient-centered principles, efficiency gains risk undermining the human dimensions of care. CONCLUSIONS: This study represents one of the first empirical examinations of how AI shapes health care practices through rehumanizing and dehumanizing dynamics. The findings demonstrate that outcomes depend not only on technical capabilities but also on regulatory frameworks, institutional strategies, and cultural adaptation. By systematically mapping influencing factors across macro-, meso-, and microlevels, the research provides actionable insights for decision-makers to ensure that efficiency gains remain aligned with patient-centered principles. Realizing AI's promise requires coordinated action to preserve empathy, trust, and interpersonal connection, ensuring that innovation strengthens rather than weakens the human dimensions of care.
28 May 2026
Read appraisal →Medicine
Effects of a psychological capital intervention on emergency nurses' work engagement and turnover intention: A randomized controlled trial
BACKGROUND: Emergency nurses are exposed to high occupational stress, which leads to decreased psychological capital, reduced work engagement, and high turnover intention. Evidence on targeted psychological capital intervention (PCI) for this population remains limited. METHODS: A parallel randomized controlled trial was conducted. A total of 122 emergency nurses from a tertiary hospital in Zhejiang Province were randomly assigned to the intervention group (6-week PCI, n = 61) or the control group (routine training, n = 61). Outcomes were measured at baseline (T0), post-intervention (T1), 1 month (T2), and 3 months (T3) using the Psychological Capital Questionnaire, Utrecht Work Engagement Scale, and Turnover Intention Scale. RESULTS: Baseline characteristics and outcome scores were comparable between groups (all P > .05). At T1 to T3, the intervention group had significantly higher Psychological Capital Questionnaire and Utrecht Work Engagement Scale scores and lower Turnover Intention Scale scores than the control group (all P < .001). Psychological capital was positively correlated with work engagement (R = 0.604, P < .001) and negatively correlated with turnover intention (r = -0.624, P < .001). CONCLUSION: This 6-week PCI program significantly improves psychological capital and work engagement, and reduces turnover intention among emergency nurses, with effects sustained for at least 3 months. These findings support the utility of PCI as a psychological management strategy for emergency nursing staff.
25 May 2026
Read appraisal →Studies in health technology and informatics
Electronic Health Record System Support for Physician-Patient Communication: National Cross-Sectional Survey on Outpatient Physicians' Experiences
The impact of digital patient engagement platforms (DPEPs) on interactions between physicians and patients has been widely studied from patients' perspectives. However, research into physicians' viewpoints remains scarce. This study aimed to identify electronic health record (EHR) characteristics that support physician-patient collaboration from the perspective of physicians working in outpatient care. A national cross-sectional survey of physicians' experiences with EHRs was conducted in Finland in 2021 where EHR coverage had reached 100% already in 2010. We selected the responses of 1983 physicians working in public sector outpatient clinics (primary and specialized care). Linear regression analysis was used to examine the relationship between eight EHR characteristics and perceived support for physician-patient collaboration and information exchange. Of these, rapid system responsiveness, ease of utilizing patient-provided data, support for adherence to treatment recommendations, assistance in preventing medication errors, and comprehensive and reliable patient data were found to be associated with support. The model accounted for 27% of the variance in the dependent variable. In addition to enhancing care outcomes, clinical decision support and easy access to patient data can aid physicians in interactions with patients. Further investigations should continue to analyze how these identified features could better support collaboration with patients - beyond DPEPs and visible interactions during appointments. Considering the rapid evolution of DPEPs, future studies should also take into account the depth of the DPEP-EHR integration.
24 May 2026
Read appraisal →Studies in health technology and informatics
Electronic Patient Records, Real-Time Clinical Documentation and Burden: An Umbrella Review
This umbrella review synthesises evidence from reviews on clinicians' documentation burden (DB) associated with electronic patient records (EPRs) in hospital and primary care settings. Guided by Donabedian's model (structure, process, outcomes), it maps causes of burden, barriers to real-time documentation, and technological solutions. Following the Joanna Briggs Institute (JBI) methodology, searches across six databases identified 2,739 records, with 166 under full-text review. Findings of this review will inform strategies to optimise real-time EPR use while minimising DB.
24 May 2026
Read appraisal →PloS one
The carbon footprint of surgical operations: 2023-2025 systematic review update
Surgical care significantly contributes to greenhouse gas emissions, with operating rooms consuming three to six times more energy than other departments relying heavily on single-use materials and anaesthetic gases. We conducted a systematic review of literature from 2023 to 2025 following PRISMA guidelines. The PubMed, Scopus and Healthcare LCA databases were searched, and eligibility criteria were applied. A total of 26 studies met inclusion criteria. Data were extracted on each study's scope and system boundaries, reported emission values, identified carbon hotspots, and any mitigation strategies evaluated. A structured quality assessment was conducted to evaluate the reliability of the included studies and to identify potential sources of bias. The included studies demonstrated wide variability in reported carbon footprints of surgery, ranging from under 5 kgCO₂e for minor procedures to over over 400 kgCO₂e for complex single-stage operations and approximately 1,000 kgCO₂e for whole multistage patient pathways. This systematic review underscores that surgical operations could have a significant carbon footprint, with emissions hotspots concentrated in consumable materials and energy consumption. It also reveals substantial variability and methodological heterogeneity in how surgical carbon footprints are calculated, pointing to the urgent need for standardized life-cycle based frameworks in surgical settings. Establishing common standards will enable more reliable benchmarking of surgical emissions and better comparisons across studies.
19 May 2026
Read appraisal →BMJ open
Implementation of safety management systems (SMS) in healthcare: a systematic review and international comparison
OBJECTIVES: Many industries where safety is a priority (eg, aviation) use safety management systems (SMSs), but evidence on their use in healthcare is needed to determine whether they could support patient safety improvement. We investigated the application of national-level SMSs to patient safety in terms of effectiveness, implementation and experience. DESIGN: Systematic review using a case study approach. DATA SOURCES: We identified patient safety organisations in each country and searched their websites. We also searched MEDLINE (in December 2023) and Embase (via Ovid), CINAHL (EBSCO) and Web of Science (in February 2024). ELIGIBILITY CRITERIA: Any evidence from five high-income countries that have publicly funded healthcare systems with universal coverage: Australia, Canada, Ireland, New Zealand and the Netherlands. We included publications on the effectiveness, implementation or experience of the patient safety approach in each country. DATA EXTRACTION AND SYNTHESIS: Included evidence was summarised and mapped onto an initial analytical framework based on analysis of SMSs in high-risk industries, enabling cross-country comparisons. Drafts were shared with experts from patient safety organisations in each country for comment. Formal quality appraisal was not possible as most evidence was non-evaluative. RESULTS: 53 publications were included, from Australia (5), Canada (7), Ireland (8), New Zealand (9) and the Netherlands (24). Only the Netherlands implemented a patient safety programme explicitly based on an SMS approach. Some aspects of patient safety in hospitals improved as a result, but there was significant variation in both the implementation of the programme and attributed outcomes.In the other four countries, the main components of an SMS were identified to some extent, along with evidence that their patient safety approaches had been influenced by concepts from high-risk industries and 'safety science' more widely. CONCLUSIONS: Emerging ideas from high-risk industries (beyond SMSs) and broader safety science have influenced all countries, reflecting increasing awareness of the need for initiatives to be context-specific to be successful. However, their implementation and impact need further evaluation. PROSPERO REGISTRATION NUMBER: CRD42023487512.
15 May 2026
Read appraisal →Systematic reviews
Barriers and facilitators to the implementation and adoption of computerised clinical decision support systems: an overview of reviews
BACKGROUND: The use of computerised clinical decision support systems (CDSSs) holds considerable potential in enhancing healthcare delivery by improving patient safety, practitioner performance, and patient outcomes. However, despite the numerous reported advantages of CDSSs, their adoption remains restricted, thereby compromising the full realisation of their potential. To enable a profound evaluation and successful implementation of these systems, it is imperative to identify the reasons for their limited uptake. The objective of this study is to provide a comprehensive overview of the barriers and facilitators to the implementation and adoption of decision support systems across healthcare settings. METHODS: This study was reported in accordance with the Preferred Reporting Items for Overviews of Reviews (PRIOR) statement. A systematic search was conducted in the databases PubMed, IEEE Xplore, Scopus, and Web of Science from inception to 15 October 2024, targeting at review articles of primary studies focusing on the identification and reporting of barriers and facilitators to the implementation and adoption of CDSSs across healthcare settings. The risk of bias of the included reviews was assessed with the Risk of Bias in Systematic Reviews (ROBIS) tool, and the primary study overlap was calculated using the corrected covered area method. All data on barriers and facilitators to the implementation and adoption of CDSSs were extracted from the included reviews, synthesised through a mapping of identical factors, and ranked according to their frequency of occurrence. RESULTS: Of the 1640 records retrieved through database searches, 30 reviews were included in this overview, which summarised the findings of 721 unique primary studies. A total of 101 distinct factors influencing the implementation and adoption of CDSSs were identified and could be categorised into the overarching categories of human, technology-related, and contextual factors. The following six factors were reported most frequently: usability, usefulness/perceived benefits, organisational readiness, training, trust, and workflow integration. CONCLUSIONS: The findings of this study highlight the diversity of factors that influence a successful implementation of CDSSs and emphasise the need for their comprehensive evaluation that goes beyond the assessment of general performance aspects but takes into account human and contextual factors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024507614.
15 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Speed and quality of complex strategic decisions
Many strategic decisions involve both substantial complexity and time pressure, but the association between decision speed and decision quality of cognitively demanding strategic decisions is not well understood. This paper presents evidence on this question using a setting with exceptionally detailed and precise information about decision times and decision quality-it analyses move-by-move data from in-person professional chess tournaments. Decision quality is measured by comparing actual moves to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results show that faster decisions are associated with higher decision quality, even after accounting for computational complexity, distinctiveness between alternatives, and time pressure. Greater computational complexity and lower distinctiveness between move alternatives are associated with longer decision times, whereas greater time pressure is associated with shorter decision times. All three factors are associated with lower decision quality. We discuss the findings against the predictions of different decision models in which individuals sequentially acquire information about alternatives with uncertain valuations, extending theories originally developed in the context of nonstrategic decisions to a strategic environment.
15 May 2026
Read appraisal →PloS one
Technological impact, generality, and complementarity of artificial intelligence patents: Evidence from Samsung Electronics.
This study examines the extent to which artificial intelligence (AI) technologies are more pervasive and technologically complementary than non-AI technologies. Drawing on the literature on general purpose technologies (GPTs), we hypothesize that AI technologies exhibit greater pervasiveness-reflected in higher technological impact and broader generality-and stronger technological complementarity than non-AI patents. Using a firm-level analysis of patents filed by Samsung Electronics between 1982 and 2018, we find that AI patents demonstrate significantly higher impact, wider generality, and stronger systematic complementarity compared to matched non-AI patents. These results are robust across alternative matching specifications and multiple operationalizations of the key variables. Our findings offer firm-level empirical supporting AI's GPT-like characteristics and shed light on how AI-driven innovations may generate sustained incentives for technological advancement, even amid processes of creative destruction. The paper concludes by discussing key theoretical, managerial, and policy implications.
11 May 2026
Read appraisal →PloS one
FairCareNLP: An AI-driven patient review analyzer for healthcare.
OBJECTIVE: To develop and evaluate an automatic patient review analyzer that applies advanced Natural Language Processing (NLP) and machine learning methods to improve the efficiency, fairness, and accuracy of healthcare feedback analysis. MATERIALS AND METHODS: We designed a multi-component pipeline incorporating sentiment analysis, key theme extraction, clinical Named Entity Recognition (NER), and fairness modules. Bias mitigation was addressed through the integration of three complementary approaches: adversarial debiasing, Hard Debiasing, and Iterative Null-space Projection (INLP). Multiple BERT-based models (DistilBERT, BioBERT, RoBERTa-base, BERT-base-uncased) were trained and evaluated under varying hyperparameters and fairness/adversarial loss configurations. Model performance was assessed using accuracy, F1, recall, precision, AUC, Equalized Odds (EOD), and Word Embedding Association Test (WEAT) metrics. RESULTS: Adversarial loss ([Formula: see text]) consistently decreased model performance across accuracy, F1, precision, and recall. In contrast, Hard Debiasing and INLP improved WEAT scores while preserving or enhancing other metrics, with INLP yielding the best overall performance. Specifically, INLP with fairness loss improved EOD by 14%, gender WEAT scores by 15%, and achieved slight gains for ethnicity and socioeconomic WEAT scores. The best model achieved accuracy of 0.856, F1 score of 0.812, recall of 0.798, and precision of 0.829. The key theme analysis module identified 82% of expert-labeled themes, though 21% of patient comments lacked expert labels for valence or related attributes. DISCUSSION: Our results demonstrate the trade-offs between fairness and performance in bias mitigation strategies. While adversarial debiasing reduced predictive accuracy, INLP and Hard Debiasing improved fairness without significant degradation in task performance. Gender bias was easier to mitigate than multi-class sensitive attributes such as ethnicity and income. This difference indicates a need for fairness techniques designed for multi-class sensitive attributes. CONCLUSION: This work presents an NLP pipeline for patient feedback analysis with multiple debiasing strategies. This pipeline improves the fairness and accuracy of insights from unstructured patient reviews and supports inclusive patient-centered care.
11 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 →PloS one
A GNN-based approach for accurate trade balance forecasting and interpretable analysis
In this study, we developed a machine learning pipeline to predict trade balances across 229 countries, utilizing a Graph Neural Network (GNN), and compared it with several deep learning and regression-based models. The data preprocessing involved handling missing values, normalizing features, and conducting exploratory data analysis to uncover key patterns. Feature selection was performed using a Random Forest Regressor to identify the most influential predictors of trade balances. We then evaluated multiple models, including a complex Deep Neural Network (DNN), Transformer with multi-head attention, Random Forest, and a hybrid ensemble model, using various regression metrics. Among these, the GNN proved to be the most effective model, achieving an MSE of 0.06, RMSE of 0.26, MAE of 0.18, and an R² of 0.91. These results demonstrate that GNN outperforms other models in terms of accuracy, robustness, and consistency in predicting trade balances. We compared models across several key evaluation metrics and conducted a detailed comparison of residual plots to assess prediction quality and error distribution. Residual plots and ROC curves were used to validate the reliability and performance of the GNN and other models, ensuring robust and accurate predictions across the board. This study highlights the potential of machine learning techniques to improve trade balance forecasting, providing policymakers and economists with a more adaptable and precise tool for navigating complex global trade dynamics. The findings contribute to more informed economic strategies and enhanced forecasting methodologies.
4 May 2026
Read appraisal →BMJ open
From legalisation to practice: implementation of newly legalised health interventions in healthcare settings - a systematic review.
OBJECTIVES: Over the last decade, a growing number of health interventions (eg, medical assistance in dying and mitochondrial donation) have become legalised or decriminalised globally. Newly legalised health interventions share characteristics that are distinct from other health interventions, making their implementation more challenging. They are often highly emotive, controversial and associated with strong opinions and ethical dilemmas, with some of them being high-stake and irreversible. This study aimed to identify, systematise and map the factors that affect the implementation of health interventions that have recently been legalised. DESIGN: A systematically conducted review. DATA SOURCES: PubMed, Scopus, EMBASE and CINAHL were searched to identify studies published between 2014 and 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included studies if they evaluated the implementation of health interventions that were newly legalised or newly decriminalised. DATA EXTRACTION AND SYNTHESIS: Data were extracted and synthesised through descriptive analysis. Both deductive and inductive thematic analyses were applied to map the barriers, facilitators and implementing strategies that influence the implementation of newly legalised health interventions in healthcare settings. RESULTS: The search strategy yielded 1510 publications, of which 78 were included in this review. Findings showed that several newly legalised health interventions, including medical assistance in dying (n=56 studies); medical abortion (n=13); assisted human reproduction (n=3); psychedelic-assisted therapies (n=3); use of medical cannabis (n=2) and use of biosimilars (n=1) were addressed. The analysis identified a total of 880 diverse barriers, facilitators and strategies in five domains across system, organisational and individual levels: (1) patients/service users/consumers; (2) healthcare providers; (3) healthcare organisation; (4) legal processes and (5) system. These were further divided into 27 themes of barriers, 18 themes of facilitators and 17 themes of strategies. CONCLUSIONS: Implementing newly legalised health interventions is complex. Our findings can support the development of an implementation plan for the spread and scaling of future health interventions, maximising the impact of interventions and making them accessible to more people and health organisations.
24 Apr 2026
Read appraisal →Journal of health organization and management
Predicting CSR authenticity in healthcare organizations: an integrated SEM-IPMA-ANN approach through the lens of self-compassion theory
PURPOSE: As healthcare business organizations face mounting pressure from civil society to act responsibly, they are expanding and strategically restructuring their CSR activities. Under the umbrella of CSR, cause-related marketing (CRM) campaigns are a means of fundraising available to healthcare firms engaging in socially responsible activities to improve global health. While healthcare organizations increasingly emphasize CSR implementation, its precise nature within the context of CSR authenticity has not been systematically investigated. Adopting self-compassion theory as a platform, this study has demonstrated novel relationships for gaining deeper insights into the key predictors of CSR authenticity in healthcare organizations. DESIGN/METHODOLOGY/APPROACH: The authors collected 882 responses and adopted the hybrid SEM-IPMA-ANN (Structural Equation modelling- Importance Performance Map Analysis- Artificial Neural Network) method to confirm the proposed model's significant predictors influencing CSR authenticity. FINDINGS: The findings provide an extended knowledge of self-compassion theory, thus contributing to the extant literature on CSR domain and healthcare marketing. The ANN findings established that social connectedness was the strongest predictor of CSR authenticity, followed by trust in the CRM campaign, isolation, mindfulness, common humanity, self-kindness, over-identification, and self-judgement. The IPMA result, consistent with ANN findings, revealed that social connectedness and trust in CRM campaigns were of relatively higher importance when predicting CSR authenticity. ORIGINALITY/VALUE: The study contributes to our knowledge, and the findings would have implications for CSR practitioners in developing effective CRM campaigns for healthcare organizations. The conclusions would offer invaluable intelligence to CRM marketers, healthcare service practitioners, ad-campaign business professionals, and non-profit managers to manage critical and informed decisions.
20 Apr 2026
Read appraisal →Journal of health organization and management
Implementing telemedicine for cancer care in European healthcare organizations: lessons from the eCAN Joint Action
PURPOSE: Telemedicine represents a promising innovation to complement conventional cancer care in Europe to improve treatment quality and patient outcomes. Funded between September 2022 and December 2024, the Joint Action "Strengthening eHealth including telemedicine and remote monitoring for health care systems for CANcer prevention and care" (eCAN JA) aimed to bring the benefits of telemedicine to cancer patients across the European Union (EU) Member States, with 35 partner organizations from 16 countries and explored its feasibility through multicenter clinical trials. This study sheds light on key factors to implement telemedicine services for cancer care in European healthcare organizations (HCOs) and offers a forward-looking perspective by conducting a foresight exercise as part of the Sustainability Work Package (WP4) of the eCAN JA. DESIGN/METHODOLOGY/APPROACH: Foresight is an umbrella term for innovative strategic planning, policy formulation and solution design methods that empower decision-makers. For our multi-country foresight study, we followed three sequential steps to gain qualitative and quantitative findings on the facilitators of telemedicine services for cancer patients in clinical settings, comprised of (1) literature review; (2) surveys to HCOs in different EU Member States and (3) a foresight workshop with survey respondents. FINDINGS: Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility and addressing resource constraints for hybrid care models for success. Future policies in the EU should focus on establishing telemedicine training for healthcare professionals and support interoperability as well as user-friendliness of telemedicine services in HCOs. Policies should also address clinical workload challenges, enable harmonized telemedicine protocols across Europe, provide incentives for implementation and invest in digital infrastructure in HCOs for a sustainable telemedicine adoption. ORIGINALITY/VALUE: To the best of our knowledge, this is the first study to conduct a foresight analysis on the feasibility of telemedicine for cancer care across different HCOs in EU Member States.
20 Apr 2026
Read appraisal →BMJ open
Report cards and outcome measurements to improve the safety of surgical care (North America): an updated systematic review from Making Healthcare Safer IV
OBJECTIVES: In the USA, an estimated 40-50 million operations are performed annually, with high rates of adverse events. Since the 1980s, report cards have been used for outcome measures and to improve safety of surgical care. As part of Making Healthcare Safer IV-an initiative aimed at publishing evidence-based reviews as they are completed to help healthcare leaders, researchers and policymakers act more quickly on evidence-supported practices-we performed an updated review on the certainty of evidence on patient safety practices related to the use of surgical report cards and outcome measurements. DESIGN: Systematic review using the Grade of Recommendations Assessment, Development and Evaluation (GRADE) approach. DATA SOURCES: PubMed, Web of Science, Scopus and the Cochrane Library were searched from November 2011 to May 2023. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: We included primary research studies (randomised control trials or observational studies with a comparison group, including pre-post studies) or observational studies that investigated a surgical report card in adult or paediatric surgical patients at the hospital or surgeon level in inpatient or outpatient settings. Excluded studies included: narrative reviews, scoping reviews, editorials, commentaries, abstracts, studies that measured only patient knowledge or levels of engagement or studies using local surgical dashboard data. DATA EXTRACTION AND SYNTHESIS: Screening and eligibility were done in duplicate, while data extraction was done by one reviewer and checked by a second reviewer. Specific items in the Risk Of Bias In Non-randomised Studies - of Interventions tool and a modification of the National Institutes of Health Tool were used to assess for bias in studies. Two reviewers assessed each study for risk of bias. A modified version of the GRADE framework was used to assess the certainty of evidence. RESULTS: We identified 19 studies that met the inclusion criteria: 13 primary research studies and 6 descriptive studies of surgical collaboratives. Of the primary studies, nine used a pre-post or longitudinal study design and four used a regression discontinuity or concurrent controlled design. Seven of the studies were about the American College of Surgeons National Surgical Quality Improvement Project. Five studies were from single institutions and the remainder included nine to greater than 700 hospitals. Pre-post studies of report cards that prompted quality improvement (QI) programmes all reported improvements in outcomes, longitudinal studies reported benefits in some but not all outcomes and one in four controlled before-and-after studies reported a statistically significant mortality benefit. All studies, except for one, were at moderate or high risk of bias. Six collaboratives were identified with preliminary data. CONCLUSIONS: Based on the above evidence, the theoretical rationale and parallel evidence in other settings, we judged that it was moderate certainty that report cards and outcomes measurements can improve surgical outcomes. However, given the evidence from studies where report cards were actively linked to institutional QI initiatives, we recommend that outcome data must be paired with actionable QI efforts to meaningfully improve patient outcomes.
19 Apr 2026
Read appraisal →PloS one
Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach
INTRODUCTION: As the effectiveness of artificial intelligence (AI) in enhancing various facets of healthcare delivery becomes more apparent, it is anticipated that AI will soon find its way into standard clinical practices, even in low and middle-income countries. The objective of this study was to create an action plan for integrating AI into medical education, research, and clinical practices utilizing the SWITCH model of change integrating both rational and emotional aspects. METHODS: This exploratory qualitative study employed reflexive thematic analysis of semi-structured interviews and a co-design workshop, followed by the collaborative development of an action plan. The study was conducted from May 2023 to May 2024, at the Aga Khan University, Karachi, Pakistan. The development of an action plan was informed by interviews, co-design workshop, and discussions with diverse group of academic leaders, healthcare professionals and medical students. All interviews, workshop sessions, and planning meetings were audio recorded, transcribed verbatim, and anonymized. Data management was conducted manually using Microsoft Word and Excel. Findings after thematic analysis of the qualitative interviews and findings from the co-design workshop were gradually, and inductively transformed into the content for the action plan for integrating AI into healthcare. RESULTS: The content analysis of interviews identified following four themes: AI opportunities, apprehensions toward AI-induced changes, pushing change through leadership styles, and importance of AI related capacity building. During the workshop, participants discussed aligning current AI knowledge with future requirements by identifying clear instructions, emotional motivators, and environmental changes required on the path of AI integration. The proposed action plan conceptualized AI integration as multidimensional change process comprising three domains: strategic actions, change pathway enablement and environmental readiness and human motivation operationalized through twenty actionable components. CONCLUSION: The study findings provide a context specific conceptual action plan for healthcare professionals to integrate AI into medical education, clinical service and research, Future work should focus on pilot implementation and empirical validations of the action plan across diverse healthcare settings to assess feasibility, effectiveness and scalability.
18 Apr 2026
Read appraisal →JMIR formative research
Development of a Contextualized, Research-Based Flemish Assessment Framework for Digital Care, Assistance, and Support: Delphi Study
BACKGROUND: The rapid evolution of digital technologies has transformed health, mental health, and social care, offering new modalities of digital care, assistance, and support through web-based platforms, mobile apps, extended reality, wearables, and artificial intelligence systems. Despite this proliferation, there is little consensus on what constitutes "high-quality" digital care. Challenges persist regarding data security, interoperability, accessibility, sustainability, and professional competence, whereas existing standards and regulations provide fragmented guidance. OBJECTIVE: This study aimed to develop a contextualized, consensus-based quality assessment framework for digital care, assistance, and support in Flanders, Belgium. For this purpose, perspectives across technology, organizational processes, and professional competencies were integrated. METHODS: The study used a multiphase design comprising (1) 10 expert interviews with Flemish government officials; (2) a narrative literature review of 303 peer-reviewed and gray literature sources; (3) a 3-round Delphi study with 50 experts across 5 domains (end users, facilitators, technology developers, deontology and ethics experts, and digital inclusion and media literacy experts); and (4) 4 complementary focus groups and 3 interviews with specialists in artificial intelligence, regulation, social work, mental health, and IT. The Delphi rounds gathered iterative feedback through open-ended elicitation, structured rating, and classification of quality criteria. Quantitative data were analyzed using descriptive statistics, whereas qualitative feedback was subjected to thematic analysis. RESULTS: A total of 50 experts participated in round 1, a total of 40 (80%) participated in round 2, and 27 (54%) participated in round 3. Round 1 generated 577 unique quality criteria, consolidated into 26 clusters organized under 3 pillars: technology, organization, and professional competencies. The relative importance across pillars was balanced (mean score 37.29, SD 12.38 for technology; 33.33, SD 10.39 for professional competencies; and 29.80, SD 10.45 for organizations). Accessibility, reliability, and safety ranked highest for the technology; vision, quality monitoring, and infrastructure ranked highest for organization; and support, digital competencies, and ethics ranked highest for professional competencies. The finalized framework included 112 criteria, of which 35 (31.3%) were designated as optional and 77 (68.8%) were designated as minimum requirements. Focus groups and interviews validated the framework's comprehensiveness and usability, emphasizing proportional implementation, user centrality, and alignment with European Union regulations. Stakeholders highlighted the need for tools, training, and governance mechanisms to ensure adoption and sustainability. CONCLUSIONS: This study produced a codeveloped, context-sensitive quality assessment framework that balances technological robustness, organizational readiness, and professional competence in digital care, assistance, and support. The framework can serve both as a quality safeguard and a developmental road map. Accompanying self-assessment and governance tools enhance practical applicability. Implementation success will depend on governmental support, resource allocation, and structured feedback loops. Future research should pilot the framework in real-world settings, assess its impact, and establish mechanisms for continuous updates to maintain relevance in a rapidly evolving digital landscape.
17 Apr 2026
Read appraisal →Journal of medical Internet research
Preferences for Personalized Text Message Appointment Reminders Among Outpatients in a Universal Health System: Cross-Sectional Study
BACKGROUND: SMS text messaging reminders are widely used to reduce missed outpatient appointments; however, evidence remains limited regarding which types of reminder content patients prefer, particularly within East Asian universal health systems. In Taiwan, minimal financial barriers to care and unrestricted access to secondary and tertiary hospitals contribute to high outpatient visit volumes and persistent no-show rates. These contextual features underscore the need for behaviorally informed and demographically tailored reminder strategies rather than uniform messaging approaches. OBJECTIVE: This study aimed to examine patient preferences for 6 theory-guided SMS appointment reminder types and to identify the predictors of reminder preference related to demographic characteristics and health care utilization, with the goal of informing personalized reminder design for a forthcoming randomized controlled trial. METHODS: We conducted a cross-sectional online survey among adults in Taiwan with prior outpatient experience. Six SMS reminder prototypes were developed based on behavioral communication principles and validated by a multidisciplinary expert panel using item-level content validity indices. Participants selected their preferred SMS reminder type and reported sociodemographic characteristics and recent health care utilization. Bivariate associations were examined using chi-square tests and one-way ANOVA, with Benjamini-Hochberg false discovery rate correction applied to control for multiple testing. To identify independent predictors of SMS reminder preference while adjusting for potential confounding, we fitted a multinomial logistic regression model with all covariates entered simultaneously. RESULTS: A total of 1095 respondents completed the survey. General reminders and messages referencing prior missed appointments were most frequently preferred, whereas empathy-based or relationally framed messages were selected less often. In false discovery rate-adjusted univariate analyses, both age and sex were associated with SMS reminder preference. However, in the fully adjusted multinomial logistic regression model, age emerged as the only statistically significant independent predictor. Participants younger than 50 years were significantly more likely to prefer alternative reminder message types compared with the general reminder (adjusted odds ratio 1.64, 95% CI 1.18-2.28; P=.003). Sex did not retain statistical significance after multivariable adjustment. Other sociodemographic characteristics and health care utilization variables, including education level, employment status, residential region, outpatient visit frequency, and recent missed appointments history, were not independently associated with reminder preference. CONCLUSIONS: Preferences for outpatient SMS reminder content vary systematically, with age representing the most robust independent predictor. Across the sample, concise and behavior-focused reminders were preferred over empathy-oriented or relational formats. These findings support age-informed tailoring of SMS reminder content and provide content-validated SMS prototypes for use in subsequent interventional research. The results offer formative evidence to guide the design of randomized trials aimed at reducing outpatient no-shows and improving the efficiency of ambulatory care delivery in Taiwan's universal health care system.
10 Apr 2026
Read appraisal →JMIR human factors
Predicting Electronic Health Record Usability: Scoping Review of Adoption Models, Metrics, and Future Directions
BACKGROUND: Electronic health records (EHRs) play an essential role in modern health care, enabling data sharing and improving patient safety; however, even though vendors must adhere to International Organization for Standardization-related usability standards for EHR certification, persistent usability issues continue to undermine efficiency, contribute to clinician burden, and increase the risk of preventable errors. OBJECTIVE: This scoping review synthesizes existing research on EHR adoption and usability, emphasizing theoretical models, measurement approaches, factors, and analytic methods used to assess or predict usability. We identify gaps and opportunities for integrating predictive analytics and artificial intelligence (AI) to advance research and improve the usability of EHRs. METHODS: Following Joanna Briggs Institute and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we systematically searched MEDLINE, Web of Science, IEEE Xplore, and Scopus library databases for studies published between January 1, 2009, and April 9, 2025. Inclusion criteria focused on empirical research using predictive methods or models related to EHR usability. Data were charted and synthesized thematically. RESULTS: Of the 2323 screened papers, 47 studies met inclusion criteria. Most research examined or predicted EHR adoption (not usability) using dominant frameworks, such as the technology acceptance model, unified theory of acceptance and use of technology, and the information system success model, which comprised usability. Factors related to usability-particularly perceived usefulness, perceived ease of use, effort expectancy, and facilitating conditions via the EHR adoption models-appeared frequently. Regression-based methods and structural equation modeling were the most common analytic techniques. No studies applied predictive modeling or AI to predict EHR usability. CONCLUSIONS: The focus of this study on the prediction of EHR usability and adoption for the past 15 years is distinctive in the literature. It extends prior usability reviews (mostly focusing on adoption, not prediction of usability). Predictive modeling for EHR usability remains underdeveloped throughout 2009 to 2025. Dominant frameworks in the EHR literature continue to prioritize predicting adoption over operational usability. These models rely heavily on self-reported, cross-sectional measures captured at a single postimplementation time point, embedding systematic bias and obscuring longitudinal usability dynamics. Despite the application of increasingly sophisticated predictive techniques-primarily variants of regression and structural equation modeling-usability has remained analytically subordinate to adoption and acceptance constructs for more than 15 years. As a result, widely used models, such as the technology acceptance model and unified theory of acceptance and use of technology, position usability merely as an antecedent to intention or use, rather than as an independent, system-level property that can be empirically measured, modeled, and predicted. Therefore, there is substantial opportunity to integrate predictive analytics, AI, and longitudinal usability measures to build dynamic models.
10 Apr 2026
Read appraisal →BMJ open
Facilitators of and barriers to teamwork between operating room nurses and other practitioners: a meta-synthesis of qualitative studies
OBJECTIVES: Seamless collaboration in the operating room is the critical foundation for patient safety and successful outcomes. This synergy enhances efficiency, minimises errors and is a key determinant of the final surgical quality. The objective of this study is to synthesise qualitative studies on facilitators of and barriers to teamwork between operating room nurses (ORNs) and other practitioners. DESIGN: Systematic review and meta-synthesis of qualitative studies. DATA SOURCES: A systematic search was conducted across nine electronic databases: PubMed, Embase, Scopus, Web of Science (Core Collection), CINAHL, PsycINFO, CNKI, WanFang and Vip. The search covered the period from 1 January 2014 to 30 September 2025. The search strategy used both medical subject headings and free-text terms, supplemented by the snowball method and grey literature. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Qualitative studies exploring teamwork involving ORNs were included. Of 4839 initially retrieved records, 29 studies finally met the inclusion criteria. DATA EXTRACTION AND SYNTHESIS: Two researchers independently extracted data, including the author, year of publication, country, study design, study objective, interview location, number of participants, age, methods of data collection and analysis and duration of interviews. The key findings were synthesised into two overarching themes: facilitators (eg, professional skills, role awareness, team familiarity, patient-centred culture, effective leadership) of and barriers (eg, individualistic behaviour, hierarchical culture, insufficient resources, punitive management, environmental constraints) to teamwork between ORNs and other practitioners. A meta-synthesis was conducted to synthesise study results from different groups and countries. NVivo 15 was used for data management. RESULTS: A total of 29 qualitative studies (published between 2014 and 2025) were included in this study. Key facilitators of teamwork included professional skills, role awareness, team familiarity, patient-centred culture and effective leadership. Major barriers encompassed individualistic behaviours, punitive management, hierarchical culture, environmental constraints and insufficient resources. These factors worked across individual, interpersonal and systemic levels to shape OR teamwork. CONCLUSION: Optimising OR teamwork requires a multifaceted approach simultaneously addressing these facilitators and barriers. Interventions should simultaneously cultivate individual competencies (eg, skills, role awareness), foster positive team dynamics (eg, familiarity, psychological safety, inclusive leadership), and rectify systemic issues (eg, hierarchical culture, resource constraints, punitive management). In this way, teamwork between ORNs and other practitioners can be enhanced, thereby contributing to improved perioperative safety and outcomes. PROSPERO REGISTRATION NUMBER: CRD42024506414.
9 Apr 2026
Read appraisal →PLoS medicine
Machine learning assisted differentiation of low acuity patients at dispatch: The MADLAD randomized controlled trial
BACKGROUND: Resource Constrained Situations (RCS) at Emergency Medical Dispatch centers where there are more patients requiring an ambulance than there are available ambulances are common. Machine Learning (ML) techniques offer a promising but largely untested approach to assessing relative risks among these patients. The study aims to establish whether the provision of ML-based risk scores predicting patient outcomes improves the ability of dispatchers to identify patients at high risk for deterioration in RCS and dispatch the first available ambulance to them. METHODS AND FINDINGS: We performed a parallel-group, randomized trial of adult patients assessed by a dispatch nurse at two study sites in Sweden as requiring a low-priority ambulance response in RCS. Patients were randomized 1:1 to be prioritized with the aid of an ML-based risk assessment tool, or per current clinical practice. The primary outcome was defined in terms of whether the first available ambulance was sent to the patient with the highest National Early Warning Score (NEWS 2) based on subsequently collected vital signs. A total of 1,245 RCS were included in the study. In the intervention arm, 68.3% of RCS were assessed correctly per the primary outcome versus 62.5% in the control group, corresponding to an odds ratio of 1.28 (95% CI [1.00, 1.63], p = 0.047). This study was limited to only patients determined to require a low-priority ambulance response in two Swedish regions, and was underpowered for the primary outcome due to a smaller than expected sample size. CONCLUSION: This study suggests that clinical ML-based decision support tools may have the ability to influence care provider decisions and improve their capacity to rapidly differentiate between high- and low-risk patients at dispatch. Further research should establish the suitability of these tools in larger cohorts, for patients with both higher- and lower-levels of priority, and in other settings. The trial was registered at ClinicalTrials.gov (NCT04757194).
4 Apr 2026
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