PLOS digital health最新文献

Artificial intelligence (AI) holds promise for enhancing glaucoma screening and management, yet its adoption depends on clinician perceptions, particularly in resource-limited regions like Eastern Europe. This study explores awareness, trust, and expectations of AI in glaucoma care among Bulgarian ophthalmologists, examining the influence of demographic factors such as age, gender, and professional experience. A cross-sectional survey was conducted from March to May 2024 among 156 ophthalmologists and residents recruited via Bulgarian professional societies. The 25-question survey, informed by the Technology Acceptance Model and validated (content validity index = 0.85; Cronbach's α = 0.78), assessed awareness, trust (5- point Likert scale), and expectations. Data were analyzed using non-parametric tests (chi-square, Spearman correlation) and thematic analysis for qualitative responses. The study was approved by the Ethics Committee of Medical University of Varna (No141/14.03.2024), with informed consent obtained and adherence to the Declaration of Helsinki. Participants (73.1% female; median age 35 years, IQR 10) showed varying awareness, with less experienced clinicians (<5 years) more informed (χ2 = 17.89, p < 0.001). Trust was low (7.5% fully trusted AI diagnosis; 5.7% for treatment), with gender differences (males more distrustful in diagnosis, p = 0.009). Younger respondents were more optimistic about AI's impact (ρ = 0.268, p < 0.001). Qualitative themes highlighted diagnostic utility (95% mentions) and concerns like training deficiencies (45%). Bulgarian ophthalmologists exhibit cautious optimism toward AI in glaucoma care, shaped by demographics, underscoring the need for targeted training to build trust. These findings inform regional AI implementation strategies, aligning with ethical priorities for equitable digital health adoption.

Acute appendicitis is a common but diagnostically challenging surgical emergency in children. Existing linear scoring systems lack sufficient accuracy for standalone use, while advanced imaging is constrained by risks of sedation, contrast, and radiation. Furthermore, no available tools provide prognostic guidance. We introduce Dharma, a machine learning framework consisting of a clinically grounded imputer and two random forest classifiers for diagnosis and severity assessment. Designed for real-world bedside use, Dharma is open-sourced and accessible through a web application. Dharma achieved excellent diagnostic performance, with an AUC-ROC of 0.98 [0.97-0.99] and accuracy of 93% [91-95]. For prognostic classification, it identified complicated appendicitis with high sensitivity (96% [93-99]) and negative predictive value (97% [94-99]). Even in cases without appendix visualization-a frequent limitation in resource-constrained settings-Dharma maintained strong performance (AUC-ROC 0.96 [0.93-0.99]), with specificity of 97% [93-100] and PPV of 93% [84-100] at a 44% threshold, and sensitivity of 92% [84-98] with NPV of 95% [91-99] at a 25% threshold. These threshold-dependent trade-offs enable Dharma to support both ruling in and ruling out appendicitis within diverse clinical workflows. Beyond pediatric appendicitis, Dharma's open-source framework and clinically grounded design also provide a generalizable foundation for developing equitable and practical decision-support systems in healthcare.

Malawi has 991,600 people living with HIV and has expanded access to annual HIV viral load testing to enhance care quality for clients. However, significant delays persist in returning viral load (VL) results back to facilities and to clients. To address this, we implemented a digital VL results return (VLRR) application, using existing mobile phone platforms to expedite results return to clients and healthcare providers (HCPs).VLRR is a digital SMS/USSD platform leveraging mobile phones to reduce turnaround time (TAT) and improve access to VL results. To evaluate the VLRR intervention, we: (1) estimated the TAT for digital results return, (2) calculated open rates of digital results, (3) conducted a mixed methods evaluation with VLRR users, and (4) estimated the potential cost savings from avoiding unnecessary sample redraws. From April 2022 to June 2024, HCPs registered 4,067 clients. For each client, TAT was calculated separately for the periods before and after enrollment in the VLRR system. On average during this period, clients received results in 128 days before VLRR enrollment and 48.5 days after enrollment, reflecting a 62.4% improvement. By July 2023, VLRR clients and HCPs received results in an average of 30 and 38 days. The overall open rate for digital results (opened by either a client or HCP) was 60% and nearly 100% of clients and HCPs indicated they wanted to the application to continue. Lastly, if VLRR were scaled nationally, it has the potential cost savings of $1.8-6.7 million USD.VLRR is effective in reducing TAT and improving access to VL results. To enhance uptake and achieve national scale, VLRR can be integrated into Malawi's existing EMR systems, further reducing TAT and enabling HCPs to deliver higher quality care and improve clinical outcomes.

Management of cirrhosis suffers from poor guideline adherence due to fragmented electronic health record (EHR) systems that scatter critical patient data across multiple modules, creating cognitive burden for clinicians and impeding evidence-based care delivery. We developed SMARTLiver, a Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART-on-FHIR) clinical decision support application employing human-centered design principles to consolidate patient data, incorporate evidence-based guidelines, and enhance cirrhosis care workflows. Following literature reviews of cirrhosis management guidelines and clinical workflow analysis within our health system, we created a FHIR-based application integrating automated task management, prognostic scoring, patient-reported outcomes, and real-time clinical decision support features. Usability evaluation with five clinical staff members using Think-Aloud protocols and the validated Health-ITUES survey revealed high satisfaction scores for Clinical Utility (4.4-4.6/5.0) and User Interface design (4.2/5.0), with moderate scores for workflow integration (4.0/5.0) and decision support (3.8-4.0/5.0). Qualitative feedback aligned with quantitative results, identifying enhancement opportunities in customization controls and notification management. The SMARTLiver prototype demonstrated technical feasibility in aggregating fragmented clinical data into a unified interface, automating evidence-based task generation, and maintaining interoperability across healthcare systems. This pilot study provides initial evidence for the potential of SMART-on-FHIR technology to address EHR fragmentation in cirrhosis care, though clinical effectiveness remains to be demonstrated.

Decentralized clinical trials (DCTs), particularly in the U.S., gained substantial attention during the COVID-19 pandemic, enabling trial activities to be conducted from participants' homes or local healthcare facilities despite restrictions and lockdowns. Regardless of the growth in interest, many facets of the DCT landscape remain unexplored or nascent in their development. This study aims to explore the key characteristics and development of the U.S.-registered DCT landscape, adoption patterns across various clinical contexts, and the role of digital technologies. We analyzed 1370 decentralized trials from ClinicalTrials.gov, collected using a broad DCT-keyword search. The data were screened and coded manually, and analyzed descriptively for temporal trends, purpose of decentralization, intervention type, geographic representation, and digitalization. Our findings align with previous reports of a growing, heterogeneous landscape of DCTs, with behavioral interventions appearing more suitable for decentralization than other types of interventions. Notably, most DCTs still focus on evaluating decentralized methods rather than merely implementing them in their investigations. Often, studies integrate digital tools either as the interventions themselves or to enable the digital delivery of study activities. Although the trial registry used is U.S.-based, and a U.S. partner is part of more than 50% of the studies identified, many trials are done in multiple countries or countries outside of the U.S. (42%). Among these trials, the data revealed considerable differences, with digitalized DCTs in this sample concentrated in high-income countries. Despite rapid growth in DCTs, our findings suggest the presence of a field in development, very much focused on establishing a methodological foundation. To unlock the potential of DCTs locally and globally, four critical areas demand further attention: digital equity, regulatory frameworks for diverse technologies, establishment of methodological validation processes, and further research on barriers to implementation.

Virtual intensive care units (vICUs) provide continuous remote monitoring and support for critically ill patients. Increasing patient complexity and staffing shortages have driven interest in vICUs, but evidence of their impact on clinical outcomes is limited. This study evaluated the effect of vICU implementation across critical care units in a large academic medical center. We conducted a before-and-after study comparing outcomes during the initial vICU implementation period (October 2022-April 2023) and the established program period (October 2023-April 2024), with a 6-month washout interval. Adult patients from a multispecialty surgical intensive care unit (ICU), neurocritical care unit, and ICU step-down unit were included if they had ICU stays longer than 6 h, hospital stays under 30 days, and mechanical ventilation for at least 12 h. The primary outcome was ICU length of stay, with secondary outcomes including hospital stay, ventilation time, vasopressor use, readmissions, and mortality. Among 530 patients (266 implementation, 264 established), ICU length of stay decreased from 232 to 198 h (p=0.011), ventilation time from 110 to 90 h (p=0.044), and vasopressor use for more than 12 h from 55% to 43% (p=0.007). Hospital stay, mortality, and readmission rates were unchanged. Subgroup analysis showed the greatest improvements in the surgical ICU, with reductions in ICU stay, ventilation time, and vasopressor use. These improvements may reflect earlier recognition of deterioration, better care coordination, and timely withdrawal of intensive therapies. Variation across units highlights the need to tailor vICU integration strategies to specific clinical workflows. These findings suggest that vICU implementation is feasible and may enhance critical care efficiency, though further multi-center studies are needed to determine generalizability and to assess patient-centered and economic outcomes.

Immersive virtual reality (IVR) is an emerging therapeutic modality that engages older adults in psychological therapeutically oriented activities developed to improve their psychological well-being. This systematic review aims to investigate the effects of IVR psychological intervention on psychological symptoms and well-being. A systematic review and meta-analysis was conducted following the Cochrane Handbook for Systematic Reviews of Interventions. Six databases were searched, including Embase, PubMed, Web of Science, Scopus, CINAHL, and PsycINFO, covering the period from 2010 to December 2024. RevMan 5.3 was utilized for meta-analysis, and the Cochrane Risk of Bias tool was employed for quality assessment. Ten randomized controlled trials of 746 older adults were included. The IVR interventions employed reminiscence (40%), garden/forest therapy (40%), cognitive stimulation (10%), and multi-sensory stimulation to reduce psychological symptoms and improve self-perception (10%). Data pooling suggested that IVR interventions have significantly reduced depressive symptoms [n = 5; SMD = -0.83, 95%CI (-1.05, -0.60), I2 = 21%, p < .001]; anxiety [n = 5, SMD = -0.77, 95% CI (-1.32, -0.22), I2 = 70%, p = .006]. Synthesis without meta-analysis (SWiM) was conducted for stress and affect outcomes following SWiM guidance. In all three studies (100%), IVR produced statistically significant reductions in stress versus usual/standard care, and in both studies (100%), it yielded statistically significant improvements in affect-higher positive and lower negative affect-compared with the respective control conditions. IVR-based interventions could be an alternative method for alleviating the psychological symptoms of older adults. Registration: PROSPERO CRD42024575387.

The FAIR principles guide data stewardship towards maximizing the value of scientific data while offering a high level of flexibility to accommodate differences in standards and scientific practices. Research communities have developed and implemented domain-specific workflows to make their data FAIR. This work compares the implementation of two externally developed structured FAIRification workflows-a generic workflow and a domain-specific workflow- using the example of metadata captured in diabetes research in Germany and applying the FAIR data maturity model developed by the Research Data Alliance. Interestingly, the implementation of both workflows required similar resources and led us to achieve the same FAIRness rating. We therefore conclude that the adaptations made in the FAIRification workflow for health research data improve efficiency but do not necessarily lead to higher FAIRness scores when applied to core data sets. Based on the results of our workflow comparison, we identified a list of requirements that should be met for the FAIRification of a core data set regardless of the workflow employed. In the future, FAIR data strategies and infrastructure should be planned and implemented as early as possible in the FAIRification journey. It is anticipated that this comparative analysis will help establish standard operating procedures for the FAIRification of core data sets for health studies.

Missing, inaccurate, or poorly documented data in healthcare is often treated as a technical problem to be statistically resolved via imputation, deletion, or modeling assumptions about randomness. However, such inaccuracies relate to far more complex socioeconomic and geopolitical issues, rather than "errors of data entry" to be ameliorated with statistical modeling techniques. We outline that what is really missing or inaccurate is the context in which the data is collected-and that only by understanding this context can we begin to prevent artificial intelligence's (AIs) amplification of misleading, decontextualized data. We critically examine how traditional modeling methods fail to account for the factors that influence what data gets recorded, and for whom. We show how AI systems trained on decontextualized data reinforce health inequities at scale. And, we review recent literature on context-aware approaches to understanding data, that incorporate metadata, social determinants of health, fairness constraints, and participatory governance to build more ethical and representative systems. Our analysis urges the AI and healthcare communities to move beyond the traditional emphasis on statistical convenience, toward socially grounded and interdisciplinary strategies for handling decontextualized data.

Cardiotocography (CTG) is a common investigative modality in obstetrics to evaluate the fetal condition. Advancements in digital technology has enabled the innovation of CTG monitoring for usage in the home setting. This review aims to comprehensively examine the current evidence on the effectiveness and applicability of home antenatal CTG monitoring. MEDLINE, EMBASE, Cochrane, Web of Science, and PubMed databases were searched from inception to June 2025. Primary studies examining home antenatal CTG were included. For randomised controlled trials (RCTs), the joint primary outcomes were perinatal mortality and emergency caesarean section. For observational studies, the feasibility, diagnostic accuracy, qualitative and economic burden of home CTG were evaluated. RCTs were eligible for meta-analysis using risk ratio or mean difference, with 95% confidence intervals. Included observational studies were narratively described due to significant methodological heterogeneity. 39 studies (28 observational, seven RCTs and four qualitative studies), comprising of 7240 participants were included. Home antenatal CTG monitoring was non-inferior to conventional care across all meta-analysed maternal, perinatal and healthcare usage outcomes. GRADE assessments were low/very low quality of evidence. Home CTG monitoring was feasible in several settings and remote interpretation was graded as moderate to excellent. Transmission failures were frequently low but commonly occurred due to infrastructure and/or equipment errors. Remote CTG monitoring demonstrated comparative capabilities to conventional CTG with respect to coincidence and beat-to-beat variability. Overall acceptability ratings were high for patient and providers. Often implementation costs were high but accrued back by non-fixed savings when compared against routine care. High-quality studies were underrepresented, particularly when assessing service-led and safety outcomes. Home antenatal CTG monitoring demonstrates noninferiority to conventional care across several outcomes, representing a promising avenue for antenatal management However, current evidence is of low quality and additional high-quality evidence with sufficient methodological detail and standardised outcome assessment is required prior to making definitive recommendations.