PLOS digital health最新文献

Patient satisfaction is a central measure of high-performing healthcare systems, yet real-world evaluations at scale remain challenging. In this study, we analyzed 122,194 de-identified patient reviews from 45 Ontario general hospitals between January 2015 and July 2022. We applied a natural language processing (NLP) pipeline using a clinical named entity recognition (NER) model fine-tuned on biomedical literature to extract references to diseases, symptoms, and medical procedures from patient reviews. Geospatial analysis was conducted to examine sentiment patterns based on regional census data related to low-income status and visible-minority composition. Our primary objective was to investigate how the COVID-19 pandemic influenced patient satisfaction trends, with a specific focus on clinical units and hospitals serving marginalized populations. We assessed changes in the proportion of positive comments across time periods and socioeconomic groups using multivariate logistic regression. Our findings show that over 80% of the hospitals studied had fewer than 50% positive reviews, highlighting possible systemic gaps in patient needs. Interestingly, the proportion of negative reviews decreased during the COVID-19 pandemic, suggesting possible changes in patient expectations or increased appreciation for healthcare workers. However, certain units, such as dentistry and radiology, experienced more negative ratings as a proportion of their total reviews. 'Anxiety' emerged as a recurrent concern in negative reviews, especially during the start of the pandemic, pointing to the growing awareness of mental health needs. Based on our geospatial analysis, hospitals located in regions with higher percentages of visible minority and low-income populations initially saw higher positive review proportions before COVID-19, but this trend reversed after 2020. Our statistical models confirmed that these shifts were significant, particularly for low-income-serving hospitals. Collectively, these results demonstrate how large-scale unstructured data can identify fundamental drivers of patient satisfaction, while underscoring the urgent need for adaptive strategies to address anxiety and combat systemic inequities.

Background: Vedolizumab, a monoclonal antibody targeting the α4β7 integrin, offers gut-selective immunosuppression and represents a cornerstone biologic therapy for moderate-to-severe ulcerative colitis (UC). While pivotal randomized controlled trials (RCTs) have established its efficacy, a substantial subset of patients experience primary non-response. This variability presents significant clinical challenges, including patient morbidity and healthcare costs associated with cycling through ineffective therapies, underscoring an urgent need for personalized treatment strategies.

Objectives: This review aims to critically reappraise the foundational RCT evidence supporting vedolizumab use in UC, examining both strengths and limitations, and providing a comprehensive analysis of how artificial intelligence (AI), particularly machine learning (ML), can be leveraged to optimize vedolizumab treatment selection, predict outcomes, and personalize management.

Methods: A systematic literature search was performed across PubMed, Scopus, and Web of Science databases. The review synthesized data from key Phase III trials (GEMINI 1, VARSITY), long-term extension safety studies, relevant meta-analyses summarizing efficacy and safety, and pertinent studies investigating the application of AI and ML techniques within inflammatory bowel disease management. The search included terms such as vedolizumab, UC, AI, and predictive modeling.

Findings: Landmark trials confirmed vedolizumab's superiority over placebo for inducing and maintaining remission, with week 52 clinical remission rates reaching 41.8% in the GEMINI 1 trial. Concurrently, emerging AI/ML models, integrating complex patient data, show considerable promise in predicting biologic response with high accuracy, with some models achieving an area under the curve (AUC) of 0.82 (95% CI 0.78-0.86). Neural networks have demonstrated an accuracy of approximately 79% in specific predictive contexts.

Conclusions: The strategic integration of AI-driven predictive analytics with vedolizumab's clinical and pharmacodynamic data represents a pivotal next step towards achieving true precision medicine in UC.

The healthcare community remains divided on the benefits of artificial intelligence (AI) in medicine. In this qualitative study, we sought to better understand the perceived opportunities and threats of AI among premedical students, medical students, and physicians. We conducted a thematic analysis on Reddit, a social platform where candid opinions are often shared. Posts from the r/premed, r/medicalschool, and r/medicine subreddits were searched using the terms "AI", "chatGPT", "openAI", and "artificial intelligence". We analyzed 2403 comments across 47 threads from December 2022 to August 2023. A coding scheme was developed manually following Braun and Clarke's (2006) framework, and common themes were extracted. The main themes identified centered on AI enhancement versus replacement. Careers perceived to be lower in the medical social hierarchy were considered most at risk of replacement. AI was thought to first replace non-medical jobs, followed by mid-levels, and then primary care and diagnostic specialties, with specialists and surgeons affected last. Some contributors emphasized that AI could never replace a physician's compassion and nuanced clinical judgment. Others viewed AI as a tool to enhance efficiency, particularly in tasks such as studying, note writing, screening, and triage. Although verifying the credentials of commenters on online forums poses a challenge, platforms like Reddit offer a valuable opportunity to understand nuanced attitudes and perceptions regarding AI in medicine. Online forums allow for a unique understanding of the impressions of AI in medicine. While AI was generally well-received, we identified a key finding: a socially hierarchical, biased form of thinking among healthcare professionals. The perpetuation of this biased mindset may contribute to role devaluation, mistrust, and collaboration challenges within healthcare teams-ultimately impacting patient care. To fully leverage AI's potential in medicine, it is critical to acknowledge and address potentially biased perceptions within the healthcare community.

The global rise in prescription opioid use has contributed to an opioid epidemic, associated harms, and unintentional deaths in several western countries. Opioids however continue to be regularly prescribed for acute pain and in the chronic pain context due to limited treatment options. Currently there are no accurate tools that help predict which patients prescribed opioids may be at risk of death, which depends on the cultural context and varies across countries. Existing models do not account for statistical considerations such as censoring and competing risks. Using nationally representative data from the United Kingdom from 1,026,139 patients newly prescribed an opioid, we developed three competing risk time-to-event models: a regression model, a random forest, and a deep neural network to predict opioid-related deaths using UK primary care records. The models were externally validated in an external cohort of 337,015 patients. The models exhibited good discrimination and positive predictive value during internal validation (C-statistic for the regression model, random forest, and neural network: 84.3%, 84.4% and 82.1% respectively), and external validation (C-statistic for the regression model, random forest, and neural network: 81.8%, 81.5% and 81.5% respectively). Prior substance abuse, lung and liver comorbidities, morphine, fentanyl, or oxycodone at initiation and co-prescription of gabapentinoids were some of candidate predictors associated with a higher risk of opioid-related mortality within the models. These results demonstrate how routinely collected data from a nationally representative dataset may be used to develop and validate opioids risk algorithms to better help clinicians and patients predict risk to this serious adverse outcome.

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.