PLOS digital health最新文献

{"title":"Accuracy of preferred language data in a multi-hospital electronic health record in Toronto, Canada.","authors":"Camron D Ford, Thomas Bodley, Martin Betts, Rob A Fowler, Alexis Gordon, Michele James, Shail Rawal, Christina Reppas-Rindlisbacher, Paul Tam, George Tomlinson, Christopher J Yarnell","doi":"10.1371/journal.pdig.0000999","DOIUrl":"10.1371/journal.pdig.0000999","url":null,"abstract":"<p><p>Accurate preferred language data is a prerequisite for providing high-quality care. We investigated the accuracy of preferred language data in the electronic health record (EHR) of a large community hospital network in Toronto, Canada. We conducted a point-prevalence audit of patients admitted to intensive care, internal medicine, and nephrology services at three hospitals. We asked each patient \"What is your preferred language for health care communication?\" and reported on agreement (with 95% confidence intervals [CI]) between interview-based and EHR-based preferred language. We used Bayesian multilevel logistic regression to analyze the association between patient factors and the accuracy of the EHR for patients who preferred a non-English language. Between June 17, 2024, and July 19, 2024, we interviewed 323 patients, of whom 124 (38%) preferred a non-English language. Median age was 77 years and 46% were female. EHR accuracy was 86% for all patients. The probability of the EHR correctly identifying a patient with non-English preferred language (sensitivity) was 69% (CI 60-77), specificity was 97% (CI 94-99), positive predictive value was 95% (CI 88-98), and negative predictive value was 83% (CI 79-87). There were 26 different non-English preferred languages, most commonly Cantonese (27%) and Tamil (14%). Accuracy was better for patients who were female or older, and varied by hospital and medical service. Mechanisms to improve accuracy for language preference data are needed to improve the validity of research studying preferred language, mitigate algorithmic bias, and overcome language-based inequities.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0000999"},"PeriodicalIF":7.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: GPT-4 can pass the Korean National Licensing Examination for Korean Medicine Doctors.","authors":"Dongyeop Jang, Taerim Yun, Choong-Yeol Lee, Young-Kyu Kwon, Chang-Eop Kim","doi":"10.1371/journal.pdig.0001000","DOIUrl":"10.1371/journal.pdig.0001000","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1371/journal.pdig.0000416.].</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0001000"},"PeriodicalIF":7.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12407400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: A systematic review.","authors":"Lekaashree Rambabu, Thomas Edmiston, Brandon G Smith, Katharina Kohler, Angelos G Kolias, Richard A I Bethlehem, Pearse A Keane, Hani J Marcus, EyeVu Consortium, Peter J Hutchinson, Tom Bashford","doi":"10.1371/journal.pdig.0000783","DOIUrl":"10.1371/journal.pdig.0000783","url":null,"abstract":"<p><p>Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients with potential intracranial pathology could prove to be beneficial, particularly in resource-limited settings where access to neuroimaging may be limited. However, a comprehensive overview of the current literature on this field is lacking. We conducted a systematic review on the use of AI for papilloedema detection by searching four databases: Ovid MEDLINE, Embase, Web of Science, and IEEE Xplore. Included studies were assessed for quality of reporting using the Checklist for AI in Medical Imaging and appraised using a novel 5-domain rubric, 'SMART', for the presence of bias. For a subset of studies, we also assessed the diagnostic test accuracy using the 'Metadta' command on Stata. Nineteen deep learning systems and eight non-deep learning systems were included. The median number of images of normal optic discs used in the training set was 2509 (IQR 580-9156) and in the testing set was 569 (IQR 119-1378). The number of papilloedema images in the training and testing sets was lower with a median of 1292 (IQR 201-2882) in training set and 201 (IQR 57-388) in the testing set. Age and gender were the two most frequently reported demographic data, included by one-third of the studies. Only ten studies performed external validation. The pooled sensitivity and specificity were calculated to be 0.87 [95% CI 0.76-0.93] and 0.90 [95% CI 0.74-0.97], respectively. Though AI model performance values are reported to be high, these results need to be interpreted with caution due highly biased data selection, poor quality of reporting, and limited evidence of reproducibility. Deep learning models show promise in retinal image analysis of papilloedema, however, external validation using large, diverse datasets in a variety of clinical settings is required before it can be considered a tool for triage of intracranial pathologies in resource-limited areas.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0000783"},"PeriodicalIF":7.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunistic use of artificial intelligence with X-ray imaging for diagnosis of HIV status in tuberculosis patients in Uganda and Tanzania.","authors":"Dmitrii Cherezov, Tanmoy Dam, Irene Najjingo, Margaret Mbabazi, Harriet Kisembo, Bruce Kirenga, Grace Soka, Esther Ngadaya, Sayoki Mfinanga, Anant Madabhushi","doi":"10.1371/journal.pdig.0000988","DOIUrl":"10.1371/journal.pdig.0000988","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 9","pages":"e0000988"},"PeriodicalIF":7.7,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nurses' experiences with inhospital continuous monitoring of vital signs in general wards: A systematic review.","authors":"Berte van Zeist-de Jonge, Janneke de Man-van Ginkel, Mick Olvers, Kees van den Berge, Laura Kooij, Paul J T Rood","doi":"10.1371/journal.pdig.0000949","DOIUrl":"10.1371/journal.pdig.0000949","url":null,"abstract":"<p><p>Recent developments make the continuous monitoring of vital signs outside the critical care setting feasible, and may provide a benefit in terms of improved patient outcomes and cost efficiency. A meta-aggregative systematic review was conducted to provide an overview of the experiences of nurses working with continuous monitoring of vital signs in patients admitted to a hospital general ward. All study designs describing nurses' experiences in a qualitative manner were included. Relevant studies were identified by searching the electronic databases Pubmed, Cinahl and Embase from 2017 up to September 2024. The search strategy combined 'nurses', 'continuous monitoring/measuring', 'vital signs', and 'hospital/(general) wards' as well as synonyms. Of 3066 articles found, nine were included. Four themes were synthesized: 1) Emotional and practical advantages, e.g., patients feel safer and nurses feel more secure, as continuous monitoring detects patients deterioration earlier; 2) practical disadvantages, e.g., reduced nurse-patient interaction and stress related to changing vital signs, potential over-monitoring and data overloading; 3) important aspects related to the implementation process, e.g., training and coaching, properly working technical infrastructure; and 4) alarm strategies and clinical assessment, e.g., reducing unnecessary alarms. We conclude that nurses report varying experiences in working with continuous monitoring of vital signs on the general ward. The advantages of continuous monitoring seem to justify investing in further development of the systems and sensors in order to reduce the practical disadvantages. The findings may facilitate optimal implementation of continuous monitoring of vital signs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000949"},"PeriodicalIF":7.7,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: AI-driven healthcare: A review on ensuring fairness and mitigating bias.","authors":"","doi":"10.1371/journal.pdig.0000994","DOIUrl":"10.1371/journal.pdig.0000994","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1371/journal.pdig.0000864.].</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000994"},"PeriodicalIF":7.7,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of integrating human and animal disease surveillance and reporting in Rwanda: Insights from a mobile reporting pilot and veterinarians' perspectives - a multi-method study.","authors":"Dieudonne Hakizimana, Janna M Schurer, Emmanuel Irimaso, Peter Rabinowitz, Joseph Ndagijimana, Janetrix Hellen Amuguni","doi":"10.1371/journal.pdig.0000990","DOIUrl":"10.1371/journal.pdig.0000990","url":null,"abstract":"<p><p>The Rwandan veterinary health system lacks reliable animal disease surveillance data, hindering effective response to zoonotic diseases and other animal health events, including pathogen spillovers with pandemic potential. To address this gap, we piloted a mobile phone reporting system among veterinarians to (1) collect data on animal and human health events and (2) gather insights for future implementations, strengthening the reporting system's operationalization. A multi-method approach was employed with 14 veterinarians equipped with smartphones. We developed a real-time reporting questionnaire synchronized with a central server and trained the veterinarians to use it during regular field visits. To evaluate the pilot, 11 in-depth interviews were conducted. Quantitative data were analyzed using descriptive statistics, while thematic analysis identified key qualitative themes. Over the study period, veterinarians submitted 1,181 reports through the mobile system, documenting 1,232 cattle disease cases. Common symptoms included inappetence (56.4%) and fever (53.3%). Suspected diseases were primarily East Coast Fever (36.8%) and anaplasmosis (17.4%), with diagnostic tests performed in only 3.6% of cases. Among 3,337 cattle owners, 354 self-reported illness, with 72.6% seeking medical attention. Mobile reporting proved feasible, improving veterinarians' record-keeping, communication, and collaboration. Key implementation facilitators included training, financial allowances, and technical support, while challenges involved phone capacity and network coverage. Veterinarians leveraged community trust to gather human health data, describing the process as both educational and empowering, and strongly supported the system's continued use and enhancements. This pilot highlighted the potential of mobile reporting systems to enhance veterinary practice and zoonotic disease surveillance in remote areas. Positive experiences from veterinarians underscore its feasibility, though scaling up requires investments in training, support, incentives, and addressing technological barriers. Future research should evaluate cost-effectiveness and stakeholder readiness to optimize adoption.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000990"},"PeriodicalIF":7.7,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalizing mobile applications for health based on user profiles: A preference matrix from a scoping review.","authors":"Laëtitia Gosetto, Gilles Falquet, Fréderic Ehrler","doi":"10.1371/journal.pdig.0000978","DOIUrl":"10.1371/journal.pdig.0000978","url":null,"abstract":"<p><p>The World Health Organization identifies unhealthy behaviors, such as smoking, as significant risk factors contributing to mortality and morbidity, underscoring the necessity to adopt healthier habits. The increasing prevalence of health applications (apps) presents opportunities for promoting healthier lifestyles. Notably, personalized mobile health (mHealth) interventions can enhance user engagement and their effectiveness. Our scoping review aims to contribute to guide the personalization of mHealth interventions for health behavior change by defining which mechanisms should be favored for a given user profile. Online databases were searched to identify articles published between 2008 and 2024 describing the topic of personalization, behavior change apps, and mobile app mechanisms. Of 1806 articles identified, 18 articles were retained. We then categorized the mechanisms and user profiles described in the selected articles into existing taxonomies. Finally, the relationship between the user profiles and mechanisms were reported. The four user profiles identified included personality and gamer profiles. Twenty-one mechanisms extracted from the articles were categorized as behavioral change techniques, gamification, or mobile app mechanisms, with limited numbers of preference relations between mechanisms and user profiles. The relation matrix was not complete and covered only 51% of possible relations: game mechanisms, 30%; behavioral change techniques, 16%; and app mechanisms, 5%. Two user profiles, the Big Five (18%) and Hexad scale (20%), covered 38% of relations, whereas the two remaining user profiles contributed to the remaining 13%. Social mechanisms, including competition, cooperation, and social comparison, exhibit strong connections to user profiles and are pivotal in persuasive system design. Self-efficacy theory links mechanisms such as self-monitoring, social persuasion, and rewards to behavior change. However, only 51% of potential relationships between profiles and mechanisms were identified. Adapting mHealth content based on user profiles requires reliable personality assessments and privacy-conscious data collection to enable personalized, profile-specific interventions for improved outcomes.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000978"},"PeriodicalIF":7.7,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12364320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144884417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction.","authors":"Vien Ngoc Dang, Charlotte Cecil, Carmine M Pariante, Jerónimo Hernández-González, Karim Lekadir","doi":"10.1371/journal.pdig.0000982","DOIUrl":"10.1371/journal.pdig.0000982","url":null,"abstract":"<p><p>Recent evidence suggests that psycho-cardio-metabolic (PCM) multimorbidity finds its origins in exposure to early-life factors (ELFs), making the exploration of this association crucial for understanding and effective management of these complex health issues. Moreover, risk prediction models for cardiovascular diseases (CVD) and diabetes, as recommended by current clinical guidelines, typically demonstrate sub-optimal performance in clinically relevant sub-populations where these ELFs may play a substantial role. Our methodological approach investigates the contribution of ELFs to machine-learning-based risk prediction models for comorbid populations, incorporating a wide set of early-life and proximal variables, with a special focus on prenatal and postnatal ELFs. To address the complexity of integrating diverse early-life and proximal factors, we leverage models capable of handling high-dimensional, heterogeneous data sources to enhance prediction accuracy in complex clinical populations. The long-term predictive ability of ELFs, along with their influence on model decisions, is assessed with the learned models, and global and local model-agnostic interpretative techniques allow us to elucidate some interactions leading to multimorbidity. The data for this study is derived from the UK Biobank, showcasing both the strengths and limitations inherent in utilizing a single, large-scale database for such research. Our results show enhanced predictive performance for CVD (AUC-ROC: +7.9%, Acc: +14.7%, Cohen's d: 1.5) among individuals with concurrent mental health issues (depression or anxiety) and diabetes. Similarly, we demonstrate improved diabetes risk prediction (AUC-ROC: +12.3%, Acc: +13.5%, Cohen's d: 2.5) in those with concurrent mental health conditions and CVD. The inspection of these models, which integrate a large set of ELFs and other predictors (including the 7-core Framingham and UKDiabetes variables), provides key information that could lead to a more profound understanding of psycho-cardio-metabolic multimorbidity. Our findings highlight the utility of incorporating life-course factors into risk models. Integrating a diverse range of physiological, psychological, and ELFs becomes particularly pertinent in the context of multimorbidity.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000982"},"PeriodicalIF":7.7,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360575/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating medical AI before midnight strikes: Addressing bias, data fidelity, and implementation challenges.","authors":"Cameron J Sabet, Ketan Tamirisa, Danielle Sara Bitterman, Leo Anthony Celi","doi":"10.1371/journal.pdig.0000986","DOIUrl":"10.1371/journal.pdig.0000986","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 8","pages":"e0000986"},"PeriodicalIF":7.7,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12360511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144877186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}