PLOS digital health最新文献

{"title":"Children's digital privacy on fast-food and dine-in restaurant mobile applications.","authors":"Christine Mulligan, Grace Gillis, Lauren Remedios, Christopher Parsons, Laura Vergeer, Monique Potvin Kent","doi":"10.1371/journal.pdig.0000723","DOIUrl":"10.1371/journal.pdig.0000723","url":null,"abstract":"<p><p>Children are targeted by unhealthy food marketing on digital media, influencing their food preferences, intakes and non-communicable disease risk. Restaurant mobile applications are powerful platforms for collecting users' data and are popular among children. This study aimed to provide insight into the privacy policies of top dine-in and fast-food mobile apps in Canada and data collected on child users. Privacy policies of the top 30 fast-food and dine-in restaurants in Canada were reviewed. A convenience sample of 11 English-speaking Canadian residents aged 9-12 years with fast-food apps on their mobile phones were recruited to use ≥1 fast-food restaurant mobile app(s). Children used the app(s) for 5-10 minutes and placed food orders. Parents submitted a Data Access Request (DAR) on their child's behalf to the food company. Descriptive analysis and a flexible deductive approach to content analysis evaluated data collected through DARs. Overall, 26 privacy policies were analyzed. The intended age of app users was indicated by 12 (46%) food companies, 10 (39%) of which specified it as ≥13 years. No company had a compulsory age verification process. Twenty-four (92%) companies disclosed the data collected on app users: 23 (89%) did not distinguish between information pertaining to children or adults, and 21 (81%) described a protocol for action if they inadvertently collected data on children. Twenty-four DARs were sent to companies; 11 (45.8%) of which were fulfilled by companies, and 4 (16.7%) resulted in the receipt of children's data. All responding food companies were found to collect sociodemographic information on child participants (e.g., name, email). Some collected other information, such as order details and available promotional offers. This study demonstrates current fast-food and dine-in restaurant privacy policies are insufficient and provides insight into data collected on children via fast-food apps. Policies must be strengthened to ensure children's privacy and protection online.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000723"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257281","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":"An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers.","authors":"Michael Owusu-Adjei, James Ben Hayfron-Acquah, Twum Frimpong, Abdul-Salaam Gaddafi","doi":"10.1371/journal.pdig.0000543","DOIUrl":"10.1371/journal.pdig.0000543","url":null,"abstract":"<p><p>The desire for safer delivery mode that preserves the lives of both mother and child with minimal or no complications before, during and after childbirth is the wish for every expectant mother and their families. However, the choice for any particular delivery mode is supposedly influenced by a number of factors that leads to the ultimate decision of choice. Some of the factors identified include maternal birth history, maternal and child health conditions prevailing before and during labor onset. Predictive modeling has been used extensively to determine important contributory factors or artifacts influencing delivery choice in related research studies. However, missing among a myriad of features used in various research studies for this determination is maternal history of spontaneous, threatened and inevitable abortion(s). How its inclusion impacts delivery outcome has not been covered in extensive research work. This research work therefore takes measurable maternal features that include real time information on administered partographs to predict delivery outcome. This is achieved by adopting effective feature selection technique to estimate variable relationships with the target variable. Three supervised learning techniques are used and evaluated for performance. Prediction accuracy score of area under the curve obtained show Gradient Boosting classifier achieved 91% accuracy, Logistic Regression 93% and Random Forest 91%. Balanced accuracy score obtained for these techniques were; Gradient Boosting 82.73%, Logistic Regression 84.62% and Random Forest 83.02%. Correlation statistic for variable independence among input variables showed that delivery outcome type as an output is associated with fetal gestational age and the progress of maternal cervix dilatation during labor onset.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000543"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798466/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257348","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":"Beyond the screen: Exploring the dynamics of social media influencers, digital food marketing, and gendered influences on adolescent diets.","authors":"Ashley Amson, Mariangela Bagnato, Lauren Remedios, Meghan Pritchard, Soulene Sabir, Grace Gillis, Elise Pauzé, Christine White, Lana Vanderlee, David Hammond, Monique Potvin Kent","doi":"10.1371/journal.pdig.0000729","DOIUrl":"10.1371/journal.pdig.0000729","url":null,"abstract":"<p><p>Adolescent obesity remains a public health concern, exacerbated by unhealthy food marketing, particularly on digital platforms. Social media influencers are increasingly utilized in digital marketing, yet their impact remains understudied. This research explores the frequency of posts containing food products/brands, the most promoted food categories, the healthfulness of featured products, and the types of marketing techniques used by social media influencers popular with male and female adolescents. By analyzing these factors, the study aims to provide a deeper understanding of how social media influencer marketing might contribute to dietary choices and health outcomes among adolescents, from a gender perspective, shedding light on an important yet underexplored aspect of food marketing. A content analysis was conducted on posts made between June 1, 2021, and May 31, 2022, that were posted by the top three social media influencers popular with males and female adolescents (13-17) on Instagram, TikTok, and YouTube (N = 1373). Descriptive statistics were used to calculate frequencies for posts containing food products/brands, promoted food categories, product healthfulness, and marketing techniques. Health Canada's Nutrient Profile Model was used to classify products as either healthy or less healthy based on their content in sugar, sodium, and saturated fats. Influencers popular with males featured 1 food product/brand for every 2.5 posts, compared to 1 for every 6.1 posts for influencers popular with females. Water (27% of posts) was the primary food category for influencers popular with females, while restaurants (24% of posts) dominated for males. Influencers popular with males more commonly posted less healthy food products (89% vs 54%). Marketing techniques varied: influencers popular with females used songs or music (53% vs 26%), other influencers (26% vs 11%), appeals to fun or coolness (26% vs 13%), viral marketing (29% vs 19%), and appeals to beauty (11% vs 0%) more commonly. Influencers popular with males more commonly used calls-to-action (27% vs 6%) and price promotions (8% vs 1%). Social media influencers play a role in shaping adolescents' dietary preferences and behaviors. Understanding gender-specific dynamics is essential for developing targeted interventions, policies, and educational initiatives aimed at promoting healthier food choices among adolescents. Policy efforts should focus on regulating unhealthy food marketing, addressing gender-specific targeting, and fostering a healthy social media environment for adolescents to support healthier dietary patterns.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000729"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11798478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143257276","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":"Addressing technology-mediated stigma in sexual health-related digital platforms: Insights from design team members.","authors":"Abdul-Fatawu Abdulai, Amanda Fuchsia Howard, Paul J Yong, Leanne M Currie","doi":"10.1371/journal.pdig.0000722","DOIUrl":"10.1371/journal.pdig.0000722","url":null,"abstract":"<p><p>Digital health technologies are increasingly used as complementary tools in accessing sexual health-related services. At the same time, there are concerns regarding how some interface features and content of these technologies could inadvertently foment stigma among end users. In this study, we explored how design teams (i.e., those involved in creating digital health technologies) might address stigmatizing components when designing sexual health-related digital technologies. We interviewed 14 design team members (i.e., software engineers, user interface and user experience (UI/UX) designers, content creators, and project managers) who were involved in digital health design projects across two universities in western Canada. The interviews sought to undersand their perspectives of how to create destigmatizing digital technologies and were centered on strategies that they might adopt or the kind of expertise or support they might need to be able to address stigmatizing features or content on sexual health-related digital technologies. The findings revealed two overarching approaches regarding how digital health technologies could be designed to prevent the unintended effects of stigma. These include functional design considerations (i.e., pop-up notifications, infographics, and video-based testimonials, and avoiding the use of cookies or other security-risk features) and non-functional design considerations (i.e., adopting an interprofessional and collaborative approach to design, educating software designers on domain knowledge about stigma, and ensuring consistent user testing of content). These findings reflected functional and non-functional design strategies as applied in software design. These findings are considered crucial in addressing stigma but are not often apparent to designers involved in digital health projects. This suggests the need for software engineers to understand and consider non-functional, emotional, and content-related design strategies that could address stigmatizing attributes via digital health platforms.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000722"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11793748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191603","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":"Digital health equity: Crafting sustainable pathways.","authors":"Robin Pierce","doi":"10.1371/journal.pdig.0000703","DOIUrl":"10.1371/journal.pdig.0000703","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000703"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11793777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190320","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":"Step-by-step causal analysis of EHRs to ground decision-making.","authors":"Matthieu Doutreligne, Tristan Struja, Judith Abecassis, Claire Morgand, Leo Anthony Celi, Gaël Varoquaux","doi":"10.1371/journal.pdig.0000721","DOIUrl":"10.1371/journal.pdig.0000721","url":null,"abstract":"<p><p>Causal inference enables machine learning methods to estimate treatment effects of medical interventions from electronic health records (EHRs). The prevalence of such observational data and the difficulty for randomized controlled trials (RCT) to cover all population/treatment relationships make these methods increasingly attractive for studying causal effects. However, researchers should be wary of many pitfalls. We propose and illustrate a framework for causal inference estimating the effect of albumin on mortality in sepsis using an Intensive Care database (MIMIC-IV) and comparing various sensitivity analyses to results from RCTs as gold-standard. The first step is study design, using the target trial concept and the PICOT framework: Population (patients with sepsis), Intervention (combination of crystalloids and albumin for fluid resuscitation), Control (crystalloids only), Outcome (28-day mortality), Time (intervention start within 24h of admission). We show that too large treatment-initiation times induce immortal time bias. The second step is selection of the confounding variables based on expert knowledge. Increasingly adding confounders enables to recover the RCT results from observational data. As the third step, we assess the influence of multiple models with varying assumptions, showing that a doubly robust estimator (AIPW) with random forests proved to be the most reliable estimator. Results show that these steps are all important for valid causal estimates. A valid causal model can then be used to individualize decision making: subgroup analyses showed that treatment efficacy of albumin was better for patients >60 years old, males, and patients with septic shock. Without causal thinking, machine learning is not enough for optimal clinical decision on an individual patient level. Our step-by-step analytic framework helps avoiding many pitfalls of applying machine learning to EHR data, building models that avoid shortcuts and extract the best decision-making evidence.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000721"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124029","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":"Comparison of anonymization techniques regarding statistical reproducibility.","authors":"David Pau, Camille Bachot, Charles Monteil, Laetitia Vinet, Mathieu Boucher, Nadir Sella, Romain Jegou","doi":"10.1371/journal.pdig.0000735","DOIUrl":"10.1371/journal.pdig.0000735","url":null,"abstract":"<p><strong>Background: </strong>Anonymization opens up innovative ways of using secondary data without the requirements of the GDPR, as anonymized data does not affect anymore the privacy of data subjects. Anonymization requires data alteration, and this project aims to compare the ability of such privacy protection methods to maintain reliability and utility of scientific data for secondary research purposes.</p><p><strong>Methods: </strong>The French data protection authority (CNIL) defines anonymization as a processing activity that consists of using methods to make impossible any identification of people by any means in an irreversible manner. To answer project's objective, a series of analyses were performed on a cohort, and reproduced on four sets of anonymized data for comparison. Four assessment levels were used to evaluate impact of anonymization: level 1 referred to the replication of statistical outputs, level 2 referred to accuracy of statistical results, level 3 assessed data alteration (using Hellinger distances) and level 4 assessed privacy risks (using WP29 criteria).</p><p><strong>Results: </strong>87 items were produced on the raw cohort data and then reproduced on each of the four anonymized data. The overall level 1 replication score ranged from 67% to 100% depending on the anonymization solution. The most difficult analyses to replicate were regression models (sub-score ranging from 78% to 100%) and survival analysis (sub-score ranging from 0% to 100. The overall level 2 accuracy score ranged from 22% to 79% depending on the anonymization solution. For level 3, three methods had some variables with different probability distributions (Hellinger distance = 1). For level 4, all methods had reduced the privacy risk of singling out, with relative risk reductions ranging from 41% to 65%.</p><p><strong>Conclusion: </strong>None of the anonymization methods reproduced all outputs and results. A trade-off has to be find between context risk and the usefulness of data to answer the research question.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 2","pages":"e0000735"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790161/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123568","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":"Developing a youth-friendly internet-enabled HIV risk calculator: A collaborative approach with young key populations, living in Soweto, South Africa.","authors":"Mamakiri Mulaudzi, Gugulethu Tshabalala, Stefanie Hornschuh, Kofi Ebenezer Okyere-Dede, Minjue Wu, Oluwatobi Ifeloluwa Ariyo, Janan J Dietrich","doi":"10.1371/journal.pdig.0000672","DOIUrl":"10.1371/journal.pdig.0000672","url":null,"abstract":"<p><p>Although South Africa is the global epicenter of the HIV epidemic, the uptake of HIV testing and treatment among young people remains low. Concerns about confidentiality impede the utilization of HIV prevention services, which signals the need for discrete HIV prevention measures that leverage youth-friendly platforms. This paper describes the process of developing a youth-friendly internet-enabled HIV risk calculator in collaboration with young people, including young key populations aged between 18 and 24 years old. Using qualitative research, we conducted an exploratory study with 40 young people including young key population (lesbian, gay, bisexual, transgender (LGBT) individuals, men who have sex with men (MSM), and female sex workers). Eligible participants were young people aged between 18-24 years old and living in Soweto. Data was collected through two peer group discussions with young people aged 18-24 years, a once-off group discussion with the [Name of clinic removed for confidentiality] adolescent community advisory board members and once off face-to-face in-depth interviews with young key population groups: LGBT individuals, MSM, and female sex workers. LGBT individuals are identified as key populations because they face increased vulnerability to HIV/AIDS and other health risks due to societal stigma, discrimination, and obstacles in accessing healthcare and support services. The measures used to collect data included a socio-demographic questionnaire, a questionnaire on mobile phone usage, an HIV and STI risk assessment questionnaire, and a semi-structured interview guide. Framework analysis was used to analyse qualitative data through a qualitative data analysis software called NVivo. Descriptive statistics were summarized using SPSS for participant socio-demographics and mobile phone usage. Of the 40 enrolled participants, 58% were male, the median age was 20 (interquartile range 19-22.75), and 86% had access to the internet. Participants' recommendations were considered in developing the HIV risk calculator. They indicated a preference for an easy-to-use, interactive, real-time assessment offering discrete and private means to self-assess HIV risk. In addition to providing feedback on the language and wording of the risk assessment tool, participants recommended creating a colorful, interactive and informational app. A collaborative and user-driven process is crucial for designing and developing HIV prevention tools for targeted groups. Participants emphasized that privacy, confidentiality, and ease of use contribute to the acceptability and willingness to use internet-enabled HIV prevention methods.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000672"},"PeriodicalIF":0.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11785273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071279","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":"Comparing imputation approaches to handle systematically missing inputs in risk calculators.","authors":"Anja Mühlemann, Philip Stange, Antoine Faul, Serena Lozza-Fiacco, Rowan Iskandar, Manuela Moraru, Susanne Theis, Petra Stute, Ben D Spycher, David Ginsbourger","doi":"10.1371/journal.pdig.0000712","DOIUrl":"10.1371/journal.pdig.0000712","url":null,"abstract":"<p><p>Risk calculators based on statistical and/or mechanistic models have flourished and are increasingly available for a variety of diseases. However, in the day-to-day practice, their usage may be hampered by missing input variables. Certain measurements needed to calculate disease risk may be difficult to acquire, e.g. because they necessitate blood draws, and may be systematically missing in the population of interest. We compare several deterministic and probabilistic imputation approaches to surrogate predictions from risk calculators while accounting for uncertainty due to systematically missing inputs. The considered approaches predict missing inputs from available ones. In the case of probabilistic imputation, this leads to probabilistic prediction of the risk. We compare the methods using scoring techniques for forecast evaluation, with a focus on the Brier and CRPS scores. We also discuss the classification of patients into risk groups defined by thresholding predicted probabilities. While the considered procedures are not meant to replace fully-informed risk calculations, employing them to get first indications of risk distribution in the absence of at least one input parameter may find useful applications in medical practice. To illustrate this, we use the SCORE2 risk calculator for cardiovascular disease and a data set including medical data from 359 women, obtained from the gynecology department at the Inselspital in Bern, Switzerland. Using this data set, we mimic the situation where some input parameters, blood lipids and blood pressure, are systematically missing and compute the SCORE2 risk by probabilistic imputation of the missing variables based on the remaining input variables. We compare this approach to established imputation techniques like MICE by means of scoring rules and visualize in turn how probabilistic imputation can be used in sample size considerations.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000712"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11781665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070172","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":"Specialty-specific Evaluation of Virtual care Outcomes: A retrospective QUality and safety analysis (S-EVOQUe).","authors":"Shawn Mondoux, Frank Battaglia, Anastasia Gayowsky, Natasha Clayton, Cailin Langmann, Paul Miller, Alim Pardhan, Julie Mathews, Alex Drossos, Keerat Grewal","doi":"10.1371/journal.pdig.0000708","DOIUrl":"10.1371/journal.pdig.0000708","url":null,"abstract":"<p><p>The objective was to compare specialty-specific 7- and 30-day outcomes between virtual care visits and in-person visits which occurred during the SARS-CoV-2 pandemic. Using administrative data from provincial databases in Ontario, ambulatory care visits occurring virtually and in-person during specific timeframes within the pandemic were analyzed. Virtual care visits were matched with corresponding in-person visits based on multiple baseline patient characteristics. We assessed short-term patient outcomes at 7 and 30 days, including subsequent visits, hospital and ICU admissions, surgeries, and mortality and compared them using multivariate logistic regression. Odds ratios were calculated as measures of association between populations. For statistical significance, we used 99% confidence intervals to account for the increased likelihood of chance findings due to the multiple comparisons conducted. Overall, 9,340,519 visits were compared between populations using a 1:1 match on a 20% random sample of the available eligible visits. Over 70% of patients included were seen by a General Practitioner. With few exceptions and across almost all specialties, revisits, ED visits, admissions, ICU and OR use, and mortality were found to be more frequent for patients seen in person. When using the administrative data available to policy makers, there is no evidence to suggest that, in the short-term, virtual care is less safe than in person care. The causes for worse in-person outcomes are not yet clear although are likely related to the streaming of more acutely unwell patients towards in-person care.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 1","pages":"e0000708"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11778768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143070181","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}