PLOS digital health最新文献

The goal of this study was to determine the difference in participant understanding, satisfaction, timing and, preference between video consent and written informed consent in a pediatric rheumatology research setting. Participants were randomized to receive either video consent or written informed consent for a registry study. After completing the first consent method, they completed a comprehension and satisfaction questionnaire. Then they received the alternate consent method and completed a second set of questionnaires. Bayesian non-parametric tests determined the difference in comprehension, satisfaction, timing and preference between video consent and written informed consent. Ninety-nine caregivers and 76 patients were randomized into video consent (n = 88) and written informed consent (n = 87) groups. Comprehension (Max = 12) and satisfaction (Max = 5) were high in both groups. There was moderate evidence supporting no difference in comprehension (medianvideo consent = 11 and medianwritten informed consent = 10) and satisfaction (medianvideo consent = 4 and medianwritten informed consent = 5) between video consent and written informed consent (BF10 = 0.225 and 0.32, respectively). The median time to complete video consent and written informed consent was 408 (95% Credible Interval (CrI): 397-412) and 360 (95% CrI: 329-391) seconds, respectively. There was decisive evidence that video consent increased the time of consent (in our sample by 48 seconds) compared to written informed consent (BF10 = 713). There was decisive evidence for participants preferring video consent over written informed consent (BF10 = 2.307x1011) as they thought it was easier to follow. Overall, participant understanding and satisfaction were comparable between video consent and written informed consent. Even though video consent was slightly less time efficient compared to written informed consent, video consent was highly preferred by caregivers and patients, supporting its use to obtain informed consent.

Pediatric emergency medicine (PEM) presents unique challenges due to the diverse developmental stages and medical conditions of young patients. The increasing patient load and nonurgent referrals to pediatric emergency departments (PEDs) emphasize the need for personalized decision-making approaches. These approaches must accommodate the complexities of pediatric care while fostering collaboration between healthcare providers and families. Integrating artificial intelligence (AI) into healthcare settings can transform PEM by enhancing diagnostic accuracy, customizing treatments, and optimizing resource allocation. AI technologies leverage vast datasets, including electronic health records and genetic profiles, to generate personalized diagnostic and treatment plans. Machine learning algorithms can identify patterns in complex data, facilitating early disease detection and precise interventions. This literature review analyzes the role of AI in supporting pediatric emergency care through diagnostic assistance, predictive modeling for febrile disease progression, and outcome optimization. It also highlights the challenges of applying AI in PEM, including data limitations and the need for algorithmic transparency. By addressing these challenges, AI has the potential to revolutionize personalized care in pediatric emergency settings, ultimately improving patient outcomes and care delivery.

Individuals diagnosed with addiction or mental health (AMH) conditions are more likely to experience potentially adverse outcomes of homelessness. Despite their link to later outcomes, research on initial episodes of AMH outcomes is limited. This study aims to use administrative data to identify the factors associated with the first healthcare encounters with indicators of homelessness (FHE-H) for individuals diagnosed with AMH. We assessed logistic regression and compared its performance with machine learning models, including random forests and extreme gradient boosting (XGBoost). We conducted a retrospective cohort study linking several administrative datasets for 232,253 individuals with Alberta health insurance in Calgary, Canada, who were aged between 18 and 65 and diagnosed with AMH between April 1, 2013, and March 31, 2018. We assessed outcomes in two years following cohort entry. Individuals with episodes of FHE-H (2,606 individuals) before the index date were excluded. Multivariable logistic regression models were used to identify factors associated with outcomes by estimating adjusted odds ratios (AORs) with 95% confidence intervals. Among 229,647 individuals diagnosed with AMH, 1,886 (0.82%) experienced FHE-H during the follow-up period. Mental health emergency visits (AORs=5.28 [95% CI: 4.41, 6.33]), substance misuse (AORs=3.87 [95% CI: 3.28, 4.56], substance use disorders (AORs=2.03 [95% CI: 1.64, 2.50]), and male sex (AORs=1.28 [95% CI: 1.14, 1.44]) were associated with FHE-H. XGBoost performance improved over logistic regression, with increases in area under the curve (AUC) by 1% and precision by 2%. Overall, several AMH features were associated with FHE-H, and machine learning models outperformed logistic regression, although to a small degree.

Sexual Reproductive Health (SRH) self-care offers a pathway for low income countries to advance towards Universal Health Coverage by empowering individuals, families, and communities to prioritize their SRH needs independently of healthcare providers. Facilitating access to SRH products is crucial for embracing self-care and digital health technologies hold promise for enhancing accessibility. This study explored the role played by rocket health digital platforms in enhancing accessibility to SRH self-care products among youth in Uganda. Employing a cross-sectional design with a mixed-method approach, the study involved key informant interviews with youth who had purchased SRH self-care products from Rocket Health in 2022, as well as key staff at Rocket Health. Quantitative data were extracted from Rocket Health's Electronic Medical Records covering the period from January 2022 to December 2022.More males (57%) utilized digital platforms for SRH compared to females (43%). The highest utilization was via the E-commerce platform (49%) while the least was via the voice call platforms (4%). A notable portion of youth (30%) still relied on visiting the pharmacy. Contraception products were predominantly consumed through digital platforms (44%), whereas self-testing were less frequently utilized (14%). The study also identified key resources such as the digital infrastructure that maximize the potential of digital health platforms in enhancing SRH self-care. By gaining insights into the digital infrastructure, preferences, barriers, and financial considerations associated with accessing SRH self-care services through digital platforms, targeted interventions such as access to contraceptives, awareness programs, prevention and treatment of Sexual Transmitted Infections can be developed to promote positive SRH outcomes among youth.

Diabetes technology generates vital health data, but healthcare professionals (HCP) and patients must navigate multiple platforms to access it. We developed a digital health platform, co-designed with patients and families living with type 1 diabetes (T1D) and their HCPs, that aim to support a collaborative care experience through shared access to diabetes data, clinical recommendations, and resources. We describe caregivers' views on the platform's impact on clinic visits and child self-management in children with T1D. A six-month observational pilot study at BC Children's Hospital Diabetes Clinic in British Columbia, Canada, gathered data through surveys and interviews. Surveys were administered to caregivers and HCPs at different time points throughout the study; 18 qualitative interviews were conducted with caregivers at the conclusion of the study. Quantitative data were summarized descriptively. Interview data were transcribed, coded using open and systematic coding, and subsequent inductive thematic analysis. Eighteen caregivers completed the surveys, and 11 HCP participants submitted 41 surveys (approximately 3-4 each) after using the platform. Most caregivers (61%; 11/18) found the platform helpful, and 56% (10/18) reported that using the platform made their clinical visits and recommendations more personalized. Nearly all HCPs (90%; 37/41) were satisfied with the platform's ability to support clinical visits. Themes identified from caregiver qualitative interviews revealed that (1) the platform provided a convenient connection that improved preparedness and empowered caregivers in managing their child's T1D; (2) the platform's value was driven by the healthcare team's usage of it; and (3) caregivers felt hopeful that the platform could better support their child's T1D management. The platform could foster a collaborative and personalized care experience that enables caregivers to engage in diabetes self-management and feel connected to their healthcare team. These results will guide the future development, evaluation, and implementation of the platform.

[This corrects the article DOI: 10.1371/journal.pdig.0000926.].

At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram (ECG), which can be difficult to interpret manually. There is a long history of applying machine learning algorithms to ECGs, but such algorithms are quite data hungry, and correctly labeled high-quality ECGs are difficult to obtain. Transfer learning has been a successful strategy for mitigating data requirements in other applications, but the benefits for predicting AMI are understudied. Here we show that a straightforward application of transfer learning leads to large improvements also in this domain. We pre-train models to classify sex and age using a collection of 840 k ECGs from non-chest-pain patients, and fine-tune the resulting models to predict AMI using 44 k ECGs from chest-pain patients. The results are compared with models trained without transfer learning. We find a considerable improvement from transfer learning, consistent across multiple state-of-the-art ResNet architectures and data sizes, with the best performing model improving from 0.79 AUC to 0.85 AUC. This suggests that even a simple form of transfer learning from a moderately sized dataset of non-chest-pain ECGs can lead to major improvements in predicting AMI.

Dental age (DA) estimation is a key diagnostic tool in pediatric dentistry, particularly when birth records are unavailable or unreliable. It guides decisions on growth assessment, orthodontic planning, and timing of interventions such as space maintenance or extractions. Unlike skeletal maturity, dental development is less affected by nutritional and environmental factors, making it a reliable marker of biological age. Conventional methods require expert interpretation and are prone to variability. There is growing interest in automated, objective approaches to streamline this process and enhance clinical utility. A total of 550 panoramic radiographs from children aged 3-14 years were labeled into 11 dental age groups based on the AAPD reference chart by two experienced pediatric dentists. Images with poor quality were excluded. The dataset was divided into training (80%) and validation (20%) sets, with data augmentation applied to the training set. The YOLOv11n-cls model, consisting of 86 layers and 1.54 million parameters, was trained for 30 epochs using the Ultralytics engine and AdamW optimizer. Model performance was evaluated using Top-1 and Top-5 accuracy on the validation set and tested on an independent set of 203 images. Grad-CAM was used for model interpretability. The model achieved 92.6% Top-1 and 99.5% Top-5 accuracy on the validation set. Performance on the test set remained high, with most misclassifications occurring between adjacent age groups. Grad-CAM visualizations showed attention to clinically relevant areas like erupting molars and root development. The findings support the high performance of DL, through YOLOv11 for pediatric age prediction. The AI tool enabled fast, accurate, and interpretable DA classification, making it a strong candidate for clinical integration as an adjunct tool into pediatric dental practice.

The availability of medical imaging data is indispensable for medical advancements such as the development of new diagnostic tools, improved surgical navigation systems, and profiling for personalized medicine through imaging biomarkers. A central challenge in data governance is balancing the need to protect patient privacy with the necessity of promoting scientific innovation. Restrictive data governance policies could limit access to the large, high-quality datasets needed for such advancements. Conversely, lenient policies could compromise patient trust and lead to potential misuse of sensitive information. We call for a deliberate and well-considered approach to data governance, highlighting important factors that patients and healthcare organizations should consider when making imaging data governance decisions around data sharing.