Search Results (1,279)

Telemedicine has transformed obstetrics and gynecology (OB/GYN), accelerated by the COVID-19 pandemic. This study aims to synthesize evidence on the adoption, effectiveness, barriers, and technological innovations of telemedicine in OB/GYN across diverse healthcare settings. This scoping review synthesized 63 peer-reviewed studies (2010–2023) using PRISMA-ScR guidelines to map global applications, outcomes, and challenges. Key modalities included synchronous consultations, remote monitoring, AI-assisted triage, tele-supervision, and asynchronous communication. Results demonstrated improved access to routine care and mental health support, with outcomes for low-risk pregnancies comparable to in-person services. Adoption surged >500% during pandemic peaks, stabilizing at 9–12% of services in high-income countries. However, significant disparities persisted: 43% of rural Sub-Saharan clinics lacked stable internet, while socioeconomic, linguistic, and cultural barriers disproportionately affected vulnerable populations (e.g., non-English-speaking, transgender, and refugee patients). Providers reported utility but also screen fatigue (41–68%) and diagnostic uncertainty. Critical barriers included fragmented policies, reimbursement variability, data privacy concerns, and limited evidence from conflict-affected regions. Sustainable integration requires equity-centered design, robust policy frameworks, rigorous longitudinal evaluation, and ethically validated AI to address clinical complexity and systemic gaps. Full article

Certain environmental exposures are associated with COVID-19 incidence and mortality. To determine whether environmental context is associated with other COVID-19 experiences, we used data from the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Study data (n = 1785; three survey waves 2020–2022 for adults in the United States). Environmental context was assessed using self-reported climate stress and county-level air pollution, greenness, toxic release inventory site, and heatwave data. Self-reported COVID-19 experiences included willingness to vaccinate, health impacts, receiving assistance for COVID-19, and provisioning assistance for COVID-19. Self-reported climate stress in 2020 or 2021 was associated with increased COVID-19 vaccination willingness by 2022 (odds ratio [OR] = 2.35; 95% confidence interval [CI] = 1.47, 3.76), even after adjusting for political affiliation (OR = 1.79; 95% CI = 1.09, 2.93). Self-reported climate stress in 2020 was also associated with increased likelihood of receiving COVID-19 assistance by 2021 (OR = 1.89; 95% CI = 1.29, 2.78). County-level exposures (i.e., less greenness, more toxic release inventory sites, and more heatwaves) were associated with increased vaccination willingness. Air pollution exposure in 2020 was positively associated with the likelihood of provisioning COVID-19 assistance in 2020 (OR = 1.16 per µg/m³; 95% CI = 1.02, 1.32). Associations between certain environmental exposures and certain COVID-19 outcomes were stronger among those who identify as a race/ethnicity other than non-Hispanic White and among those who reported experiencing discrimination; however, these trends were not consistent. A latent variable representing a summary construct for environmental context was associated with COVID-19 vaccination willingness. Our results suggest that intersectional equity issues affecting the likelihood of exposure to adverse environmental conditions are also associated with health-related outcomes. Full article

Background/Objectives: To determine the inter-rater agreement of visual and AI-based assessments of a renowned semi-quantitative chest CT scoring system (Pan-score) used to evaluate the severity of pulmonary involvement (e.g., ground-glass opacities, consolidations) in patients suffering from COVID-19. Methods: This retrospective study includes patients with PCR-confirmed COVID-19, who received a chest CT scan (not more than three days prior to or after the positive PCR test) between 21 March 2020 and 30 December 2022. The five lung lobes were scored separately on a scale from 0 (no pulmonary involvement) to 5 (>75% pulmonary involvement) by a radiology specialist, an experienced assistant physician, a medical student, and a dedicated AI-based software tool for chest CT. Weighted Cohen’s κ values were calculated to assess the reliability of agreement between the different readers. Results: A total of 569 consecutive patients (381 males [67.0%], 188 females [33.0%]; mean age 68.8 years) with confirmed COVID-19 were evaluated. All of them received at least one chest CT scan. There was a significant difference (p < 0.001) between the mean Pan-score evaluated by the three human readers (9.35 ± 6.03) and the score computed fully automatically by the software (10.44 ± 5.10). However, the inter-rater agreement both between the three different human readers and between the human readers and the AI was high throughout, with κ values of 0.71–0.86 and 0.83, respectively. The slice thickness of the reconstructed CT images did not have an impact on the inter-rater agreement, but the total score was significantly higher when the images were acquired following the administration of i. v. contrast media. Conclusions: The evaluated chest CT scoring system is user-friendly due to its simplicity, though it is generally prone to inaccuracies, since the estimation of the extent of pulmonary involvement is quite subjective. Nevertheless, the inter-rater agreement was high throughout, both between the differently experienced human readers and between the human readers and the AI software. In summary, the Pan-score seems to be a reliable approach to estimate the extent of pulmonary involvement in patients suffering from COVID-19. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

Hand hygiene (HH) is essential for preventing disease transmission, particularly in schools where children are in close contact with other children. This study evaluated a school-based intervention on observed HH practices and hand cleanliness in six primary schools in Guatemala. Hand cleanliness was measured using the Quantitative Personal Hygiene Assessment Tool. The intervention included (1) HH behavior change promotion through Handwashing Festivals, and (2) increased access to HH materials at HH stations. Handwashing Festivals were day-long events featuring creative student presentations on HH topics. Schools were provided with soap and alcohol-based hand rub throughout the project to support HH practices. Appropriate HH practices declined from 51.2% pre-intervention to 33.1% post-intervention, despite an improvement in median Quantitative Personal Hygiene Assessment Tool scores from 6 to 8, indicating cleaner hands. Logistic regression showed higher odds of proper HH when an assistant was present. The decline in HH adherence was likely influenced by fewer assistants and changes in COVID-19 policies, while improvements in hand cleanliness may reflect observational bias. These findings emphasize the importance of sustained behavior change strategies, reliable HH material access, and targeted interventions to address gaps in HH practices, guiding school health policy and resource allocation. Full article

Background: Insomnia and excessive sleepiness are significant health problems with a complex etiology, increasingly affecting young people, especially students. This study aimed to assess the prevalence of sleep disturbances and patterns of psychoactive drug use among female Polish students. We also explored the potential impact of the COVID-19 pandemic on sleep behaviors. We hypothesized that sleep disorders are common in this group, that medical students are more likely to experience insomnia and excessive sleepiness, and that the pandemic has exacerbated both sleep disturbances and substance use. Methods: This cross-sectional study utilized a custom survey designed using standardized questionnaires—the Athens Insomnia Scale and Epworth Sleepiness Scale—that was distributed online using the Computer-Assisted Web Interviewing method. A total of 11,988 responses were collected from 31 January 2016 to 1 January 2021. Inclusion criteria were being female, having a college student status, and giving informed consent. Results: Among the 11,988 participants, alcohol use declined after the pandemic began (p = 0.001), while sedative use increased (p < 0.001). Insomnia (AIS) was associated with study year, university profile, and field of study (p < 0.001), with the highest rates in first-year and non-medical students. It was more common among users of sedatives, psychostimulants, and multiple substances. No significant change in insomnia was found before and after the pandemic. Excessive sleepiness (ESS) peaked in first-year and medical students. It decreased during the pandemic (p < 0.001) and was linked to the use of alcohol, psychostimulants, cannabinoids, and multiple substances. Conclusions: These findings highlight that female students are particularly vulnerable to sleep disorders. The influence of the COVID-19 pandemic on sleep disturbances remains inconclusive. Given the varied results in the existing literature, further research is needed. Full article

This study included 221 patients with COVID-19 who were admitted to the emergency department of Avicenne Hospital in Rabat between August 2020 and August 2021. Patients were divided into three groups according to their D-dimer levels (<1, 1–2, and >2 µg/mL). Adjusted and unadjusted logistic regression analyses were performed to assess the association between elevated D-dimer levels and in-hospital mortality. Pearson’s correlation analysis was performed to explore the relationship between D-dimer levels and various biological and clinical parameters. The results revealed a statistically significant difference in the mean (SD) age among the three groups (p = 0.006). Analysis showed a statistically significant difference in the means (SD) of oxygen saturation, duration of hospital stay, and breathing rate among the three independent groups of COVID-19 patients. Patients with elevated D-dimer levels (greater than 2 µg/mL) experienced worse outcomes than those in the other groups, with severity, transfer to intensive care, and in-hospital mortality of 55 (40.7%), 35 (16%), and 24 (11%) patients, respectively, with p-values of 0.048, 0.002, and 0.002, respectively. Patients in the D-dimer > 2 µg/mL group had significantly higher C-reactive protein (CRP), lactate dehydrogenase, urea, cardiac troponin, B-type natriuretic peptide, and ferritin levels than those in the other two groups. The p-value was significant among the three groups (p = 0.044, p = 0.001, and p < 0.001). Age and elevated D-dimer levels (greater than 2 µg/mL) were associated with mortality in patients diagnosed with COVID-19. Correlation analysis indicated that D-dimer in COVID-19 patients is associated with worsening respiratory, hepatic, cardiac, and coagulation parameters, suggesting their utility as an integrative marker of disease severity. D-dimer levels > 2 µg/mL were identified as an independent risk factor for COVID-19 in-hospital mortality. Measuring and monitoring D-dimer levels can assist clinicians in taking timely actions and predicting the prognosis of patients with COVID-19. Full article

Background: This study examined risk factors for syndrome novel coronavirus 2 virus (SARS-CoV-2) infection and self-reported adherence to infection prevention and control (IPC) measures among healthcare workers (HCWs) at a hospital in Thailand near the Myanmar border. Methods: From March to July 2022, HCWs aged ≥ 18 with COVID-19 exposure at Mae Sot General Hospital completed a questionnaire on IPC adherence, training, and COVID-19 knowledge. Nasopharyngeal samples were collected bi-weekly for SARS-CoV-2 testing. A mobile application was used for real-time monitoring of daily symptoms and exposure risks. Chi-square, Fisher’s exact tests, and log-binomial regression were performed to investigate association. Results: Out of 289 (96.3%) participants, 27 (9.9%) tested positive for SARS-CoV-2, with cough reported by 85.2% of cases. Nurse assistants (NAs) had a higher risk of infection (adjusted relative risk [aRR] 3.87; 95% CI: 0.96–15.6). Working in inpatient departments (aRR 2.37; 95% CI: 1.09–5.15) and COVID-19 wards (aRR 5.97; 95% CI: 1.32–26.9) was also associated with increased risk. While 81.7% reported consistent hand hygiene, 37% indicated inadequate IPC knowledge. Conclusions: HCWs, especially NAs and those in high-risk departments, should receive enhanced IPC training. Real-time digital monitoring tools can enhance data collection and HCW safety and are likely to be useful tools for supporting surveillance and data collection efforts. Full article

Accurate classification of chest X-ray (CXR) images is crucial for diagnosing lung diseases in medical imaging. Existing deep learning models for CXR image classification face challenges in distinguishing non-lung features. In this work, we propose a new segmentation-assisted fusion-based classification method. The method involves two stages: first, we use a lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet) designed based on an encoder–decoder architecture, to accurately obtain lung masks from CXR images. Then, a fusion of the masked CXR image with the original image enables classification using the improved lightweight ShuffleNetV2 model. The proposed method is trained and evaluated on segmentation datasets including the Montgomery County Dataset (MC) and Shenzhen Hospital Dataset (SH), and classification datasets such as Chest X-Ray Images for Pneumonia (CXIP) and COVIDx. Compared with seven segmentation models (U-Net, Attention-Net, SegNet, FPNNet, DANet, DMNet, and SETR), five classification models (ResNet34, ResNet50, DenseNet121, Swin-Transforms, and ShuffleNetV2), and state-of-the-art methods, our PCSNet model achieved high segmentation performance on CXR images. Compared to the state-of-the-art Attention-Net model, the accuracy of PCSNet increased by 0.19% (98.94% vs. 98.75%), and the boundary accuracy improved by 0.3% (97.86% vs. 97.56%), while requiring 62% fewer parameters. For pneumonia classification using the CXIP dataset, the proposed strategy outperforms the current best model by 0.14% in accuracy (98.55% vs. 98.41%). For COVID-19 classification with the COVIDx dataset, the model reached an accuracy of 97.50%, the absolute improvement in accuracy compared to CovXNet was 0.1%, and clinical metrics demonstrate more significant gains: specificity increased from 94.7% to 99.5%. These results highlight the model’s effectiveness in medical image analysis, demonstrating clinically meaningful improvements over state-of-the-art approaches. Full article

Introduction: Efficient communication and collaboration among local health departments (LHDs), healthcare organizations, governmental entities, and other community stakeholders are critical for public health preparedness and response. This study evaluates (1) the impact of informatics on LHDs’ frequency and collaboration in creating consistent COVID-19 messaging; (2) the influence of informatics on targeted messaging for vulnerable populations; and (3) LHD characteristics linked to their consistent and/or targeted messaging engagement. Methods: This study analyzed the 2020 National Association of County and City Health Officials (NACCHO) Forces of Change (FOC) survey, the COVID-19 Edition. Of the 2390 LHDs invited to complete the core questionnaire, 905 were asked to fill out the module questionnaire as well. The response rate for the core was 24% with 587 responses, while the module received 237 responses, achieving a 26% response rate. Descriptive analyses and six logistic regression models were utilized. Results: Over 80% (183) of LHDs collaborated regularly with public health partners, and 95% (222) used information management applications for COVID-19. Most interacted with local and state agencies, but only half with federal ones. LHDs that exchanged data with local non-health agencies, engaged with local non-health agencies, and communicated weekly to daily with the public about long-term/assisted care had higher odds of creating consistent messages for the public, and about the use and reuse of masks had lower odds of collaborating with public health partners to develop consistent messages for the public. Conclusion: Our findings underscore the centrality of informatics infrastructure and collaboration in ensuring equitable public health messaging. Strengthening public health agencies and investing in targeted training are crucial for effective communication across the communities served by these agencies. Full article

Background/Objectives: In Germany, the provision of healthcare for post-COVID patients primarily lies with general practitioners (GPs), who often lack the necessary knowledge and skills. As part of the PostCovidCare pilot study (PCC), case management software incorporating a symptom diary was introduced and piloted in n = 10 GP practices with n = 33 included patients involved (September 2022–March 2023). This study aimed to explore patients’ experiences. Methods: Semi-structured telephone interviews were transcribed and analyzed using qualitative content analysis. A total of n = 10 patient interviews were conducted (July–September 2023). Results: Patients’ experiences were heterogeneous. The service was largely structured, involving an extensive initial assessment, follow-up appointments, questionnaires, and support from medical assistants, but technical problems with the symptom diary occurred. The GP consultation played a prominent role. Positive aspects included being actively asked about their symptoms, being given a lot of time, initiating diagnostic and therapeutic measures, and having a closer relationship with their GP. Negative aspects included the time taken, resulting exhaustion, duplication of efforts, and insufficient involvement in the consultation process. Conclusions: The pilot study conducted at an early stage of the post-COVID era demonstrated the basic feasibility of case management in primary care from patients’ perspectives. In addition, for future projects, it is important to integrate patients into the design from the outset, adapt the software to users’ needs, and consider care providers’ perspectives. Full article

COVID-19 has caused global problems with complex ramifications. Vulnerable households worry about disruptions to food security. Mobility restrictions, decreased salaries, and supply chain disruptions have increased food insecurity. This study examined the best food security model and its determinants. The primary research data were collected from 257 respondents via cluster random sampling. Binary logistic regression, using R-Studio, was employed to analyze the data. The study showed that the Minimal Model (MM) was optimal in explaining food security status, with three predictors: the available food stock (AFS), education of the household head (EHH), and household income (HIc). This aligned with studies showing that food purchase ability depends on income and education. Male household heads demonstrated better food security than females, while women’s education influenced consumption through improved nutritional knowledge. Higher income provides more alternatives for meeting needs, while decreased income limits options. Food reserve storage influenced household food security during the pandemic. The Minimal Model effectively influenced food security through the AFS, EHH, and HIc. The findings underline the importance of available food stock, household head education, and household income in developing approaches to assist food-insecure households. The research makes a significant contribution to ensuring food availability and promoting sustainable development post-pandemic. Full article

This paper describes the development of a CNN model for the analysis of chest X-rays and the automated diagnosis of pneumonia, bacterial or viral, and lung pathologies resulting from COVID-19, offering new insights for further research through the development of an AI-based diagnostic tool, which can be automatically implemented and made available for rapid differentiation between normal pneumonia and COVID-19 starting from X-ray images. The model developed in this work is capable of performing three-class classification, achieving 97.48% accuracy in distinguishing chest X-rays affected by COVID-19 from other pneumonias (bacterial or viral) and from cases defined as normal, i.e., without any obvious pathology. The novelty of our study is represented not only by the quality of the results obtained in terms of accuracy but, above all, by the reduced complexity of the model in terms of parameters and a shorter inference time compared to other models currently found in the literature. The excellent trade-off between the accuracy and computational complexity of our model allows for easy implementation on numerous embedded hardware platforms, such as FPGAs, for the creation of new diagnostic tools to support medical practice. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article