Frontiers in digital health最新文献

{"title":"The impact of Internet-based healthcare derived from the COVID-19 pandemic on outpatients in a cardiology department.","authors":"Jie Liu, Yunhan Fei, Ying Gao, Yu Meng, Dongxue Huang, Wenjuan Zhao, Keliang Xie","doi":"10.3389/fdgth.2025.1475422","DOIUrl":"10.3389/fdgth.2025.1475422","url":null,"abstract":"<p><strong>Objective: </strong>This study examines the impact of a COVID-19 pandemic-derived online medical service on cardiovascular patient visits and assesses whether these services can ease the strain on medical resources.</p><p><strong>Method: </strong>This study investigated the impact of the COVID-19 pandemic on cardiology services and hospital operations. We analyzed key primary medical indicators in cardiology, including outpatient visits, inpatient improvement rates, cure rates, and mortality rates, over three years from 2019 to 2021. Furthermore, the study assessed the influence of the development of Internet-based medical services on the treatment of cardiovascular disease. Specifically, we compared the changes in the number of outpatient visits in four categories of offline outpatient clinics in the Department of Cardiology during two phases: Phase I (1 February 2019 to 28 February 2020) and Phase II (1 March 2020 to 28 February 2021).</p><p><strong>Results: </strong>Compared to the period before online services (T1), the second stage (T2) saw a significant decrease in total offline and general clinic visits. After the establishment of the online clinic, the third period (P3) showed a significant reduction in total offline, general, and senior clinic visits compared to the first period (P1), while vice-senior and VIP/international clinic visits increased. The number of online clinic visits and VIP/international clinic visits continued to rise. Online consultations had the highest proportion (55.9%), while prescriptions and examinations had the lowest (3.3%), although they showed a gradually increasing trend. After the implementation of the online clinic, the improvement rate of patients' conditions increased and the mortality rate decreased.</p><p><strong>Conclusion: </strong>Since the advent of online medical services, cardiovascular patients have increasingly opted for online diagnosis and treatment. Since March 2021, the online outpatient service has driven the overall growth in hospital outpatient numbers while maintaining medical quality. The primary use of the online medical service is for consultations, which shortens medical time and reduces implicit costs for patients.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1475422"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560361","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 and testing tele-support psychotherapy using mobile phones for depression among youth in Kampala district, Uganda: study protocol for a pilot randomized controlled trial.","authors":"Etheldreda Nakimuli-Mpungu, Jeremiah Mutinye Kwesiga, John Mark Bwanika, Davis Musinguzi, Caroline Nakanyike, Jane Iya, Sabrina Bakeera Kitaka, Benedict Akimana, Charlotte Hawkins, Patricia Cavazos, Jean B Nachega, Edward J Mills, Musisi Seggane","doi":"10.3389/fdgth.2025.1515193","DOIUrl":"10.3389/fdgth.2025.1515193","url":null,"abstract":"<p><strong>Introduction: </strong>In the post-COVID-19 era, depressive disorders among youth have risen significantly, creating an urgent need for accessible, cost-effective mental health interventions. This study adapts Group Support Psychotherapy into Tele-Support Psychotherapy (TSP) via mobile phones. It aims to evaluate its feasibility, acceptability, effectiveness, and cost-efficiency in addressing mild to moderate depression among youth in central Uganda.</p><p><strong>Methods and analysis: </strong>This study will use a mixed-methods approach, starting with a qualitative phase to adapt Group Support Psychotherapy into Tele-Support Psychotherapy (TSP) via mobile phones. Guided by ecological theories and the Unified Theory of Acceptance and Use of Technology (UTAUT), focus group discussions and interviews with youth, mental health professionals, and stakeholders will inform the development of a youth-tailored call platform integrated into Rocket Health Africa's telehealth services. Data will be analyzed using grounded theory and MAXQDA Analytics Pro 2022 to guide intervention adaptation. An open-label randomized controlled trial will enroll 300 youth (15-30 years) with mild to moderate depression from Kampala, Uganda, to evaluate Tele-Support Psychotherapy (TSP). Participants will be randomized to TSP with standard mental health services (SMHS) or SMHS alone. Primary outcomes include feasibility and acceptability, with secondary outcomes assessing cost-effectiveness, depressive symptom changes, and social support. Intention-to-treat analysis using structural equation modeling will evaluate treatment effects, complemented by qualitative insights into implementation barriers and facilitators.</p><p><strong>Discussion: </strong>This study protocol develops and evaluates Tele-Support Psychotherapy (TSP) for youth depression in resource-limited settings, addressing mental health gaps exacerbated by COVID-19. Using user-centered design and mixed methods, it explores TSP's feasibility, adaptability, and cost-effectiveness while addressing barriers like technology literacy, laying the groundwork for accessible digital mental health solutions.</p><p><strong>Trial registration: </strong>PACTR202201684613316.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1515193"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560360","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":"Bridging language barriers in healthcare: a patient-centric mobile app for multilingual health record access and sharing.","authors":"Theodoros Solomou, Stelios Mappouras, Efthyvoulos Kyriacou, Ioannis Constantinou, Zinonas Antoniou, Ionut Cristian Canciu, Marios Neophytou, Zoltan Lantos, Christos N Schizas, Constantinos S Pattichis","doi":"10.3389/fdgth.2025.1542485","DOIUrl":"10.3389/fdgth.2025.1542485","url":null,"abstract":"<p><strong>Introduction: </strong>Access to health data for patients is hindered by a fragmented healthcare system and the absence of unified, patient-centric solutions. Additionally, there are no mechanics for easy sharing of medical records with healthcare providers, risking incomplete diagnoses. To further intensify the problem, when patients seek care abroad, language barriers may prevent foreign doctors from understanding their health data, further complicating treatment.</p><p><strong>Methods: </strong>Our study presents the development and evaluation of a mobile application designed to enable users to access and share their health records directly from their device, in multiple languages, ensuring ease of use and convenience. The solution utilizes OpenNCP for translating patient summaries into multiple languages and the FHIR Smart Health Links Protocol for secure sharing. We conducted a user acceptance study with 45 participants to evaluate our mobile app's interface and functionality.</p><p><strong>Results: </strong>The feedback was positive, highlighting the app's user-friendliness and usefulness. The participants felt it would enhance communication between physicians and patients and the features of sharing and translating are going to give more control of their medical data to the patients.</p><p><strong>Discussion: </strong>Based on the results and participants feedback, our mobile solution significantly enhances healthcare accessibility and efficiency by enabling easy access and sharing of health records in multiple languages, using relevant protocols and standards, reducing medical errors and ensuring personalized care.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1542485"},"PeriodicalIF":3.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560353","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":"Evaluating working memory in young individuals with normal hearing through tele-assessment and traditional assessment: a comparative study.","authors":"Kavassery Venkateswaran Nisha, N Devi, B Vandana, E Rashmi, Shraddha A Shende, Raksha A Mudar","doi":"10.3389/fdgth.2025.1499737","DOIUrl":"10.3389/fdgth.2025.1499737","url":null,"abstract":"<p><strong>Aim: </strong>This pilot study examined the feasibility of tele-assessment of working memory (WM) compared to conventional face-to-face assessment.</p><p><strong>Methods: </strong>In total, 15 young adults aged between 18 and 30 years who were native speakers of Kannada with normal hearing completed three WM tests in Indian English: forward digit span, backward digit span, and n-back task through tele-assessment and in-person/face-to-face assessment. The National Aeronautics and Space Administration (NASA) task load index, which assesses subjective workload, was used to determine the difficulties across the two modes of assessment.</p><p><strong>Results: </strong>Paired comparison <i>t</i>-tests showed no significant differences in performance in the forward digit span (<i>p</i> = 0.29), backward digit span (<i>p</i> = 0.71), and n-back (<i>p</i> = 0.66) tasks across the two assessment conditions. Furthermore, the NASA task load index did not differ across the two assessment conditions for forward digit span (<i>p</i> = 0.29), backward digit span (<i>p</i> = 0.71), and n-back (<i>p</i> = 0.66). The Wilcoxon signed-rank test showed that the backward digit span task was the most difficult among the working memory tasks in both modes of assessment. The findings of our pilot study suggest that both modes can be used successfully to assess working memory, and tele-assessment yields similar results to face-to-face WM assessment in young normal-hearing adults. These results support the feasibility of conducting WM tests via tele-assessment, which has implications for use in clinical populations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1499737"},"PeriodicalIF":3.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544743","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":"Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning.","authors":"Emeka Abakasanga, Rania Kousovista, Georgina Cosma, Ashley Akbari, Francesco Zaccardi, Navjot Kaur, Danielle Fitt, Gyuchan Thomas Jun, Reza Kiani, Satheesh Gangadharan","doi":"10.3389/fdgth.2025.1538793","DOIUrl":"https://doi.org/10.3389/fdgth.2025.1538793","url":null,"abstract":"<p><strong>Purpose: </strong>Individuals with learning disabilities (LD) often face higher rates of premature mortality and prolonged hospital stays compared to the general population. Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. However, there is limited research on the application of machine learning (ML) models to this population. Furthermore, approaches designed for the general population often lack generalisability and fairness, particularly when applied across sensitive groups within their cohort.</p><p><strong>Method: </strong>This study analyses hospitalisations of 9,618 patients with LD in Wales using electronic health records (EHR) from the SAIL Databank. A Random Forest (RF) ML model was developed to predict hospital LOS, incorporating demographics, medication history, lifestyle factors, and 39 long-term conditions. To address fairness concerns, two bias mitigation techniques were applied: a post-processing threshold optimiser and an in-processing reductions method using an exponentiated gradient. These methods aimed to minimise performance discrepancies across ethnic groups while ensuring robust model performance.</p><p><strong>Results: </strong>The RF model outperformed other state-of-the-art models, achieving an area under the curve of 0.759 for males and 0.756 for females, a false negative rate of 0.224 for males and 0.229 for females, and a balanced accuracy of 0.690 for males and 0.689 for females. Bias mitigation algorithms reduced disparities in prediction performance across ethnic groups, with the threshold optimiser yielding the most notable improvements. Performance metrics, including false positive rate and balanced accuracy, showed significant enhancements in fairness for the male cohort.</p><p><strong>Conclusion: </strong>This study demonstrates the feasibility of applying ML models to predict LOS for patients with LD and MLTCs, while addressing fairness through bias mitigation techniques. The findings highlight the potential for equitable healthcare predictions using EHR data, paving the way for improved clinical decision-making and resource management.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1538793"},"PeriodicalIF":3.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11868268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544777","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":"What social media analyses can tell us about Ghanaian women's concerns during pregnancy.","authors":"Martina Anto-Ocrah, Tori Valachovic, Joseph W Lanning, Ali Ghanem, Claire Couturier, Celestin Hakizimana, Celestin Niyomugabo, Nabeeha Jabir Affan, Hemika Vempalli, Ruth Sally Kodam","doi":"10.3389/fdgth.2025.1479392","DOIUrl":"10.3389/fdgth.2025.1479392","url":null,"abstract":"<p><strong>Introduction: </strong>Social media platforms are used by over 4.9 billion people for networking and community building, as well as for healthcare information seeking and decision-making. Most studies investigating the utilization of social media during pregnancy have focused on high-income countries, despite the surge in social media utilization globally. In this study, we analyzed how pregnant women in Ghana, West Africa, utilize Facebook to inform decision-making on their most salient pregnancy concerns.</p><p><strong>Methods: </strong>We utilized machine learning techniques (Web Scraping and Latent Dirichlet Allocation) to mine and analyze posts from the Ghana-based MidWife Sally Pregnancy School Facebook group between August 16, 2020 and April 29, 2023. Posts were extracted, cleaned, and analyzed using Gensim python library. Topics were generated based on their probabilities and relevance to the study goal.</p><p><strong>Results: </strong>A total of 3,328 posts were extracted and 3,322 were analyzed after removing 6 empty posts. Seven major topics with listed subtopics were identified: Pregnant (693 posts): personal physiological changes, exercise during pregnancy, medication (e.g., anti-malarials, pain killers) Delivery (367): emergency delivery, vaginal/caesarean birthing, breastmilk production, exercise during pregnancy Pain (350): location of pain and pain relief modalities (e.g., exercise, medication, sleep) Breastfeeding (248): delivery, emergency service, milk production Water (174): cold water consumption, infant feeding (e.g., gripe water, constipation, formula) Sleeping (165): discomfort, sleeping positions, exercise to induce sleep, sleep as a natural analgesic Antenatal (124): fetal growth, progress, hospital selection Of note, content from \"Pregnant\", \"Delivery\" and \"Sleeping\" included mentions of depression, while \"Breastfeeding\" highlighted cultural approaches to increasing milk production. The sentiment analysis showed that 43.4% of the responses were neutral and primarily focused on seeking information. Negative sentiments, which were more distressing, comprised 46.4% of the responses, while positive sentiments, had a celebratory tone and represented 10.2% of the data.</p><p><strong>Conclusion: </strong>Social media analysis, previously employed in high income settings, can provide impactful, granular snapshots of pregnant people's concerns in the African region, which could be used to inform social media interventions aimed at filling educational gaps in antenatal care for those without adequate healthcare access.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1479392"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525479","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":"Usefulness of lumbar puncture educational videos for older people with HIV.","authors":"Chhitij Tiwari, Keely Copperthite, Tia Morgan, Jonathan Oakes, Luigi Troiani, Chris Evans, Sonia Napravnik, Claire E Farel, Monica M Diaz","doi":"10.3389/fdgth.2025.1508163","DOIUrl":"10.3389/fdgth.2025.1508163","url":null,"abstract":"<p><strong>Background: </strong>Video-based education offers opportunities to enhance patients' medical literacy and to reduce anxiety and hesitation for patients undergoing diagnostic procedures such as lumbar puncture (LP). Multiple studies centered on LP education have demonstrated that video-based education can reduce anxiety regarding possible adverse events, while increasing literacy regarding the procedure itself for clinical purposes. Our study sought to assess the impact of video-based education on knowledge of and willingness to undergo an LP among older people with HIV (PWH).</p><p><strong>Methods: </strong>We enrolled PWH age ≥ 50 years who regularly attend our Infectious Diseases clinic between March 3 and November 16, 2023. Participants watched a patient-centered educational video explaining the LP procedure and completed a questionnaire both pre- and post-video assessing demographics, general awareness and prior experience with an LP, specific knowledge, attitudes and perceptions toward an LP and willingness to undergo an LP.</p><p><strong>Results: </strong>Our study included 99 PWH with mean (standard deviation, SD) age of 58.8 (5.7) years, one-third females and 60% African American/Black race. After watching the video, participants were significantly more likely to correctly identify technical details of the procedure (excluding those who had previously had an LP, 83.7% pre-video vs. 95.9% post-video) and common complications of an LP; agree that LPs can result in back pain (<i>p</i> < 0.001) and headaches (<i>p</i> < 0.001). There was no significant difference in participants' willingness to undergo an LP for diagnostic or research purposes. Only 5% said that they would never have an LP under any circumstance after watching the video.</p><p><strong>Conclusions: </strong>Other educational interventions, such as in-person demonstrations or models, may help mitigate fears of LP. Our study provides important insight into the knowledge and perceptions of PWH when asked to undergo an LP and demonstrates that video-based education may not be sufficient to mitigate fears surrounding LP procedures, or a lack of interest or time for participating in an LP.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1508163"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525477","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":"Evaluating the feasibility of using the Multiphase Optimization Strategy framework to assess implementation strategies for digital mental health applications activations: a proof of concept study.","authors":"Ayla Aydin, Wouter van Ballegooijen, Ilja Cornelisz, Anne Etzelmueller","doi":"10.3389/fdgth.2025.1509415","DOIUrl":"10.3389/fdgth.2025.1509415","url":null,"abstract":"<p><strong>Background: </strong>Despite the effectiveness and potential of digital mental health interventions (DMHIs) in routine care, their uptake remains low. In Germany, digital mental health applications (DiGA), certified as low-risk medical devices, can be prescribed by healthcare professionals (HCPs) to support the treatment of mental health conditions. The objective of this proof-of-concept study was to evaluate the feasibility of using the Multiphase Optimization Strategy (MOST) framework when assessing implementation strategies.</p><p><strong>Methods: </strong>We tested the feasibility of the MOST by employing a 2⁴ exploratory retrospective factorial design on existing data. We assessed the impact of the implementation strategies (calls, online meetings, arranged and walk-in on-site meetings) individually and in combination, on the number of DiGA activations in a non-randomized design. Data from <i>N</i> = 24,817 HCPs were analyzed using non-parametric tests.</p><p><strong>Results: </strong>The results primarily demonstrated the feasibility of applying the MOST to a non-randomized setting. Furthermore, analyses indicated significant differences between the groups of HCPs receiving specific implementation strategies [<i>χ²</i> (15) = 1,665.2, <i>p</i> < .001, <i>ɛ</i> ² = 0.07]. Combinations of implementation strategies were associated with significantly more DiGA activations. For example, combinations of arranged and walk-in on-site meetings showed higher activation numbers (e.g., <i>Z</i> = 10.60, <i>p</i> < 0.001, <i>χ²</i>  = 1,665.24) compared to those receiving other strategies. We found a moderate positive correlation between the number of strategies used and activation numbers (<i>r</i> = 0.30, <i>p</i> < 0.001).</p><p><strong>Discussion and limitations: </strong>These findings support the feasibility of using the MOST to evaluate implementation strategies in digital mental health care. It also gives an exploratory example on how to conduct factorial designs with information on implementation strategies. However, limitations such as non-random assignment, underpowered analysis, and varying approaches to HCPs affect the robustness and generalizability of the results. Despite these limitations, the results demonstrate that the MOST is a viable method for assessing implementation strategies, highlighting the importance of planning and optimizing strategies before their implementation. By addressing these limitations, healthcare providers and policymakers can enhance the adoption of digital health innovations, ultimately improving access to mental health care for a broader population.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1509415"},"PeriodicalIF":3.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143525434","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":"Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction.","authors":"Waqar A Sulaiman, Charithea Stylianides, Andria Nikolaou, Zinonas Antoniou, Ioannis Constantinou, Lakis Palazis, Anna Vavlitou, Theodoros Kyprianou, Efthyvoulos Kyriacou, Antonis Kakas, Marios S Pattichis, Andreas S Panayides, Constantinos S Pattichis","doi":"10.3389/fdgth.2024.1498939","DOIUrl":"10.3389/fdgth.2024.1498939","url":null,"abstract":"<p><p>This study aims to address the critical issue of emergency department (ED) overcrowding, which negatively affects patient outcomes, wait times, and resource efficiency. Accurate prediction of ED length of stay (LOS) can streamline operations and improve care delivery. We utilized the MIMIC IV-ED dataset, comprising over 400,000 patient records, to classify ED LOS into short (≤4.5 hours) and long (>4.5 hours) categories. Using machine learning models, including Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MLP), we identified GB as the best performing model outperforming the other models with an AUC of 0.730, accuracy of 69.93%, sensitivity of 88.20%, and specificity of 40.95% on the original dataset. In the balanced dataset, GB had an AUC of 0.729, accuracy of 68.86%, sensitivity of 75.39%, and specificity of 58.59%. To enhance interpretability, a novel rule extraction method for GB model was implemented using relevant important predictors, such as triage acuity, comorbidity scores, and arrival methods. By combining predictive analytics with interpretable rule-based methods, this research provides actionable insights for optimizing patient flow and resource allocation. The findings highlight the importance of transparency in machine learning applications for healthcare, paving the way for future improvements in model performance and clinical adoption.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"6 ","pages":"1498939"},"PeriodicalIF":3.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517368","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":"Predictors of smartphone addiction and its effect on quality of life: a cross-sectional study among the young adults in Bangladesh.","authors":"Zubair Ahmed Ratan, Anne-Maree Parrish, Mohammad Saud Alotaibi, Hassan Hosseinzadeh","doi":"10.3389/fdgth.2025.1351955","DOIUrl":"10.3389/fdgth.2025.1351955","url":null,"abstract":"<p><p>The enigma of smartphone addiction (SA) has plagued academics for the last decade, now scholars believed this behaviour might affect physical and mental wellbeing. SA has become a complex problem, yet to date, there is limited research investigating the predictors of SA and its effect on \"health-related quality of life (HRQoL)\". This study aimed to address this gap. The data was gathered from a convenience sample of 440 young adults completed between July 2021 and February 2022 through online survey in Bangladesh. On Logistic regression, after controlling for socio-demographic variables; friend support, process, social and compulsive usage were determined as significant predictors of SA. Those who were smartphone addicted were more presumably to have a lower quality of life. This study has significant implications for designing prevention pro-grams and policy development in relation to predictors of SA and its effect on HRQoL.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1351955"},"PeriodicalIF":3.2,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517289","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}