Frontiers in digital health最新文献

{"title":"A modified UTAUT model for acceptance to use telemedicine services and its predictors among healthcare professionals at public hospitals in North Shewa Zone of Oromia Regional State, Ethiopia.","authors":"Debela Tsegaye Hailu, Mequannint Sharew Melaku, Solomon Abuhay Abebe, Agmasie Damtew Walle, Kefyalew Naniye Tilahun, Kassahun Dessie Gashu","doi":"10.3389/fdgth.2025.1469365","DOIUrl":"10.3389/fdgth.2025.1469365","url":null,"abstract":"<p><strong>Introduction: </strong>The shortage of healthcare professionals, long waiting time for treatment, inadequate transportation, and hard-to-reach geographical locations remained challenging in the healthcare service delivery in resource-limited settings. To overcome these challenges, healthcare providers are looking to use telemedicine technologies as an alternative solution. However, user resistance has consistently been identified as a major obstacle to the successful implementation of telemedicine. Thus, this study aimed to assess acceptance to use telemedicine services and its predictors among healthcare professionals at public hospitals in the North Shewa Zone of Oromia Regional State, Ethiopia.</p><p><strong>Method: </strong>A cross-sectional study design was employed among a total of 627 healthcare professionals working at public hospitals in the North Shewa Zone from 3 April to 1 May 2023. The study participants were selected using simple random sampling techniques. A questionnaire, which is adapted from the original instrument developed by Venkatesh et al.'s study and several studies regarding the UTAUT model was used. Data were collected using a self-administered structured questionnaire in English version. The descriptive statistics were estimated using the SPSS version 25, and structural equation modeling analysis was employed using AMOS V.21 software.</p><p><strong>Results: </strong>In this study, 601 (95.85% response rate) study subjects participated. The study has shown that 315 (52.4%) (95% CI: 48.3-56.5) of the participants accepted to use telemedicine in their routine healthcare services. Performance expectancy (<i>β</i> = 0.184, <i>p</i> = 0.001), effort expectancy (<i>β</i> = 0.183, <i>p</i> < 0.001), facilitating conditions (<i>β</i> = 0.249, <i>p</i> < 0.001), and digital literacy (<i>β</i> = 0.403, <i>p</i> < 0.001) had a significant positive effect on the acceptance to use telemedicine services. Age was used to moderate facilitating conditions (<i>β</i> = 0.400, <i>p</i> < 0.001) and digital literacy (<i>β</i> = 0.598, <i>p</i> < 0.001) in relation to acceptance to use telemedicine services.</p><p><strong>Conclusion: </strong>The healthcare professionals' acceptance to use the offered telemedicine services was promising for the future. Additionally, our research found significant effects between healthcare professionals' acceptance to use telemedicine services with the predictors except social influence. Facilitating conditions and digital literacy with acceptance to use were moderated by age. Thus, the health facility should strengthen its telemedicine technology by raising awareness of its usefulness and ease of use.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1469365"},"PeriodicalIF":3.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12171827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318808","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":"Continuous monitoring of critically ill patients using photoplethysmography-the road to a less invasive ICU monitoring.","authors":"João Rosinhas, Rui Malheiro, João Tiago Pimenta, Ricardo Sá, Francisco Serdoura, José-Artur Paiva","doi":"10.3389/fdgth.2025.1605020","DOIUrl":"10.3389/fdgth.2025.1605020","url":null,"abstract":"<p><strong>Introduction: </strong>Intensive Care Medicine is based on continuous timely monitoring of physiological variables to guide modulation of therapy. This monitoring is often invasive, but there is a trend for the adoption of non-invasive devices, already largely used in wards and homecare, to reduce risk of device-associated side effects. The aim of this study was to assess the accuracy of a non-invasive equipment (Corsano Cardiowatch 287-2B) in the assessment of blood pressure, heart rate, temperature and oxygen saturation in critically ill patients admitted to the ICU.</p><p><strong>Method: </strong>This prospective cohort study developed in an adult ICU admitting patients for level 3 and 2 of care compared the Corsano Cardiowatch 287-2B with the ICU standard monitoring, namely continuous electrocardiogram, invasive arterial blood pressure through arterial catheter, pulse oximeter and central thermometer. Concordance was assessed using the Bland-Altman test.</p><p><strong>Results: </strong>Nineteen patients were included in the study. The number of time-points included for comparison between the two monitoring strategies were more than 50,000 in pulse and heart rate, around 40,000 in oxygen saturation and body temperature and 1,200 in systolic and diastolic blood pressure. Bias for heart rate and pulse were -1.73 and -0.77, respectively. The limits of agreement were between -14.90 and 11.33, for heart rate, and -14.25 and 12.71, for pulse. Small biases were also estimated for oxygen saturation (0.21), with limits of agreement between -6.97 and 7.39, and body temperature (0.58), with limits between -1.12 and 2.47. Concordance was low for diastolic and systolic blood pressure, with bias of 5.18 and -11.27, respectively.</p><p><strong>Conclusions: </strong>Corsano Cardiowatch 287-2B reaches good levels of concordance compared to traditional ICU monitoring for heart and pulse rates and may be a valuable solution for their less invasive monitoring, with promising results for future operationalization for oxygen saturation and body temperature. Concordance is low for blood pressure, meaning the device is currently unsuitable for use with that purpose.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1605020"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162652/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting benzodiazepine use through induced eye convergence inability with a smartphone app: a proof-of-concept study.","authors":"Kiki W K Kuijpers, Markku D Hämäläinen, Andreas Zetterström, Maria Winkvist, Marieke Niesters, Monique van Velzen, Fred Nyberg, Albert Dahan, Karl Andersson","doi":"10.3389/fdgth.2025.1584716","DOIUrl":"10.3389/fdgth.2025.1584716","url":null,"abstract":"<p><strong>Background: </strong>Benzodiazepines (BZDs) are readily available potent drugs that act as central depressants. These drugs are widely used, misused, and abused. For patients with BZD use disorder, the traditional sobriety monitoring method is periodic urine tests.</p><p><strong>Methods: </strong>The utility of eye-scanning data related to non-convergence (the ability to cross eyes) collected using smartphones with the Previct Drugs app before and after ingestion of the BZD lorazepam for detecting BZD-driven effects was evaluated using data from 12 individuals from a historic clinical study (NCT05731999). Using a novel metric that represents the change in distance between irises when converging eyes, either in absolute terms (NCdiff) or individualized (NCdiffInd), classifiers were built using logistic regression.</p><p><strong>Results: </strong>The ability to converge eyes is a strongly individual and acquired skill that is impaired after ingesting lorazepam. The maximum NCdiff for a BZD-sober individual may be smaller than the impaired NCdiff for another individual. Using the NCdiff measured in a sober condition after approximately 1 week of regular eye-scanning as the individual baseline to form NCdiffInd produced a highly functional classifier with an area under the curve (AUC) = 0.88, which was superior to a classifier based on NCdiff with an AUC = 0.79.</p><p><strong>Conclusions: </strong>The loss of eye convergence induced by lorazepam is continuous, individual, and can be partial. Smartphone-based eye-scanning technology combined with a classifier adapted to the ability of eye convergence of individuals shows promising performance in detecting ingestion of lorazepam.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1584716"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303828","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":"The evolution of digital health: a global, Latin American, and Brazilian bibliometric analysis.","authors":"Maria Eulália Vinadé Chagas, Gabriela de Oliveira Laguna Silva, Gabriel Ricardo Fernandes, Gabriela Tizianel Aguilar, Mariana Motta Dias da Silva, Evandro Luis Moraes, Isadora D Avila Lottici, Jerusa da Rosa de Amorim, Tiago de Abreu, Taís de Campos Moreira, Felipe Cezar Cabral","doi":"10.3389/fdgth.2025.1582719","DOIUrl":"10.3389/fdgth.2025.1582719","url":null,"abstract":"<p><strong>Introduction: </strong>Digital health provides remote healthcare assistance, contributing to reducing inequalities in access to services. For its widespread adoption, it is essential to disseminate successful models implemented in countries with developed digital health networks, so that they can be adapted and replicated in developing regions. The dissemination of scientific studies on the topic, combining digital health activities within various contexts with scientific research, is crucial for promoting significant advancements in the understanding and application of these technologies. This study aims to conduct a comprehensive bibliometric analysis of global scientific production in digital health from 2019 to 2024, with special attention to Latin America and Brazil.</p><p><strong>Methods: </strong>A bibliometric analysis was conducted with searches in PubMed, Scopus, and Web of Science. The analysis used the Bibliometrix package in RStudio, and the data were filtered for the global dimension, Latin American countries, and Brazil. The authorship analysis was restricted to publications with at least one Brazilian author and was carried out through a manual check of each record. The protocol was registered on the Open Science Framework platform under the number 10.17605/OSF.IO/43WQ5.</p><p><strong>Results: </strong>A total of 51,723 publications were included in the global dimension, 2,410 in Latin America, and 1,317 in the Brazilian analysis. The number of publications increased from 2019 to 2021. In the global scenario, the United States led scientific production in digital health, whereas Brazil led in Latin America.</p><p><strong>Conclusion: </strong>Digital health has expanded exponentially, consolidating itself as a strategic pillar in healthcare systems. Investments in international collaborations that encourage knowledge exchange, strengthen research networks, and drive scientific publications are essential. These partnerships are crucial for adapting digital tools to different socioeconomic contexts and ensuring equitable care for the population.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1582719"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303832","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":"Greening healthcare and slashing carbon emissions through telemedicine: a cross-sectional study from over 50 thousand remote consults at a leading tertiary hospital.","authors":"Roberto Nunes Umpierre, Rita Mattiello, Carlos André Aita Schmitz, Enrique Falceto de Barros, Rodolfo Souza da Silva, Marcelo Rodrigues Gonçalves, José Roberto Goldim","doi":"10.3389/fdgth.2025.1497770","DOIUrl":"10.3389/fdgth.2025.1497770","url":null,"abstract":"<p><strong>Introduction: </strong>Minimizing healthcare systems' resource footprints is crucial. To expand this focus, our objective was to assess the carbon emission reductions achievable through the introduction of telemedicine services at a prominent Brazilian tertiary hospital.</p><p><strong>Methods: </strong>This cross-section study included all patients who had remotely held appointments in a Brazilian tertiary hospital. The primary outcome was carbon emissions. The estimated carbon emissions were first calculated based on the distance between the hospital and the patient's home address. After, the calculated distance was multiplied by the amount of carbon estimated according to the type of transport used.</p><p><strong>Results: </strong>The study included 28,244 patients undergoing 52,878 remote appointments between March and December 2020, residing in 417 municipalities in Rio Grande do Sul and 80 towns in other Brazilian states. The total sum of distances and carbon gas reduction saved with the implementation of remote consultations amounted to 805,252.00 km and 939,641.94 kg of CO₂ emissions, respectively.</p><p><strong>Discussion: </strong>Telemedicine initiatives implemented in tertiary hospitals for less than a year result in a large amount of greenhouse gas emissions saved. Telemedicine emerges as a promising strategy with significant potential to mitigate the impact on planetary health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1497770"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303829","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":"Machine learning and explainable artificial intelligence to predict and interpret lead toxicity in pregnant women and unborn baby.","authors":"Priyanka Chaurasia, Pratheepan Yogarajah, Abbas Ali Mahdi, Sally McClean, Mohammad Kaleem Ahmad, Tabrez Jafar, Sanjay Kumar Singh","doi":"10.3389/fdgth.2025.1608949","DOIUrl":"10.3389/fdgth.2025.1608949","url":null,"abstract":"<p><strong>Introduction: </strong>Lead toxicity is a well-recognised environmental health issue, with prenatal exposure posing significant risks to infants. One major pathway of exposure to infants is maternal lead transfer during pregnancy. Therefore, accurately characterising maternal lead levels is critical for enabling targeted and personalised healthcare interventions. Current detection methods for lead poisoning are based on laboratory blood tests, which are not feasible for the screening of a wide population due to cost, accessibility, and logistical constraints. To address this limitation, our previous research proposed a novel machine learning (ML)-based model that predicts lead exposure levels in pregnant women using sociodemographic data alone. However, for such predictive models to gain broader acceptance, especially in clinical and public health settings, transparency and interpretability are essential.</p><p><strong>Methods: </strong>Understanding the reasoning behind the predictions of the model is crucial to building trust and facilitating informed decision-making. In this study, we present the first application of an explainable artificial intelligence (XAI) framework to interpret predictions made by our ML-based lead exposure model.</p><p><strong>Results: </strong>Using a dataset of 200 blood samples and 12 sociodemographic features, a Random Forest classifier was trained, achieving an accuracy of 84.52%.</p><p><strong>Discussion: </strong>We applied two widely used XAI methods, SHAP (SHapley additive explanations) and LIME (Local Interpretable Model-Agnostic Explanations), to provide insight into how each input feature contributed to the model's predictions.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1608949"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303830","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":"Synthetic4Health: generating annotated synthetic clinical letters.","authors":"Libo Ren, Samuel Belkadi, Lifeng Han, Warren Del-Pinto, Goran Nenadic","doi":"10.3389/fdgth.2025.1497130","DOIUrl":"10.3389/fdgth.2025.1497130","url":null,"abstract":"<p><p>Clinical letters contain sensitive information, limiting their use in model training, medical research, and education. This study aims to generate reliable, diverse, and de-identified synthetic clinical letters to support these tasks. We investigated multiple pre-trained language models for text masking and generation, focusing on Bio_ClinicalBERT, and applied different masking strategies. Evaluation included qualitative and quantitative assessments, downstream named entity recognition (NER) tasks, and clinically focused evaluations using BioGPT and GPT-3.5-turbo. The experiments show: (1) encoder-only models perform better than encoder-decoder models; (2) models trained on general corpora perform comparably to clinical-domain models if clinical entities are preserved; (3) preserving clinical entities and document structure aligns with the task objectives; (4) Masking strategies have a noticeable impact on the quality of synthetic clinical letters: masking stopwords has a positive impact, while masking nouns or verbs has a negative effect; (5) The BERTScore should be the primary quantitative evaluation metric, with other metrics serving as supplementary references; (6) Contextual information has only a limited effect on the models' understanding, suggesting that synthetic letters can effectively substitute real ones in downstream NER tasks; (7) Although the model occasionally generates hallucinated content, it appears to have little effect on overall clinical performance. Unlike previous research, which primarily focuses on reconstructing original letters by training language models, this paper provides a foundational framework for generating diverse, de-identified clinical letters. It offers a direction for utilizing the model to process real-world clinical letters, thereby helping to expand datasets in the clinical domain. Our codes and trained models are available at https://github.com/HECTA-UoM/Synthetic4Health.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1497130"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12163008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303831","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":"Artificial intelligence in clinical decision support and the prediction of adverse events.","authors":"S P Oei, T H G F Bakkes, M Mischi, R A Bouwman, R J G van Sloun, S Turco","doi":"10.3389/fdgth.2025.1403047","DOIUrl":"10.3389/fdgth.2025.1403047","url":null,"abstract":"<p><p>This review focuses on integrating artificial intelligence (AI) into healthcare, particularly for predicting adverse events, which holds potential in clinical decision support (CDS) but also presents significant challenges. Biases in data acquisition, such as population shifts and data scarcity, threaten the generalizability of AI-based CDS algorithms across different healthcare centers. Techniques like resampling and data augmentation are crucial for addressing biases, along with external validation to mitigate population bias. Moreover, biases can emerge during AI training, leading to underfitting or overfitting, necessitating regularization techniques for balancing model complexity and generalizability. The lack of interpretability in AI models poses trust and transparency issues, advocating for transparent algorithms and requiring rigorous testing on specific hospital populations before implementation. Additionally, emphasizing human judgment alongside AI integration is essential to mitigate the risks of deskilling healthcare practitioners. Ongoing evaluation processes and adjustments to regulatory frameworks are crucial for ensuring the ethical, safe, and effective use of AI in CDS, highlighting the need for meticulous attention to data quality, preprocessing, model training, interpretability, and ethical considerations.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1403047"},"PeriodicalIF":3.2,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12162700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303805","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":"\"Voice is the New Blood\": a discourse analysis of voice AI health-tech start-up websites.","authors":"Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Jean-Christophe Bélisle-Pipon","doi":"10.3389/fdgth.2025.1568159","DOIUrl":"10.3389/fdgth.2025.1568159","url":null,"abstract":"<p><strong>Introduction: </strong>Voice as a biomarker has emerged as a transformative field in health technology, providing non-invasive, accessible, and cost-effective methods for detecting, diagnosing, and monitoring various conditions. Start-ups are at the forefront of this innovative field, developing and marketing clinical voice AI solutions to a range of healthcare actors and shaping the field's early development. However, there is limited understanding of how start-ups in this field frame their innovations, and address-or overlook-critical socio-ethical, technical, and regulatory challenges in the rapidly evolving field of digital health.</p><p><strong>Methods: </strong>This study uses discourse analysis to examine the language on the public websites of 25 voice AI health-tech start-ups. Grounded in constitutive discourse analysis, which asserts that discourse both reflects and shapes realities, the study identifies patterns in how these companies describe their identities, technologies, and datasets.</p><p><strong>Results: </strong>The analysis shows start-ups consistently highlight the efficacy, reliability, and safety of their technologies, positioning them as transformative healthcare solutions. However, descriptions of voice datasets used to train algorithms vary widely and are often absent, reflecting broader gaps in acoustic and ethical standards for voice data collection and insufficient incentives for start-ups to disclose key data details.</p><p><strong>Discussion: </strong>Start-ups play a crucial role in the research, development, and marketization of voice AI health-tech, prefacing the integration of this new technology into healthcare systems. By publicizing discourse around voice AI technologies at this early stage, start-ups are shaping public perceptions, setting expectations for end-users, and ultimately influencing the implementation of voice AI technologies in healthcare. Their discourse seems to strategically present voice AI health-tech as legitimate by using promissory language typical in the digital health field and showcase the distinctiveness from competitors. This analysis highlights how this double impetus often drives narratives that prioritize innovation over transparency. We conclude that the lack of incentive to share key information about datasets is due to contextual factors that start-ups cannot control, mainly the absence of clear standards and regulatory guidelines for voice data collection. Addressing these complexities is essential to building trust and ensuring responsible integration of voice AI into healthcare systems.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1568159"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158947/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287345","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":"Exploring user experiences of clinicians engaged with the digital healthcare interventions across the referral and university teaching hospitals in Nigeria: a qualitative study.","authors":"Uchechukwu Solomon Onyeabor, Okechukwu Onwuasoigwe, Wilfred Okwudili Okenwa, Thorsten Schaaf, Niels Pinkwart, Felix Balzer","doi":"10.3389/fdgth.2025.1488880","DOIUrl":"10.3389/fdgth.2025.1488880","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Given that Nigeria and several developing countries are still at the early stage of digital healthcare interventions adoption (like the use of electronic health records systems) there is scarcity of research/empirical reports investigating the overall user experiences of clinicians, the doctors and the nurses who are or who had been practically engaged with the use of these new digital healthcare support implementations that had engendered new culture across their care delivery facilities. The referral and university teaching hospitals in Nigeria numbering over 166 and scattered across over 37 states of the federation and the Federal Capital Territory (FCT) make up a strategic component of Nigeria's healthcare ecosystem. This research was therefore designed and restricted to clinicians who had used these systems so as to explore their experiences with these systems and possibly unveil any challenges/limitations that can bedevil successful and sustainable acquisition of digital healthcare intervention programmes and projects across referral and university teaching hospitals in the Southeastern Region of Nigeria; and could also hamper any future implementations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method: &lt;/strong&gt;This study was designed in a manner that allows the clinicians the liberty to conveniently express in writing (via comments) issues, challenges and concerns that they had perceived, encountered or experienced bedevilling electronic health record adoption and use across their care facilities. So a structured interview method was chosen by the research team (after due considerations) as fitting the research context. This (structured interview) was therefore designed and targeted at about 400 clinical participants, including the doctors and the nurses from three select referral and university teaching hospitals in the Southeastern Region of Nigeria (a federal, state, and national specialist referral hospital).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;Out of the 400 clinicians who were targeted in the survey, 326 of them practically responded to the interview questions. The outcome showed the clinicians willingly exposing several issues and challenges that had stifled electronic health record adoption across the hospitals. Issues identified were categorized into themes including challenges bordering on lack of political will on the part of hospital administration; lack of computer/digital/EHR literacy; poor and often lack of comprehensive training on the workings of EHR; poor maintenance culture; poor EHR system design, poor implementation and use-based struggles and challenges; infrastructure issues, system breakdown and network challenges etc were reported.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The outcome of this investigation has profoundly exposed practical issues that had hitherto stifled and often suffocated electronic health record implementation projects across referral and university teaching hospitals in Nigeria. And given the strat","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1488880"},"PeriodicalIF":3.2,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12158922/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287347","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}