PLOS digital health最新文献

{"title":"Co-developing Georgia's National Digital Health Strategy through a multistakeholder convergence workshop: An implementation case study from the WHO European Region.","authors":"Nato Gzirishili, Mehar Grover, Resham Sethi, Mariam Beridze, Tomas Roubal, Lela Sulaberidze, Karapet Davtyan, Silviu Domente, Karthik Adapa","doi":"10.1371/journal.pdig.0001787","DOIUrl":"10.1371/journal.pdig.0001787","url":null,"abstract":"<p><p>National digital health strategies (DHS) are widely recognized as essential for advancing universal health coverage. However, published evidence on operational, multi-stakeholder approaches to co-development-particularly beyond the Asian and African settings where convergence methodology has previously been applied-remains limited. This study details the application of a convergence-based methodology to validate and refine the draft National DHS of Georgia, an upper-middle-income country, aligning it with European Union frameworks. An implementation case study was conducted in Georgia from June to September 2025. Central to this was the Multistakeholder Convergence Workshop, adapted from the Asian Development Bank Digital Health Convergence Meeting Toolkit and a four-phase blueprint methodology. Forty-seven participants from diverse sectors (public, private, academia, and development partners) collaborated in five thematic working groups. They reviewed the draft DHS, generating recommendations that were synthesized against the seven WHO-ITU eHealth strategy building blocks using hybrid deductive-inductive thematic analysis by two independent coders with third-party arbitration. The revised strategy comprises six strategic goals and 38 priority activities. Key technical recommendations included adopting a federated data governance model, utilizing the existing national identification number instead of a separate Health ID, and establishing a national Health Information Exchange. The strategy endorses FHIR for data exchange, LOINC for laboratory data, and the WHO-FIC (including ICD-11) complemented by SNOMED CT. Governance recommendations emphasized enacting a stand-alone Digital Health Act, forming a dedicated Digital Health Unit within the MoIDPLHSA, integrating digital health into professional education, and extending the strategy horizon to 2030 with a mid-2026 review checkpoint. Structured convergence methodologies, initially developed for Asian settings, can be effectively adapted for upper-middle-income WHO European Region countries pursuing European Health Data Space alignment. This approach produced technically specific outputs, reinforced national ownership, and supported Georgia's transition toward implementation, offering a highly transferable framework for similar contexts.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001787"},"PeriodicalIF":7.7,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186845","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":"Pathologies and technologies for digital therapeutics: An analysis of global clinical trials.","authors":"Paolo Rocco, Valentina Pagella, Umberto M Musazzi, Sara Manellari, Elena Paola Lanati, Paola Minghetti","doi":"10.1371/journal.pdig.0001750","DOIUrl":"10.1371/journal.pdig.0001750","url":null,"abstract":"<p><p>CDigital therapeutics (DTx) are software-based medical devices that deliver evidence-based therapeutic interventions directly to patients. While the clinical use of DTx is expanding rapidly, regulatory fragmentation and the absence of harmonized legal definitions hinder their assessment and reimbursement policies, as well as their integration into healthcare systems. There is currently a lack of comprehensive overviews of DTx clinical development, which are essential for informing policy, research and regulatory priorities. This paper provides an updated overview of randomised clinical trials involving DTx. 254 eligible trials on DTx, (defined according to ISO/TR 11147:2023) registered through January 2025 were identified through a search on ClinicalTrials.gov. Mobile applications represent the most prevalent technology (65%), followed by web-based tools and virtual reality (20%). Psychiatric disorders are the primary therapeutic target (52% of the studies) with insomnia, depression, and anxiety being the most addressed conditions. Trials were primarily conducted in North America (41%) and Europe (32%), where Germany, Spain, and Italy represent the most active countries. The analysis of clinical trials registrations on ClinicalTrials.gov reveals a heterogenous geographical distribution of DTx development but also a clear direction in the type of conditions for which they are mostly indicated, namely psychiatric and nervous system disorders. The fragmented policies on DTx reimbursement, even among EU countries, and the different capacity of governments to fund DTx research and development emerge as key factors, along with cultural and ethical issues, in determining the willingness of the industry to invest in these devices.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001750"},"PeriodicalIF":7.7,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186871","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":"A longitudinal analysis of real world symptom tracking and patient-reported outcomes in Brisa, a German Digital Health App for multiple sclerosis.","authors":"Ann-Sophie Stratil, Steffeni Papukchieva, Julia Steinmetz-Späh, Natalie Joschko, Lars Masanneck, Benjamin Friedrich, Tjalf Ziemssen","doi":"10.1371/journal.pdig.0001751","DOIUrl":"https://doi.org/10.1371/journal.pdig.0001751","url":null,"abstract":"<p><p>Brisa, a companion app designed for multiple sclerosis (MS) patients, can support the patient journey by collecting longitudinal real-world data. This study aimed to characterize the demographic and clinical profile of Brisa users and longitudinal trends in app engagement, self-reported symptoms and patient-reported outcomes (PROs). This retrospective analysis included Brisa users aged ≥18 years, reported use of category 1 (low), 2 (medium), or 3 (high efficacy) disease-modifying therapy (DMT) according to German DGN guidelines, and had no medication switching. Users diagnosed within 0-1 years were classified as newly diagnosed cases. App engagement was defined as any screen activity; disengagement was defined as >180 consecutive days without engagement and analysed using Cox proportional hazards regression. Symptoms were rated on a 1-5 emoticon-based scale (1 = poor, 5 = excellent). PROs included the MFIS, PDDS, PDQ-5, IVIS, and PES. Among 4,194 users, the majority were female (80.8%), aged 26-35 years (30.5%) and diagnosed with relapsing-remitting MS (77.4%), with 27.9% newly diagnosed. Consistent with a shift towards high-efficacy treatment, use of category 3 (high-efficacy) DMTs among newly diagnosed users significantly increased from 2023 to 2024/2025 (χ2, p < 0.0001). In engagement analyses, users receiving category 3 DMTs, those aged ≥36 years, and newly diagnosed users showed a significantly lower risk of disengagement compared with users on category 1 DMTs, aged 18-25 years or diagnosed ≥2 years prior. Overall, about 76.0% of users tracked at least one symptom or PRO. Longitudinally, among users with ≥1 year of tracking, symptom and PRO scores remained largely stable. A shift toward higher efficacy treatment in newly diagnosed users was observed, while symptom and PRO scores remained largely stable over time. These findings highlight the potential of digital health tools for tracking treatment patterns, patient engagement and self-reported symptom changes in MS.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001751"},"PeriodicalIF":7.7,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186731","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":"A machine learning model for in-hospital mortality prediction in patients with sepsis-related myocardial injury.","authors":"Da-Cheng Wang, Yan-Pin Liu, Yu-Ting Wang, Xiao-Huan Zhuang, Xiao-Peng Yin, Xin-Yuan Zhang, Yan Zhuang","doi":"10.1371/journal.pdig.0001758","DOIUrl":"10.1371/journal.pdig.0001758","url":null,"abstract":"<p><p>Sepsis-related myocardial injury (SRMI) is associated with high mortality, but the transportability of prediction models across heterogeneous cohorts remains uncertain. We developed and externally validated a machine learning model for in-hospital mortality prediction in SRMI and evaluated its discrimination, calibration, interpretability, and distributional shift across cohorts. Using MIMIC-IV for development and MIMIC-III and a local cohort for external validation (n = 1,226, 962, and 280, respectively), we compared eight classification algorithms. A two-stage feature-selection strategy, recursive feature accumulation and sequential elimination with incremental feature evaluation, ranked 31 candidate variables to derive a parsimonious predictor set. The selected model was evaluated for discrimination, precision-recall performance, calibration, clinical utility, and risk stratification. Logistic recalibration, population stability index (PSI) analysis, and SHapley Additive exPlanations (SHAP) were used to assess probability alignment, cross-cohort distributional shift, and model interpretability. Feature selection reduced 31 candidate variables to 8 predictors with little loss of cross-validated performance (AUC, 0.819 with 31 variables versus 0.810 with 8 variables). The selected random forest model achieved an AUC of 0.819 (bootstrap 95% CI, 0.787-0.848) and a PR-AUC of 0.702 (0.657-0.751) in MIMIC-III, and an AUC of 0.758 (0.693-0.816) and a PR-AUC of 0.525 (0.435-0.630) in the local cohort. Recalibration reduced the Brier score from 0.162 to 0.147 in MIMIC-III and from 0.233 to 0.196 in the local cohort. PSI indicated minimal distributional shift between MIMIC-IV and MIMIC-III but substantial shift in the local cohort, particularly for SOFA score, respiratory rate, and prothrombin time. SHAP identified prothrombin time, invasive ventilation, lactate, temperature, and urine output as major contributors to model predictions. In two external cohorts, an 8-variable random forest model showed moderate discrimination for mortality prediction in SRMI. The evaluation of external performance, calibration, distributional shift, and SHAP-based explanations provides an assessment of model transportability. Prospective validation is needed before clinical deployment.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001758"},"PeriodicalIF":7.7,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13657301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172218","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 complexities and contradictions of online eating disorder content: A qualitative evidence synthesis of young people's views and experiences.","authors":"Meena Khatwa, Rebecca Rees, Claire Stansfield, Vidhi Bassi, Ceri-Nicole Wilkins, Kelly Dickson","doi":"10.1371/journal.pdig.0001066","DOIUrl":"10.1371/journal.pdig.0001066","url":null,"abstract":"<p><p>Eating disorders (ED) continue to be a concern worldwide. There is a growing focus on problematic discourse around ideal bodies as well as on how exposure to related content and spaces online might influence disordered eating and other ED symptoms. A qualitative evidence synthesis using thematic synthesis methods was conducted to explore young people's lived experiences of online ED content. Studies were included if they provided, from 2007 onwards, views about online content from young people from OECD countries aged 11-25 years old who identified as having had an eating disorder, disordered eating or eating difficulties. This article focuses on findings from 13 included studies whereby three key themes were identified: Comparing - how online content was used to evaluate others and oneself; Curating - how young people manage this content; and Community - spaces for connection over shared experience. Participants across the studies described the complex 'double-edged' nature of online spaces, which can offer inclusion but can also be experienced as harmful. The synthesis outlined a lack of supportive offline mental health-promoting spaces as part of the context of young people's online content use.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001066"},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149278","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":"Retrieval-to-abstraction LLM-assisted literature synthesis: Case demonstrations in digital health technology-enabled randomized controlled trial outcome reporting.","authors":"Taylor B Harrison, Dian Hu, Heling Jia, Shyang Hong Tan, Fang Chen, Zhongyu Zhang, Qiuhao Lu, Jinlian Wang, Liwei Wang, Ming Huang, Larry J Prokop, Matt Hoy, Jennifer St Sauver, Sunyang Fu, Hongfang Liu","doi":"10.1371/journal.pdig.0001768","DOIUrl":"10.1371/journal.pdig.0001768","url":null,"abstract":"<p><p>Systematic reviews of randomized controlled trials (RCTs) support evidence-based clinical advancement. Clinical trial reporting guidelines, including CONSORT, establish common data elements that support systematic synthesis of published trial outcomes. However, the rapid rise of digital health technologies presents new challenges for reporting guidelines, and substantial real-world reporting heterogeneity has been observed. Therefore, machine-assisted evaluation of reporting adherence offers a pathway for more agile information management to accommodate the diverse and disruptive nature of digital health technology-enabled RCTs. Here, we propose an accelerated retrieval-to-abstraction literature synthesis framework to conduct article screening, text and table data preprocessing, and full-text review using an unmodified pretrained large language model. We apply this methodology in two digital health technology-enabled RCT case studies evaluating adherence to technology and digital health equity guideline reporting items. Large language model performance was on par with inter-annotator agreement F1 scores (≥0.9) in both screening and information-extraction tasks. These findings support the scalability and rigor of accelerated evidence synthesis and demonstrate the importance of common reporting elements across digital health technology-enabled RCTs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001768"},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149659","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":"Performance of large language models in the Chinese National Medical Licensing Examinations and Beyond: Scoping review.","authors":"Hui Zong, Jiao Wang, Jiaxue Cha, Rongrong Wu, Shanshan Hu, Yan Zhao, Bairong Shen","doi":"10.1371/journal.pdig.0001773","DOIUrl":"10.1371/journal.pdig.0001773","url":null,"abstract":"<p><p>Large language models (LLMs) such as GPT-4 and DeepSeek have shown potential in medical education and assessment. In China, the Chinese National Medical Licensing Examination (CNMLE) has become a critical benchmark for evaluating LLMs' professional knowledge and reasoning ability in non-English contexts. This scoping review aims to synthesize research evaluating LLM performance on the CNMLE and related Chinese medical examinations, identifying performance trends, methodological gaps, and future directions. A scoping review was conducted in accordance with PRISMA-ScR guidelines. PubMed and Web of Science were searched on June 12, 2025, using terms related to LLMs and Chinese medical examinations. Studies were included if they evaluated any LLM on national-level Chinese medical exams and reported performance metrics. Two reviewers screened and extracted data. Study quality was assessed using a 12-item checklist covering dataset characteristics, LLM setup, and evaluation methods. Fisher's exact test was used to assess differences in the passing rates of different LLMs. 14 studies were included, covering 51 evaluation records across 8 types of Chinese medical examinations, including CNMLE and specialty exams such as critical care, radiation oncology, and ultrasound medicine. Exam years ranged from 2017 to 2024, with a shift toward using recent exams. A total of 9 LLMs were evaluated, including GPT-3.5, GPT-4, GPT-4o, ERNIE, DeepSeek-R1, Qwen-72B, Baichuan2-7B, Baichuan2-13B and DISC-MedLLM. GPT-4o and DeepSeek-R1 achieved the highest scores of 552 (92.00%) and 523 (87.20%) in the 2024 CNMLE, respectively, both surpassing the passing threshold. In an exploratory comparison, GPT-4 achieved a higher pass rate than GPT-3.5 with a statistically significant difference, although inconsistencies in datasets, model transparency, and evaluation criteria persist across the included studies. LLMs show improving performance on Chinese medical exams, especially newer models. Nevertheless, future research should prioritize standardized benchmarks, multimodal capabilities, and transparent evaluation to ensure meaningful clinical relevance and educational value.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001773"},"PeriodicalIF":7.7,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148893","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":"Examining public acceptance of AI versus human-centric dementia care across NHS England's dementia pathway stages.","authors":"Thomas O'Fee, Santosh Vijaykumar, Michael Craig","doi":"10.1371/journal.pdig.0001088","DOIUrl":"10.1371/journal.pdig.0001088","url":null,"abstract":"<p><p>There are over five-hundred-thousand individuals with recorded dementia diagnoses in England, highlighting an urgent need for scalable and effective care solutions to ease pressure on health and social care systems. Artificial Intelligence (AI)-enabled smart home systems are emerging as promising digital health innovations, offering cognitive support, real-time monitoring, and decision-making assistance. However, concerns around trust, user agency, and misconceptions about AI continue to limit acceptance and may hinder adoption. This study examined how stage of dementia care, system design, and level of system involvement shape public attitudes towards AI-driven care technologies. A repeated-measures design was employed, using vignettes that varied across three dimensions: stage of care (aligned with NHS England's Dementia Well Pathway), system centrism (AI-based versus human-based), and level of involvement (moderate support versus full control). Participants aged 55-64 years - a group at elevated risk of developing dementia or encountering these technologies within the next decade - rated each scenario on both acceptability and perceived likelihood of actualisation/plausibility. Findings showed that acceptability was sensitive to both care stage and system type. A significant interaction indicated that full human involvement was consistently rated as more acceptable, while AI involvement was viewed more favourably only at moderate levels. This interaction effect intensified across the dementia care pathway, with the largest discrepancies observed in later stages such as Living well and Dying well, where full-control AI was rated least acceptable. In terms of perceived likelihood, scenarios were judged more likely under conditions of moderate involvement, with human-centric scenarios rated as more likely than AI-centric ones, particularly in later stages. These results highlight the importance of trust, autonomy, and public understanding in AI adoption. Acceptance was highest when AI was positioned to augment rather than replace human input, supporting hybrid models that preserve agency while enhancing scalability.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001088"},"PeriodicalIF":7.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13648964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139820","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":"Analysis and mitigation of equipment-induced shortcuts in AI models for laparoscopic cholecystectomy.","authors":"Sergey Protserov, Anastasiia Repalo, Pouria Mashouri, Jaryd Hunter, Caterina Masino, Amin Madani, Michael Brudno","doi":"10.1371/journal.pdig.0001421","DOIUrl":"10.1371/journal.pdig.0001421","url":null,"abstract":"<p><p>Machine learning models have seen a lot of success in medical image segmentation domain. However, one of the challenges that they face are confounders or shortcuts: spurious correlations or biases in the training data that affect the resulting models. One example of such confounders for surgical machine learning is the setup of surgical equipment, including tools and lighting. Using the task of identification of safe and dangerous zones of dissection in laparoscopic cholecystectomy images and videos as a use-case, we inspect two equipment-induced biases: the location of surgical tools in the field of view and the direction of lighting, both of which are tightly related to the region of interest. We propose methods for evaluating the severity of these biases based on measurements of model consistency under real or simulated exposure to these shortcuts, and augmentation-based methods for mitigating them. We show that our tool bias mitigations based on pasting tool images in random locations improve the models' prediction consistency under tool movements by 9 percentage points in the most inconsistent cases, and by 4 percentage points on average. Our lighting bias mitigations based on simulated lighting adjustments help reduce fraction of pixels originally predicted as belonging to the dangerous zone that may flip to safe under light changes from 5% to 1.5%, without compromising segmentation quality.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001421"},"PeriodicalIF":7.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13649130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139838","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":"Perceived value but persistent barriers: A qualitative study of healthcare worker experiences with the Impilo electronic health record system in rural Zimbabwe.","authors":"Shalom Rudorwashe Dube, Perez Livias Moyo, Methembe Yotamu Khozah","doi":"10.1371/journal.pdig.0001765","DOIUrl":"10.1371/journal.pdig.0001765","url":null,"abstract":"<p><p>Electronic Health Records (EHRs) are pivotal for improving healthcare data quality and service delivery. Despite the introduction of the Impilo EHR system at St Anne's Mission Hospital in Brunapeg, utilisation remains low. This study aimed to explore healthcare workers' (HCWs) experiences and identify the key barriers and facilitators to Impilo's utilisation. A descriptive qualitative study was conducted at St Anne's Mission Hospital. In-depth interviews were conducted with 20 HCWs authorised to use Impilo, selected via purposive sampling until thematic saturation was achieved. Thematic analysis, following the Braun and Clarke approach, was used to analyse the data. Trustworthiness was ensured through multi-coder analysis, the maintenance of an audit trail, and thick descriptive reporting. HCWs reported positive experiences related to ease of data access, improved communication, and better inventory management. However, significant barriers undermine utilisation. The analysis revealed three key themes: (1) Benefits of Impilo, (2) Multifaceted Challenges, and (3) Suggested Improvements. Challenges included pervasive technical and infrastructural issues (e.g., frequent network downtime, lack of offline functionality), human resource constraints leading to dual documentation and increased workload, and subsequent compromised data quality due to inconsistent entry. The underutilisation of the Impilo system is not due to a lack of perceived value but is primarily driven by remediable barriers related to infrastructure, training, and workflow design issues. For successful implementation, interventions must be multifaceted, addressing technical reliability (e.g., offline functionality), human capacity (continuous training and support), and organisational processes (dedicated data clerks and inclusive system redesign). This study provides a framework for improving EHR adoption in similar low-resource rural settings.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"5 10","pages":"e0001765"},"PeriodicalIF":7.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13648981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139212","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}