International Journal of Medical Informatics最新文献

{"title":"Automatic (near-) duplicate content document detection in a cancer registry","authors":"Tapio Niemi,&nbsp;Jean Pierre Ghobril,&nbsp;Gautier Defossez,&nbsp;Simon Germann,&nbsp;Eloïse Martin,&nbsp;Jean-Luc Bulliard","doi":"10.1016/j.ijmedinf.2025.105799","DOIUrl":"10.1016/j.ijmedinf.2025.105799","url":null,"abstract":"<div><h3>Background</h3><div>Duplicate and near-duplicate medical documents are problematic in document management, clinical use, and medical research. In this study, we focus on multisourced medical documents in the context of a population-based cancer registry in Switzerland. Although the data collection process is well-regulated, the volume of transmitted documents steadily increases and the presence of full or near-duplicates slows down and complicates document processing. Identifying near-duplicates is particularly challenging because the large number of documents makes pairwise comparison non-feasible.</div></div><div><h3>Methods</h3><div>We implemented a system based on both normal hash functions, Simhash (Locality Sensitive Hashing), and Smith-Waterman text alignment similarity. Simhash offers good performance and confirming its results by the Smith-Waterman algorithm with a selected similarity threshold reduces the false positive rate to near zero without lowering sensitivity. Extracted differences in near-duplicate content documents are shown by highlighting differences in original PDF documents.</div><div>We validated the method using 3042 manually verified document pairs containing 1252 full-duplicate and 398 near-duplicate pairs. The area under the curve (AUC) was 0.96, sensitivity 0.92, specificity 1.00, PPV 1.00, and NPV 0.91. For the same size simulated data, corresponding values were 0.86, 0.72, 1.00, 1.00, and 0.77, respectively.</div></div><div><h3>Results</h3><div>We applied the method against 224,398 medical documents in the cancer registry. We found 5.5% of duplicates on the text level, and 0.17–0.24% near-duplicates depending on the used parameters and threshold values. Most near-duplicates related to the same patient and originated from the same transmitter. Manual evaluation showed that only 2% of differences were in medical contents and 83% in administrative data (21% in patient, 11% in doctor, and 51% in other administrative data). Many near-duplicates looked strikingly similar from a human perspective.</div></div><div><h3>Conclusions</h3><div>We demonstrated that our method can efficiently find all full-duplicates and most near-duplicates in a large set of multisourced medical documents. Potential ways to further improve this method are discussed. The method can be applied to documents in all domains.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105799"},"PeriodicalIF":3.7,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records","authors":"Liqin Wang ,&nbsp;John Novoa-Laurentiev ,&nbsp;Claire Cook ,&nbsp;Shruthi Srivatsan ,&nbsp;Yining Hua ,&nbsp;Jie Yang ,&nbsp;Eli Miloslavsky ,&nbsp;Hyon K. Choi ,&nbsp;Li Zhou ,&nbsp;Zachary S. Wallace","doi":"10.1016/j.ijmedinf.2025.105797","DOIUrl":"10.1016/j.ijmedinf.2025.105797","url":null,"abstract":"<div><h3>Background</h3><div>ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.</div></div><div><h3>Methods</h3><div>We examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity, F-score, area under the receiver operating characteristic curve (AUROC), and area under the precision and recall curve (AUPRC). The hierarchical attention network (HAN) was further evaluated for its ability to classify AAV cases at the patient-level, compared with rule-based algorithms in 2000 randomly chosen samples.</div></div><div><h3>Results</h3><div>Datasets I, II, and III comprised 6000, 3008, and 7500 note sections, respectively. HAN achieved the highest AUROC in all three datasets, with scores of 0.983, 0.991, and 0.991. The deep learning approach also had among the highest PPVs across the three datasets (0.941, 0.954, and 0.800, respectively). In a test cohort of 2000 cases, the HAN model achieved a PPV of 0.262 and an estimated sensitivity of 0.975. Compared to the best rule-based algorithm, HAN identified six additional AAV cases, representing 13% of the total.</div></div><div><h3>Conclusion</h3><div>The deep learning model effectively classifies clinical note sections for AAV diagnosis. Its application to EHR notes can potentially uncover additional cases missed by traditional rule-based methods.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105797"},"PeriodicalIF":3.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The use of Artificial Intelligence Algorithms in drug development and clinical trials: A scoping review","authors":"Camila de Brito Pontes ,&nbsp;Antonio Valerio Netto","doi":"10.1016/j.ijmedinf.2025.105798","DOIUrl":"10.1016/j.ijmedinf.2025.105798","url":null,"abstract":"<div><div>Background: Artificial Intelligence (AI) is transforming drug development and clinical trials, helping researchers find new treatments faster and personalize care for patients. By automating tasks like molecule screening and predicting treatment outcomes, AI addresses critical challenges in modern medicine. Objectives: This review explores how AI is being used in drug development and clinical trials, focusing on its benefits, limitations, and potential to improve healthcare outcomes. Methods: A scoping review based on Arksey and O’Malley’s, 2005 framework was conducted, analyzing 1,956 studies from PubMed, Web of Science, IEEE Xplore, and Scopus. Ten studies were selected for in-depth analysis. Results: Common AI techniques include Support Vector Machines, Neural Networks, and Random Forests, applied in tasks such as identifying new drug uses, predicting antibiotic resistance, and streamlining clinical trials. While AI has shown great promise, challenges like inconsistent data quality and difficulties in clinical validation remain. Conclusions: AI offers exciting opportunities to improve healthcare by making drug development and clinical trials more efficient. However, overcoming barriers like data integration and methodological standardization is essential to ensure these tools benefit diverse populations, especially in settings like Brazil, where genetic diversity and health inequalities pose unique challenges.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105798"},"PeriodicalIF":3.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing information for action: A strategic tool for strengthening public health emergency management systems","authors":"Catherine Smallwood ,&nbsp;Carlos Matos ,&nbsp;Hugo Monteiro ,&nbsp;Mark Shapiro ,&nbsp;Miranda Tran Ngoc ,&nbsp;Mohamed Elamein ,&nbsp;Raviv Raz ,&nbsp;Samuel Petragallo","doi":"10.1016/j.ijmedinf.2025.105791","DOIUrl":"10.1016/j.ijmedinf.2025.105791","url":null,"abstract":"<div><h3>Background</h3><div>This paper addresses the importance of timely and robust information systems that underpin emergency response decision-making, as evidenced during the COVID-19 pandemic in the WHO European Region. Recognizing the relevance of these systems, we propose the strengthening of national emergency response information management systems (ERIMS) within the broader digital health information system (HIS) framework. We aim to develop and present an innovative assessment tool designed to evaluate and assist in the strengthening of ERIMS, contributing to a more resilient and effective emergency response.</div></div><div><h3>Methods</h3><div>This study presents the development of an ERIMS assessment tool using a systems-based approach and defined standards by the WHO Regional Office for Europe. The tool provides a systematic methodology to evaluate and enhance a country’s ERIMS capacity, and is designed to accommodate different information system architecture, emergency response coordination mechanisms, and digitalisation levels. The development process involved consultations with experts in digital health, emergency management, and public health policy, as well as lessons identified through pilot implementation of the assessment tool in several countries.</div></div><div><h3>Main findings</h3><div>The application of this tool revealed gaps in existing ERIMS and highlighted the importance of digital transformation in emergency management practices. The tool facilitated the identification of priority areas for improvement and supported the establishment of best practices and international standards for digital health initiatives that relate to emergency management.</div></div><div><h3>Conclusions</h3><div>This assessment tool represents a contribution to the field of digital health and emergency management. It offers a way for countries to enhance their emergency response capabilities, focusing on information for action. By improving ERIMS, countries can ensure better preparedness, timely response and effective recovery from health emergencies, thereby strengthening global health security in the face of future challenges.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"196 ","pages":"Article 105791"},"PeriodicalIF":3.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICT acceptance in social and healthcare context – A scoping review of theories and models","authors":"Heli Kumpulainen ,&nbsp;Ulla-Mari Kinnunen ,&nbsp;Virpi Jylhä","doi":"10.1016/j.ijmedinf.2025.105796","DOIUrl":"10.1016/j.ijmedinf.2025.105796","url":null,"abstract":"<div><h3>Background</h3><div>Although Information and Communication Technology (ICT) acceptance processes have been studied widely in the social and healthcare context, the theoretical background of these studies is widely based on Technology Acceptance Models (TAM, TAM2, UTAUT) and their extensions. These theoretical models have been criticized for being overly simplistic and focusing narrowly on individual adopters’ beliefs, perceptions, and usage intentions without considering multidimensional approaches to capture the complexity of certain phenomena. Thus, there is a need for identifying other approaches to study ICT acceptance in the complex and digitalizing social and health care context.</div></div><div><h3>Aim</h3><div>The aim of this review is to identify and describe existing theories and models of ICT acceptance studies, other than TAM and UTAUT, in the social and health care context. In addition, the aim is to identify and classify the dimensions of ICT acceptance within these theories and models.</div></div><div><h3>Methods</h3><div>JBI methodology for scoping reviews was used as guidance. A literature search was done on four electronic databases (PubMed, Scopus, Web of Science and CINAHL). The reporting was conducted by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for the scoping review (PRISMA-ScR). Further, descriptive statistics and inductive content analysis were used to analyse the theories, models and dimensions of ICT acceptance within them.</div></div><div><h3>Results</h3><div>21 studies were included in this review. Results indicated that ICT acceptance in the social and healthcare context have been studied by using a variety of theories and models from different disciplines. Within these theories and models, organization, technology, human, sociocultural and environment were identified as the main dimensions of ICT acceptance in the social and healthcare context.</div></div><div><h3>Conclusions</h3><div>This review provides an overview of the alternative theories and models of ICT acceptance and dimensions to be considered when assessing ICT acceptance in the social and healthcare context.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105796"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reducing reading time and assessing disease in capsule endoscopy videos: A deep learning approach","authors":"Luís Pinto ,&nbsp;Isabel N. Figueiredo ,&nbsp;Pedro N. Figueiredo","doi":"10.1016/j.ijmedinf.2025.105792","DOIUrl":"10.1016/j.ijmedinf.2025.105792","url":null,"abstract":"<div><h3>Background</h3><div>The wireless capsule endoscope (CE) is a valuable diagnostic tool in gastroenterology, offering a safe and minimally invasive visualization of the gastrointestinal tract. One of the few drawbacks identified by the gastroenterology community is the time-consuming task of analyzing CE videos.</div></div><div><h3>Objectives</h3><div>This article investigates the feasibility of a computer-aided diagnostic method to speed up CE video analysis. We aim to generate a significantly smaller CE video with all the anomalies (i.e., diseases) identified by the medical doctors in the original video.</div></div><div><h3>Methods</h3><div>The summarized video consists of the original video frames classified as anomalous by a pre-trained convolutional neural network (CNN). We evaluate our approach on a testing dataset with eight CE videos captured with five CE types and displaying multiple anomalies.</div></div><div><h3>Results</h3><div>On average, the summarized videos contain 93.33% of the anomalies identified in the original videos. The average playback time of the summarized videos is just 10 min, compared to 58 min for the original videos.</div></div><div><h3>Conclusion</h3><div>Our findings demonstrate the potential of deep learning-aided diagnostic methods to accelerate CE video analysis.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105792"},"PeriodicalIF":3.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a drug allergy alert tiering algorithm for penicillins and cephalosporins","authors":"Rachel L. Wasserman ,&nbsp;Heba H. Edrees ,&nbsp;Diane L. Seger ,&nbsp;Foster R. Goss ,&nbsp;Kimberly G. Blumenthal ,&nbsp;Ying-Chih Lo ,&nbsp;Suzanne Blackley ,&nbsp;David W. Bates ,&nbsp;Li Zhou","doi":"10.1016/j.ijmedinf.2025.105789","DOIUrl":"10.1016/j.ijmedinf.2025.105789","url":null,"abstract":"<div><h3>Introduction</h3><div>Limited research is available regarding recommendations about which drug allergy alerts (DAAs) in clinical decision support (CDS) systems should interrupt provider workflow. The objective was to evaluate the frequency of penicillin and cephalosporin DAA overrides at two institutions. A secondary objective was to redesign DAAs using a new tiered alerting system based on patient factors.</div></div><div><h3>Methods</h3><div>A retrospective, observational study evaluated CDS DAA overrides for penicillins and cephalosporins at two large academic medical centers. Included patients were at least 18 years of age and had a penicillin or cephalosporin DAA fired at the time of medication ordering. We developed a rule-based algorithm to classify DAAs into three groups: no alerts presented to user, non-interruptive (informational) alerts, and interruptive alerts requiring a coded response. The rule-based algorithm includes drug class or cross-sensitivity matches and reaction types with designated severities (high, medium, or low).</div></div><div><h3>Results</h3><div>DAAs for penicillin and cephalosporins were overridden 55% of the time at each institution. Of the DAAs overrides, 85% were cross sensitivity matches and 15% were drug class matches. Reactions were classified as 22% high severity, 29% medium, and 48% low. Most low severity reactions were rash (25%), unspecified reactions with no comments (13%), nausea/vomiting (4%), and GI upset (3%). High severity reactions were mostly other reactions with comments (19%) and anaphylaxis (4%). Approximately 30% of the penicillin and cephalosporin alert overrides could have been non-interruptive alerts based on the penicillin or cephalosporin allergic reaction documented in the EHR at each institution.</div></div><div><h3>Conclusion</h3><div>The majority of penicillin and cephalosporin DAAs were overridden, largely for cross sensitivity in lower severity reactions. The data can be used to inform DAA redesign, reduce override rates, and improve patient safety.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105789"},"PeriodicalIF":3.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing artificial intelligence and cellular population data for timely identification of bacteremia in hospitalized patients","authors":"Wei-Hsun Chen ,&nbsp;Yu-Hsin Chang ,&nbsp;Chiung-Tzu Hsiao ,&nbsp;Po-Ren Hsueh ,&nbsp;Hong-Mo Shih","doi":"10.1016/j.ijmedinf.2025.105788","DOIUrl":"10.1016/j.ijmedinf.2025.105788","url":null,"abstract":"<div><h3>Background</h3><div>Bacteremia is a critical condition with high mortality that requires prompt detection to prevent progression to life-threatening sepsis. Traditional diagnostic approaches, such as blood cultures, are time-consuming. This limitation has encouraged the exploration of rapid prediction methodologies. Cellular Population Data (CPD), which provides detailed insights into white blood cell morphology and functionality, is a promising technique for the early detection of bacteremia.</div></div><div><h3>Methods</h3><div>This study applied machine learning models to analyze laboratory data from hospitalized patients at risk of bacteremia from three hospitals. Using complete blood count (CBC), differential count (DC), and CPD, collected at various time intervals, we trained two sets of artificial intelligence models: one trained using data from patients in the Emergency Department (ED) and another specifically designed for and trained using data from a hospitalized cohort. We evaluated the performance of both models by applying them to the same hospitalized population and comparing their outcomes.</div></div><div><h3>Results</h3><div>The study encompassed analysis of over 66,000 CBC samples. The model tailored for hospitalized patients exhibited superior performance in bacteremia prediction across all cohorts compared with the ED-model, achieving an area under the receiver operating characteristic curve (AUROC) of 0.772 in the validation cohort from China Medical University Hospital and 0.808 and 0.843 in two other hospital cohorts. Notably, nearly half of the top fifteen important features identified by shapely additive explanations values were CPD parameters, underscoring the pivotal role of CPD in predictive models for bacteremia.</div></div><div><h3>Conclusions</h3><div>Artificial intelligence models incorporating CPD data can accurately predict bacteremia in hospitalized patients. Models specifically trained on hospitalized patient data demonstrate enhanced performance over those based on ED data in predicting bacteremia occurrences. Future research must explore the clinical effects of these models, focusing on their potential to assist physicians in managing antibiotic use and patient health.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105788"},"PeriodicalIF":3.7,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring tele-speech therapy: A scoping review of interventions, applications, benefits, and challenges","authors":"Khadijeh Moulaei ,&nbsp;Fatemeh Dinari ,&nbsp;Mobina Hosseini ,&nbsp;Sohrab Almasi ,&nbsp;Babak Sabet ,&nbsp;Romina Anabestani ,&nbsp;Mohammad Reza Afrash","doi":"10.1016/j.ijmedinf.2025.105784","DOIUrl":"10.1016/j.ijmedinf.2025.105784","url":null,"abstract":"<div><h3>Background</h3><div>Speech disorders can significantly impact communication, social interaction, and overall quality of life, affecting individuals of all ages. Telespeech therapy has emerged as an innovative solution, leveraging technology to provide accessible and effective speech interventions remotely. This approach offers flexibility and convenience, addressing barriers such as geographical limitations and scheduling conflicts. This review aims to explore key interventions, applications, benefits, and challenges of telespeech therapy to enhance understanding of its potential in improving speech and language outcomes.</div></div><div><h3>Methods</h3><div>The scoping review was carried out in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Three electronic databases PubMed, Scopus, and Web of Science were searched. Two authors independently screened and selected the studies.</div></div><div><h3>Results</h3><div>Of the 2,587 papers, 52 articles were included in our review. Telespeech was most commonly used for treating aphasia (n = 17), stuttering (n = 8), and Parkinson’s disease (n = 6). The primary interventions included videoconferencing (63 %), web-based platforms (24 %), and mobile applications (13 %), with most services delivered synchronously (63 %) and some asynchronously (37 %). The most common applications were “rehabilitation and treatment” (59 %) and “performance assessment of patients”(35 %). A total of 264 tele-speech benefits and challenges were identified and later consolidated into 40 items (26 benefits, 14 challenges). Key benefits included “reliable access to healthcare services and addressing disparities” (n = 26), “cost savings” (n = 23), and “improving patient outcomes and quality of care” (n = 21). Major challenges were “low-speed internet” (n = 13), “lack of technology skills” (n = 11), and “limited access to technology” (n = 8).</div></div><div><h3>Conclusion</h3><div>Telespeech therapy can be effectively integrated into routine practice, especially in underserved or remote areas. It offers a flexible, cost-effective solution for rehabilitation and performance assessment, improving patient outcomes and addressing healthcare gaps. Continued technological advancements and targeted training can further enhance its benefits and effectiveness.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105784"},"PeriodicalIF":3.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142967401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the COVID-19 pandemic on mHealth adoption: Identification of the main barriers through an international comparative analysis","authors":"Ana Jiménez-Zarco ,&nbsp;Sergio Cámara Mateos ,&nbsp;Marina Bosque-Prous ,&nbsp;Albert Espelt ,&nbsp;Joan Torrent-Sellens ,&nbsp;Keyrellous Adib ,&nbsp;Karapet Davtyan ,&nbsp;Ryan Dos Santos ,&nbsp;Francesc Saigí-Rubió","doi":"10.1016/j.ijmedinf.2024.105779","DOIUrl":"10.1016/j.ijmedinf.2024.105779","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic greatly challenged health systems worldwide. The adoption and application of mHealth technology emerged as a critical response. However, the permanent implementation and use of such technology faces several barriers, which vary by each country’s innovation level and specific health policies. This study provides a detailed analysis of the transformations in mHealth service implementation within the context of the COVID-19 pandemic.</div></div><div><h3>Objectives</h3><div>The study analyses the changes to mHealth service implementation during the COVID-19 pandemic. It seeks to identify the main uses of technology in mHealth, to assess their level of adoption, and to address any barriers found. It also aims to compare different countries to understand how factors such as geographical location and public health policies affect mHealth status worldwide.</div></div><div><h3>Methods</h3><div>The survey tool was a revised version of the World Health Organization (WHO) 2015 Global Survey on eHealth, which had been updated to reflect the latest advances and policy priorities. The 2022 Survey on Digital Health in the WHO European Region was conducted by the WHO between April and October 2022 to gather information from the Member States of that region.</div></div><div><h3>Results</h3><div>This study shows that across the countries analysed, significant variations occurred in mHealth service adoption during the pandemic. Teleconsultation, access to patient information, and appointment reminders were the most implemented services, highlighting the importance of remote care during health crises. Regional differences were identified regarding barriers such as privacy and security and patient digital literacy, underscoring the need to address such shortcomings. These conclusions have important implications for stakeholders in the digital health sector and emphasise the need for collaboration to address the identified challenges.</div></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":"195 ","pages":"Article 105779"},"PeriodicalIF":3.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}