BMJ Health & Care Informatics最新文献

{"title":"AIAIAI: AI insights on amassing influence in AI-related publications - an AI-assisted retrospective analysis into AI-related publication.","authors":"Rotem Lahat, Noa Berick, Majd Hajouj, Tali Teitelbaum, Isaac Shochat","doi":"10.1136/bmjhci-2024-101244","DOIUrl":"10.1136/bmjhci-2024-101244","url":null,"abstract":"<p><strong>Objectives: </strong>This study analyses the trend of artificial intelligence (AI)-related publications in the medical field over the past decade and demonstrates the potential of AI in automating data analysis. We hypothesise exponential growth in AI-related publications, with continuous growth in the foreseeable future.</p><p><strong>Methods: </strong>Retrospective, AI-assisted analysis was conducted using the OpenAI application programming interface for data collection and evaluation. Publications from the top 50 medical journals (Web of Science, Journal Citation Report, 2022) covering 2014 to June 2024. A total of 315 209 papers were initially retrieved with 212 620 remaining after filtering. The outcomes were the total number and percentage of AI-related publications per year, with future trends prediction using statistical models.</p><p><strong>Results: </strong>AI-related publications increased from approximately 500 in 2014 to over 1000 in 2022, with the percentage rising from 2.5% to over 6% in 2024. The analysis identified cardiology and oncology as leading in AI adoption. Predictive models forecast that AI-related publications could reach 10% by 2030 with long-term projections suggesting potential dominance of AI presence by the mid-22nd century.</p><p><strong>Discussion: </strong>The study highlights the significant growth and integration of AI in medical research, with cardiology and oncology at the forefront. AI-assisted data analysis proves efficient and scalable but requires human oversight to maintain credibility.</p><p><strong>Conclusions: </strong>The trajectory of AI-related publications indicates substantial growth and future integration across medical disciplines. Ongoing evaluation of AI's reliability and applicability in medical research remains essential.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143787564","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":"Value of a Nationwide University Network in scaling up telemonitoring: a qualitative study.","authors":"H J Harm Gijsbers, S Azam Nurmohamed, Linda W Dusseljee-Peute, Marlies P Schijven, Tom H van de Belt","doi":"10.1136/bmjhci-2024-101320","DOIUrl":"10.1136/bmjhci-2024-101320","url":null,"abstract":"<p><strong>Objectives: </strong>The adoption and subsequent implementation of telemonitoring across university hospital settings is a challenging task. This study provides insight into the perceived value of using a nationwide network to support scaling up telemonitoring in university hospitals.</p><p><strong>Methods: </strong>A qualitative approach was used to evaluate the role of the National eHealth network 'Citrien eHealth programme Implementation and Upscaling (Citrien-2)'. In phase 1, an inventory questionnaire was used to identify successes and lessons learnt. Phase 2 consisted of a semi-structured group interview to develop a deeper understanding about the potential value of the network. Subsequently, we conducted a qualitative content analysis and results were organised into key themes of the non-adoption, abandonment, scale-up, spread and sustainability framework.</p><p><strong>Results: </strong>In total, 20 participants responded to our questionnaire, and 7 participants participated in our semistructured group interview. Qualitative analysis revealed 28 themes. The network's key value is the collaboration and structured approach it promotes. This serves as a foundation for exchanging ideas, identifying both temporary and sustainable funding, and establishing a robust stakeholder position, all of which serve to act as a catalyst for implementation and scaling up of telemonitoring.</p><p><strong>Discussion: </strong>Our findings align with known barriers to digital innovation, such as funding and legal issues. Our study shows the value of a nationwide network in overcoming these barriers.</p><p><strong>Conclusions: </strong>The Citrien-2 nationwide network contributes to scaling up telemonitoring across university settings. Therefore, we recommend that governments and their funding agencies recognise and embrace the power of these nationwide networks in scaling up digital initiatives.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771354","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":"Usability of an electronic health record 6 months post go-live and its association with burnout, insomnia and turnover intention: a cross-sectional study in a hospital setting.","authors":"Signe Lohmann-Lafrenz, Sigmund Østgård Gismervik, Solveig Osborg Ose, Lene Aasdahl, Hilde Brun Lauritzen, Arild Faxvaag, Ellen Marie Bardal, Eivind Schjelderup Skarpsno","doi":"10.1136/bmjhci-2024-101200","DOIUrl":"10.1136/bmjhci-2024-101200","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to assess how different groups of health professionals evaluated the usability of a new electronic health record (EHR) and to investigate the association between the usability and burnout, insomnia and turnover intention.</p><p><strong>Methods: </strong>This cross-sectional study included 1424 health professionals who worked at a Norwegian University Hospital. The usability was measured with the System Usability Scale (SUS) 6 months after the previous electronic record was replaced with a more comprehensive, sector-wide, patient-centred EHR in 2022.</p><p><strong>Results: </strong>The median SUS score was 25 (IQR 12.5-37.5) out of 100 and ranged from 15 (IQR 7.5-25.0) among medical doctors to 40 (IQR 27.6-55.0) among laboratory technicians. Nurses reported a score of 25 (IQR 12.5-40.0). In clinical contexts, the median SUS score ranged from 15 (IQR 10.0-30.0) within radiology to 27.5 (IQR 15.0-42.5) within internal medicine, whereas laboratory medicine reported a score of 37.5 (IQR 27.5-55.0). In multivariable analyses using health professionals in the highest quarter of the SUS as the reference, those in the lowest quarter were more likely to report burnout (OR 3.05, 95% CI 1.86 to 5.00), insomnia (OR 1.72, 95% CI 1.18 to 2.50) and turnover intention (OR 2.35, 95% CI 1.53 to 3.64).</p><p><strong>Conclusion: </strong>Most health professionals across all occupational groups and clinical contexts reported low usability of a new EHR 6 months after go-live. Those who reported the lowest usability were more likely to report burnout, insomnia and turnover intention.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742212","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 for predicting interstitial fibrosis and tubular atrophy using diagnostic ultrasound imaging and biomarkers.","authors":"Ting-Wei Chang, Chang-Yu Tsai, Zhen-Yi Tang, Cai-Mei Zheng, Chia-Te Liao, Chung-Yi Cheng, Mai-Szu Wu, Che-Chou Shen, Yen-Chung Lin","doi":"10.1136/bmjhci-2024-101192","DOIUrl":"10.1136/bmjhci-2024-101192","url":null,"abstract":"<p><strong>Background: </strong>Chronic kidney disease (CKD) is a global health concern characterised by irreversible renal damage that is often assessed using invasive renal biopsy. Accurate evaluation of interstitial fibrosis and tubular atrophy (IFTA) is crucial for CKD management. This study aimed to leverage machine learning (ML) models to predict IFTA using a combination of ultrasonography (US) images and patient biomarkers.</p><p><strong>Methods: </strong>We retrospectively collected US images and biomarkers from 632 patients with CKD across three hospitals. The data were subjected to pre-processing, exclusion of sub-optimal images, and feature extraction using a dual-path convolutional neural network. Various ML models, including XGBoost, random forest and logistic regression, were trained and validated using fivefold cross-validation.</p><p><strong>Results: </strong>The dataset was divided into training and test datasets. For image-level IFTA classification, the best performance was achieved by combining US image features and patient biomarkers, with logistic regression yielding an area under the receiver operating characteristic curve (AUROC) of 99%. At the patient level, logistic regression combining US image features and biomarkers provided an AUROC of 96%. Models trained solely on US image features or biomarkers also exhibited high performance, with AUROC exceeding 80%.</p><p><strong>Conclusion: </strong>Our artificial intelligence-based approach to IFTA classification demonstrated high accuracy and AUROC across various ML models. By leveraging patient biomarkers alone, this method offers a non-invasive and robust tool for early CKD assessment, demonstrating that biomarkers alone may suffice for accurate predictions without the added complexity of image-derived features.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647065","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":"Data's big three: how learning health systems, artificial intelligence and predictive analytics are transforming healthcare.","authors":"Kerryn Butler-Henderson, Salma Arabi, Wei Wang","doi":"10.1136/bmjhci-2024-101414","DOIUrl":"10.1136/bmjhci-2024-101414","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11907027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630107","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":"Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.","authors":"Eva María Arriero-País, María Auxiliadora Bajo-Rubio, Roberto Arrojo-García, Pilar Sandoval, Guadalupe Tirma González-Mateo, Patricia Albar-Vizcaíno, Gloria Del Peso-Gilsanz, Marta Ossorio-González, Pedro Majano, Manuel López-Cabrera","doi":"10.1136/bmjhci-2024-101138","DOIUrl":"10.1136/bmjhci-2024-101138","url":null,"abstract":"<p><strong>Objectives: </strong>To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patients in real time using effluent-measured biomarkers of the mesothelial-to-mesenchymal transition (MMT).</p><p><strong>Methods: </strong>Retrospective, longitudinal, triple blind study in two independent hospitals (Spain), designed under information-theoretical approaches for feature selection and machine learning-based modelling techniques. A total of 151 (train set) and 32 (validation) PD patients in 1979-2022 were included. PD outcomes were analysed in four categories (endurance, exit from PD, cause of PD end, technical failure) by using MMT biomarkers in effluents and clinical databases.</p><p><strong>Results: </strong>MMT biomarkers and clinical data can predict PD with a mean absolute error of 16.99 months by using an Extra Tree (ET) regressor. Linear discriminant analysis (LDA) discerns among transfer to haemodialysis or death, predicts whether the cause of PD end is ultrafiltration failure (UFF) or cardiovascular disease (CVD) and anticipates the type of CVD (receiver operating characteristic curve under the area>0.71).</p><p><strong>Discussion: </strong>Our combination of longitudinal PD datasets, attribute shrinkage and gold-standard algorithms with overfitting testing and class imbalance ensures robust predictions in PD. Biomarkers displayed proper mutual information and SHapley values, indicating that MMT processes may have a causal relationship in the development of UFF and CVD.</p><p><strong>Conclusions: </strong>MMT biomarkers and clinical data may be associated in a causal manner with ultrafiltration failure (local effect) and cardiovascular events (systemic effect) in PD. The machine learning-based software MAUXI provides applicability of ET-LDA models with ≤38 variables to predict PD endurance and type of PD technique failure related to peritoneal membrane deterioration.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530876","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":"Benchmarking open-source large language models on Portuguese Revalida multiple-choice questions.","authors":"João Victor Bruneti Severino, Pedro Angelo Basei de Paula, Matheus Nespolo Berger, Filipe Silveira Loures, Solano Amadori Todeschini, Eduardo Augusto Roeder, Maria Han Veiga, Murilo Guedes, Gustavo Lenci Marques","doi":"10.1136/bmjhci-2024-101195","DOIUrl":"https://doi.org/10.1136/bmjhci-2024-101195","url":null,"abstract":"<p><strong>Objective: </strong>The study aimed to evaluate the top large language models (LLMs) in validated medical knowledge tests in Portuguese.</p><p><strong>Methods: </strong>This study compared 31 LLMs in the context of solving the national Brazilian medical examination test. The research compared the performance of 23 open-source and 8 proprietary models across 399 multiple-choice questions.</p><p><strong>Results: </strong>Among the smaller models, Llama 3 8B exhibited the highest success rate, achieving 53.9%, while the medium-sized model Mixtral 8×7B attained a success rate of 63.7%. Conversely, larger models like Llama 3 70B achieved a success rate of 77.5%. Among the proprietary models, GPT-4o and Claude Opus demonstrated superior accuracy, scoring 86.8% and 83.8%, respectively.</p><p><strong>Conclusions: </strong>10 out of the 31 LLMs attained better than human level of performance in the Revalida benchmark, with 9 failing to provide coherent answers to the task. Larger models exhibited superior performance overall. However, certain medium-sized LLMs surpassed the performance of some of the larger LLMs.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy.","authors":"Claudia Cosma, Alessio Radi, Rachele Cattano, Patrizio Zanobini, Guglielmo Bonaccorsi, Chiara Lorini, Marco Del Riccio","doi":"10.1136/bmjhci-2024-101248","DOIUrl":"10.1136/bmjhci-2024-101248","url":null,"abstract":"<p><strong>Objectives: </strong>Informed consent forms are important for assisting patients in making informed choices regarding medical procedures. Because of their lengthy nature, complexity and specialised terminology, consent forms usually prove challenging for the general public to comprehend. This pilot study aims to use Chat Generative Pretrained Transformer (ChatGPT), a large language model (LLM), to improve the readability and understandability of a consent form for vaccination.</p><p><strong>Methods: </strong>The study was conducted in Italy, within the Central Tuscany Local Health Unit. Three different consent forms were selected and approved: the standard consent form currently in use (A), a new form totally generated by ChatGPT (B) and a modified version of the standard form created by ChatGPT (C). Healthcare professionals in the vaccination unit were asked to evaluate the consent forms regarding adequacy, comprehensibility and completeness and to give an overall judgement. The Kruskal-Wallis test and Dunn's test were used to evaluate the median scores of the consent forms across these variables.</p><p><strong>Results: </strong>Consent forms A and C achieved the top scores in every category; consent form B obtained the lowest score. The median scores were 4.0 for adequacy on consent forms A and C and 3.0 on consent form B. Consent forms A and C received high overall judgement ratings with median scores of 4.0, whereas consent form B received a median score of 3.0.</p><p><strong>Conclusions: </strong>The findings indicate that LLM tools such as ChatGPT could enhance healthcare communication by improving the clarity and accessibility of consent forms, but the best results are seen when these tools are combined with human knowledge and supervision.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848659/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476004","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 implementation of a digital coordination centre in an Australian hospital setting: a mixed method study protocol.","authors":"Sam Freeman, Colin Malone, Wynona Black, Daniel Capurro, Wendy W Chapman, Timothy N Fazio, Jana Gazarek, Meredith J Layton, Kayley Lyons, Laura Pumo, Samantha Plumb, Brad Astbury","doi":"10.1136/bmjhci-2024-101300","DOIUrl":"10.1136/bmjhci-2024-101300","url":null,"abstract":"<p><strong>Introduction: </strong>This protocol outlines a mixed methods study evaluating a new Digital Coordination Centre (DCC) at the Royal Melbourne Hospital (RMH), Melbourne, Australia. While coordination centres show potential for impact, evidence on effective implementation in the Australian context remains scarce. This study aims to address this gap.</p><p><strong>Methods and analysis: </strong>The evaluation involves a two-stage approach: a process evaluation to clarify DCC design and identify implementation factors, and an initial outcome evaluation to assess short and medium term outcomes. A developmental approach will support continuous improvement, and implementation science theories applied to unpack change processes. Data sources will include interviews, project documentation and observations, with qualitative and quantitative analyses targeting metrics like emergency department boarding and length of stay.</p><p><strong>Ethics and dissemination: </strong>This study has been approved by the RMH Human Research Ethics Committee (QA2023089). Findings will be shared through peer-reviewed publications and conference presentations.</p>","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11804194/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363483","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":"Biodesign in the generative AI era: enhancing innovation and equity with NLP and LLM tools.","authors":"Jowy Tani","doi":"10.1136/bmjhci-2024-101409","DOIUrl":"10.1136/bmjhci-2024-101409","url":null,"abstract":"","PeriodicalId":9050,"journal":{"name":"BMJ Health & Care Informatics","volume":"32 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143363480","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}