Journal of the American Medical Informatics Association最新文献

{"title":"Variations in digital health literacy for pediatric caregivers of hospitalized children: implications for digital health equity.","authors":"Steven Crook, Glenn Rosenbluth, David V Glidden, Alicia Fernandez, Chuan-Mei Lee, Lizette Avina, Leslie Magana, Kiana Washington, Naomi S Bardach","doi":"10.1093/jamia/ocae305","DOIUrl":"10.1093/jamia/ocae305","url":null,"abstract":"<p><strong>Objectives: </strong>We sought to assess whether race, ethnicity, and preferred language were associated with digital health literacy in pediatric caregivers.</p><p><strong>Materials and methods: </strong>We used linear regression to measure associations between 3 eHealth Literacy Questionnaire (eHLQ) domains (score range: 1-4) and demographic characteristics.</p><p><strong>Results: </strong>Non-Latinx White respondents (n = 230) had highest adjusted mean eHLQ scores: 3.44 (95% confidence interval: 3.36-3.52) in \"Ability to engage,\" 3.39 (3.31 to 3.47) in \"Feel safe and in control,\" and 3.34 (3.25 to 3.41) in \"Motivated.\" By contrast, Spanish-preferring Latinx respondents (n = 246) had lower adjusted mean scores across all 3 eHLQ domains: 2.97 (P < .0001), 3.21 (P = .004), and 3.19 (P = .033), respectively.</p><p><strong>Discussion: </strong>Our study contributes insights in variations across ethnoracial and language preference groups by different eHLQ domains, with implications for addressing digital health inequities.</p><p><strong>Conclusion: </strong>Digital health literacy was lower in Spanish-preferring Latinx pediatric caregivers compared to non-Latinx White caregivers across 3 eHLQ domains. It was lower than English-preferring Latinx caregivers in \"Ability.\"</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"572-578"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839964","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":"A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.","authors":"Junbo Shen, Bing Xue, Thomas Kannampallil, Chenyang Lu, Joanna Abraham","doi":"10.1093/jamia/ocae316","DOIUrl":"10.1093/jamia/ocae316","url":null,"abstract":"<p><strong>Objective: </strong>Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning.</p><p><strong>Materials and methods: </strong>This retrospective cohort study used data from the electronic health records of adult surgical patients over 4 years (2018-2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared surgVAE's prediction performance against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation.</p><p><strong>Results: </strong>89 246 surgeries (49% male, median [IQR] age: 57 [45-69]) were included, with 6502 in the targeted cardiac surgery cohort (61% male, median [IQR] age: 60 [53-70]). surgVAE demonstrated generally superior performance over existing ML solutions across postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance.</p><p><strong>Discussion and conclusion: </strong>Our advanced representation learning framework surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"459-469"},"PeriodicalIF":4.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833467/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142899994","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":"A perspective on individualized treatment effects estimation from time-series health data.","authors":"Ghadeer O Ghosheh, Moritz Gögl, Tingting Zhu","doi":"10.1093/jamia/ocae323","DOIUrl":"https://doi.org/10.1093/jamia/ocae323","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this study is to provide an overview of the current landscape of individualized treatment effects (ITE) estimation, specifically focusing on methodologies proposed for time-series electronic health records (EHRs). We aim to identify gaps in the literature, discuss challenges, and propose future research directions to advance the field of personalized medicine.</p><p><strong>Materials and methods: </strong>We conducted a comprehensive literature review to identify and analyze relevant works on ITE estimation for time-series data. The review focused on theoretical assumptions, types of treatment settings, and computational frameworks employed in the existing literature.</p><p><strong>Results: </strong>The literature reveals a growing body of work on ITE estimation for tabular data, while methodologies specific to time-series EHRs are limited. We summarize and discuss the latest advancements, including the types of models proposed, the theoretical foundations, and the computational approaches used.</p><p><strong>Discussion: </strong>The limitations and challenges of current ITE estimation methods for time-series data are discussed, including the lack of standardized evaluation metrics and the need for more diverse and representative datasets. We also highlight considerations and potential biases that may arise in personalized treatment effect estimation.</p><p><strong>Conclusion: </strong>This work provides a comprehensive overview of ITE estimation for time-series EHR data, offering insights into the current state of the field and identifying future research directions. By addressing the limitations and challenges, we hope to encourage further exploration and innovation in this exciting and under-studied area of personalized medicine.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558469","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":"Integrating State-Space Modeling, Parameter Estimation, Deep Learning, and Docking Techniques in Drug Repurposing: A Case Study on COVID-19 Cytokine Storm.","authors":"Abhisek Bakshi, Kaustav Gangopadhyay, Sujit Basak, Rajat K De, Souvik Sengupta, Abhijit Dasgupta","doi":"10.1093/jamia/ocaf035","DOIUrl":"https://doi.org/10.1093/jamia/ocaf035","url":null,"abstract":"<p><strong>Objective: </strong>This study addresses the significant challenges posed by emerging SARS-CoV-2 variants, particularly in developing diagnostics and therapeutics. Drug repurposing is investigated by identifying critical regulatory proteins impacted by the virus, providing rapid and effective therapeutic solutions for better disease management.</p><p><strong>Materials and methods: </strong>We employed a comprehensive approach combining mathematical modeling and efficient parameter estimation to study the transient responses of regulatory proteins in both normal and virus-infected cells. Proportional-integral-derivative (PID) controllers were used to pinpoint specific protein targets for therapeutic intervention. Additionally, advanced deep learning models and molecular docking techniques were applied to analyze drug-target and drug-drug interactions, ensuring both efficacy and safety of the proposed treatments. This approach was applied to a case study focused on the cytokine storm in COVID-19, centering on Angiotensin-converting enzyme 2 (ACE2), which plays a key role in SARS-CoV-2 infection.</p><p><strong>Results: </strong>Our findings suggest that activating ACE2 presents a promising therapeutic strategy, whereas inhibiting AT1R seems less effective. Deep learning models, combined with molecular docking, identified Lomefloxacin and Fostamatinib as stable drugs with no significant thermodynamic interactions, suggesting their safe concurrent use in managing COVID-19-induced cytokine storms.</p><p><strong>Discussion: </strong>The results highlight the potential of ACE2 activation in mitigating lung injury and severe inflammation caused by SARS-CoV-2. This integrated approach accelerates the identification of safe and effective treatment options for emerging viral variants.</p><p><strong>Conclusion: </strong>This framework provides an efficient method for identifying critical regulatory proteins and advancing drug repurposing, contributing to the rapid development of therapeutic strategies for COVID-19 and future global pandemics.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450819","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":"Establishing best practices in large language model research: an application to repeat prompting.","authors":"Robert J Gallo, Michael Baiocchi, Thomas R Savage, Jonathan H Chen","doi":"10.1093/jamia/ocae294","DOIUrl":"10.1093/jamia/ocae294","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to demonstrate the importance of establishing best practices in large language model research, using repeat prompting as an illustrative example.</p><p><strong>Materials and methods: </strong>Using data from a prior study investigating potential model bias in peer review of medical abstracts, we compared methods that ignore correlation in model outputs from repeated prompting with a random effects method that accounts for this correlation.</p><p><strong>Results: </strong>High correlation within groups was found when repeatedly prompting the model, with intraclass correlation coefficient of 0.69. Ignoring the inherent correlation in the data led to over 100-fold inflation of effective sample size. After appropriately accounting for this issue, the authors' results reverse from a small but highly significant finding to no evidence of model bias.</p><p><strong>Discussion: </strong>The establishment of best practices for LLM research is urgently needed, as demonstrated in this case where accounting for repeat prompting in analyses was critical for accurate study conclusions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"386-390"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830529","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":"Health system-wide access to generative artificial intelligence: the New York University Langone Health experience.","authors":"Kiran Malhotra, Batia Wiesenfeld, Vincent J Major, Himanshu Grover, Yindalon Aphinyanaphongs, Paul Testa, Jonathan S Austrian","doi":"10.1093/jamia/ocae285","DOIUrl":"10.1093/jamia/ocae285","url":null,"abstract":"<p><strong>Objectives: </strong>The study aimed to assess the usage and impact of a private and secure instance of a generative artificial intelligence (GenAI) application in a large academic health center. The goal was to understand how employees interact with this technology and the influence on their perception of skill and work performance.</p><p><strong>Materials and methods: </strong>New York University Langone Health (NYULH) established a secure, private, and managed Azure OpenAI service (GenAI Studio) and granted widespread access to employees. Usage was monitored and users were surveyed about their experiences.</p><p><strong>Results: </strong>Over 6 months, over 1007 individuals applied for access, with high usage among research and clinical departments. Users felt prepared to use the GenAI studio, found it easy to use, and would recommend it to a colleague. Users employed the GenAI studio for diverse tasks such as writing, editing, summarizing, data analysis, and idea generation. Challenges included difficulties in educating the workforce in constructing effective prompts and token and API limitations.</p><p><strong>Discussion: </strong>The study demonstrated high interest in and extensive use of GenAI in a healthcare setting, with users employing the technology for diverse tasks. While users identified several challenges, they also recognized the potential of GenAI and indicated a need for more instruction and guidance on effective usage.</p><p><strong>Conclusion: </strong>The private GenAI studio provided a useful tool for employees to augment their skills and apply GenAI to their daily tasks. The study underscored the importance of workforce education when implementing system-wide GenAI and provided insights into its strengths and weaknesses.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"268-274"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756645/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711185","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":"Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.","authors":"Laura G Militello, Julie Diiulio, Debbie L Wilson, Khoa A Nguyen, Christopher A Harle, Walid Gellad, Wei-Hsuan Lo-Ciganic","doi":"10.1093/jamia/ocae291","DOIUrl":"10.1093/jamia/ocae291","url":null,"abstract":"<p><strong>Objectives: </strong>To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).</p><p><strong>Materials and methods: </strong>This perspective paper discusses the interdependency between AI-based algorithm development and UI design and proposes strategies for increasing the safety and efficacy of CDS.</p><p><strong>Results: </strong>The role of design in biasing user behavior is well documented in behavioral economics and other disciplines. We offer an example of how UI designs play a role in how bias manifests in our machine learning-based CDS development.</p><p><strong>Discussion: </strong>Much discussion on bias in AI revolves around data quality and algorithm design; less attention is given to how UI design can exacerbate or mitigate limitations of AI-based applications.</p><p><strong>Conclusion: </strong>This work highlights important considerations including the role of UI design in reinforcing/mitigating bias, human factors methods for identifying issues before an application is released, and risk communication strategies.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"398-403"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756570/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683165","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":"Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.","authors":"Shreya J Shah, Anna Devon-Sand, Stephen P Ma, Yejin Jeong, Trevor Crowell, Margaret Smith, April S Liang, Clarissa Delahaie, Caroline Hsia, Tait Shanafelt, Michael A Pfeffer, Christopher Sharp, Steven Lin, Patricia Garcia","doi":"10.1093/jamia/ocae295","DOIUrl":"10.1093/jamia/ocae295","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.</p><p><strong>Materials and methods: </strong>This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings.</p><p><strong>Results: </strong>Paired survey analysis (n = 38) revealed large statistically significant reductions in task load (-24.42, p <.001) and burnout (-1.94, p <.001), and moderate statistically significant improvements in usability scores (+10.9, p <.001). Post-survey responses (n = 46) indicated favorable utility with improved perceptions of efficiency, documentation quality, and ease of use.</p><p><strong>Discussion: </strong>In one of the first pilot implementations of ambient AI scribe technology, improvements in physician task load, burnout, and usability were demonstrated.</p><p><strong>Conclusion: </strong>Ambient AI scribes like DAX Copilot may enhance clinical workflows. Further research is needed to optimize widespread implementation and evaluate long-term impacts.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"375-380"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830526","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":"Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing.","authors":"Jihye Kim Scroggins, Ismael I Hulchafo, Sarah Harkins, Danielle Scharp, Hans Moen, Anahita Davoudi, Kenrick Cato, Michele Tadiello, Maxim Topaz, Veronica Barcelona","doi":"10.1093/jamia/ocae290","DOIUrl":"10.1093/jamia/ocae290","url":null,"abstract":"<p><strong>Objective: </strong>To identify stigmatizing language in obstetric clinical notes using natural language processing (NLP).</p><p><strong>Materials and methods: </strong>We analyzed electronic health records from birth admissions in the Northeast United States in 2017. We annotated 1771 clinical notes to generate the initial gold standard dataset. Annotators labeled for exemplars of 5 stigmatizing and 1 positive/preferred language categories. We used a semantic similarity-based search approach to expand the initial dataset by adding additional exemplars, composing an enhanced dataset. We employed traditional classifiers (Support Vector Machine, Decision Trees, and Random Forest) and a transformer-based model, ClinicalBERT (Bidirectional Encoder Representations from Transformers) and BERT base. Models were trained and validated on initial and enhanced datasets and were tested on enhanced testing dataset.</p><p><strong>Results: </strong>In the initial dataset, we annotated 963 exemplars as stigmatizing or positive/preferred. The most frequently identified category was marginalized language/identities (n = 397, 41%), and the least frequent was questioning patient credibility (n = 51, 5%). After employing a semantic similarity-based search approach, 502 additional exemplars were added, increasing the number of low-frequency categories. All NLP models also showed improved performance, with Decision Trees demonstrating the greatest improvement (21%). ClinicalBERT outperformed other models, with the highest average F1-score of 0.78.</p><p><strong>Discussion: </strong>Clinical BERT seems to most effectively capture the nuanced and context-dependent stigmatizing language found in obstetric clinical notes, demonstrating its potential clinical applications for real-time monitoring and alerts to prevent usages of stigmatizing language use and reduce healthcare bias. Future research should explore stigmatizing language in diverse geographic locations and clinical settings to further contribute to high-quality and equitable perinatal care.</p><p><strong>Conclusion: </strong>ClinicalBERT effectively captures the nuanced stigmatizing language in obstetric clinical notes. Our semantic similarity-based search approach to rapidly extract additional exemplars enhanced the performances while reducing the need for labor-intensive annotation.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"308-317"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756426/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683210","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":"Distributed, immutable, and transparent biomedical limited data set request management on multi-capacity network.","authors":"Yufei Yu, Maxim Edelson, Anh Pham, Jonathan E Pekar, Brian Johnson, Kai Post, Tsung-Ting Kuo","doi":"10.1093/jamia/ocae288","DOIUrl":"10.1093/jamia/ocae288","url":null,"abstract":"<p><strong>Objective: </strong>Our study aimed to expedite data sharing requests of Limited Data Sets (LDS) through the development of a streamlined platform that allows distributed, immutable management of network activities, provides transparent and intuitive auditing of data access history, and systematically evaluated it on a multi-capacity network setting for meaningful efficiency metrics.</p><p><strong>Materials and methods: </strong>We developed a blockchain-based system with six types of smart contracts to automate the LDS sharing process among major stakeholders. Our workflow included metadata initialization, access-request processing, and audit-log querying. We evaluated our system using synthetic data on three machines with varying specifications to emulate real-world scenarios. The data employed included ∼1000 researcher requests and ∼360 000 log queries.</p><p><strong>Results: </strong>On average, it took ∼2.5 s to register and respond to a researcher access request. The average runtime for an audit-log query with non-empty output was ∼3 ms. The runtime metrics at each institution showed general trends affiliated with their computational capacity.</p><p><strong>Discussion: </strong>Our system can reduce the LDS sharing request time from potentially hours to seconds, while enhancing data access transparency in a multi-institutional setting. There were variations in performance across sites that could be attributed to differences in hardware specifications. The performance gains became marginal beyond certain hardware thresholds, pointing to the influence of external factors such as network speeds.</p><p><strong>Conclusion: </strong>Our blockchain-based system can potentially accelerate clinical research by strengthening the data access process, expediting access and delivery of data links, increasing transparency with clear audit trails, and reinforcing trust in medical data management. Our smart contracts are available at: https://github.com/graceyufei/LDS-Request-Management.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"296-307"},"PeriodicalIF":4.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11756586/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683182","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}