Journal of the American Medical Informatics Association最新文献

筛选
英文 中文
Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software. 生成合成电子健康记录数据:对表型数据和开源软件进行基准测试的方法学范围审查。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf082
Xingran Chen, Zhenke Wu, Xu Shi, Hyunghoon Cho, Bhramar Mukherjee
{"title":"Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software.","authors":"Xingran Chen, Zhenke Wu, Xu Shi, Hyunghoon Cho, Bhramar Mukherjee","doi":"10.1093/jamia/ocaf082","DOIUrl":"10.1093/jamia/ocaf082","url":null,"abstract":"<p><strong>Objectives: </strong>To conduct a scoping review (ScR) of existing approaches for synthetic Electronic Health Records (EHR) data generation, to benchmark major methods, and to provide an open-source software and offer recommendations for practitioners.</p><p><strong>Materials and methods: </strong>We search three academic databases for our scoping review. Methods are benchmarked on open-source EHR datasets, Medical Information Mart for Intensive Care III and IV (MIMIC-III/IV). Seven existing methods covering major categories and two baseline methods are implemented and compared. Evaluation metrics concern data fidelity, downstream utility, privacy protection, and computational cost.</p><p><strong>Results: </strong>Forty-eight studies are identified and classified into five categories. Seven open-source methods covering all categories are selected, trained on MIMIC-III, and evaluated on MIMIC-III or MIMIC-IV for transportability considerations. Among them, Generative Adversarial Network (GAN)-based methods demonstrate competitive performance in fidelity and utility on MIMIC-III, rule-based methods excel in privacy protection. Similar findings are observed on MIMIC-IV, except that GAN-based methods further outperform the baseline methods in preserving fidelity.</p><p><strong>Discussion: </strong>Method choice is governed by the relative importance of the evaluation metrics in downstream use cases. We provide a decision tree to guide the choice among the benchmarked methods. An extensible Python package, \"SynthEHRella\", is provided to facilitate streamlined evaluations.</p><p><strong>Conclusion: </strong>GAN-based methods excel when distributional shifts exist between the training and testing populations. Otherwise, CorGAN and MedGAN are most suitable for association modeling and predictive modeling, respectively. Future research should prioritize enhancing fidelity of the synthetic data while controlling privacy exposure, and comprehensive benchmarking of longitudinal or conditional generation methods.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1227-1240"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217409","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}
引用次数: 0
Dynamic few-shot prompting for clinical note section classification using lightweight, open-source large language models. 使用轻量级、开源的大型语言模型进行临床笔记部分分类的动态少量提示。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf084
Kurt Miller, Steven Bedrick, Qiuhao Lu, Andrew Wen, William Hersh, Kirk Roberts, Hongfang Liu
{"title":"Dynamic few-shot prompting for clinical note section classification using lightweight, open-source large language models.","authors":"Kurt Miller, Steven Bedrick, Qiuhao Lu, Andrew Wen, William Hersh, Kirk Roberts, Hongfang Liu","doi":"10.1093/jamia/ocaf084","DOIUrl":"10.1093/jamia/ocaf084","url":null,"abstract":"<p><strong>Objective: </strong>Unlocking clinical information embedded in clinical notes has been hindered to a significant degree by domain-specific and context-sensitive language. Identification of note sections and structural document elements has been shown to improve information extraction and dependent downstream clinical natural language processing (NLP) tasks and applications. This study investigates the viability of a dynamic example selection prompting method to section classification using lightweight, open-source large language models (LLMs) as a practical solution for real-world healthcare clinical NLP systems.</p><p><strong>Materials and methods: </strong>We develop a dynamic few-shot prompting approach to classifying sections where section samples are first embedded using a transformer-based model and deposited in a vector store. During inference, the embedded samples with the most similar contextual embeddings to a given input section text are retrieved from the vector store and inserted into the LLM prompt. We evaluate this technique on two datasets comprising two section schemas, including varying levels of context. We compare the performance to baseline zero-shot and randomly selected few-shot scenarios.</p><p><strong>Results: </strong>The dynamic few-shot prompting experiments yielded the highest F1 scores in each of the classification tasks and datasets for all seven of the LLMs included in the evaluation, averaging a macro F1 increase of 39.3% and 21.1% in our primary section classification task over the zero-shot and static few-shot baselines, respectively.</p><p><strong>Discussion and conclusion: </strong>Our results showcase substantial performance improvements imparted by dynamically selecting examples for few-shot LLM prompting, and further improvement by including section context, demonstrating compelling potential for clinical applications.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1164-1173"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217407","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}
引用次数: 0
Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges. 在医疗保健中采用人工智能:卫生系统优先事项、成功和挑战的调查。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf065
Eric G Poon, Christy Harris Lemak, Juan C Rojas, Janet Guptill, David Classen
{"title":"Adoption of artificial intelligence in healthcare: survey of health system priorities, successes, and challenges.","authors":"Eric G Poon, Christy Harris Lemak, Juan C Rojas, Janet Guptill, David Classen","doi":"10.1093/jamia/ocaf065","DOIUrl":"10.1093/jamia/ocaf065","url":null,"abstract":"<p><strong>Importance: </strong>The US healthcare system faces significant challenges, including clinician burnout, operational inefficiencies, and concerns about patient safety. Artificial intelligence (AI), particularly generative AI, has the potential to address these challenges, but its adoption, effectiveness, and barriers to implementation are not well understood.</p><p><strong>Objective: </strong>To evaluate the current state of AI adoption in US healthcare systems, assess successes and barriers to implementation during the early generative AI era.</p><p><strong>Design, setting, and participants: </strong>This cross-sectional survey was conducted in Fall 2024, and included 67 health systems members of the Scottsdale Institute, a collaborative of US non-profit healthcare organizations. Forty-three health systems completed the survey (64% response rate). Respondents provided data on the deployment status and perceived success of 37 AI use cases across 10 categories.</p><p><strong>Main outcomes and measures: </strong>The primary outcomes were the extent of AI use case development, piloting, or deployment, the degree of reported success for AI use cases, and the most significant barriers to adoption.</p><p><strong>Results: </strong>Across the 43 responding health systems, AI adoption and perceptions of success varied significantly. Ambient Notes, a generative AI tool for clinical documentation, was the only use case with 100% of respondents reporting adoption activities, and 53% reported a high degree of success with using AI for Clinical Documentation. Imaging and radiology emerged as the most widely deployed clinical AI use case, with 90% of organizations reporting at least partial deployment, although successes with diagnostic use cases were limited. Similarly, many organizations have deployed AI for clinical risk stratification such as early sepsis detection, but only 38% report high success in this area. Immature AI tools were identified a significant barrier to adoption, cited by 77% of respondents, followed by financial concerns (47%) and regulatory uncertainty (40%).</p><p><strong>Conclusions and relevance: </strong>Ambient Notes is rapidly advancing in US healthcare systems and demonstrating early success. Other AI use cases show varying degrees of adoption and success, constrained by barriers such as immature AI tools, financial concerns, and regulatory uncertainty. Addressing these challenges through robust evaluations, shared strategies, and governance models will be essential to ensure effective integration and adoption of AI into healthcare practice.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1093-1100"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057241","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}
引用次数: 0
A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis. 用于自动超声心动图报告分析的隐私保护大语言模型的比较分析。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf056
Elham Mahmoudi, Sanaz Vahdati, Chieh-Ju Chao, Bardia Khosravi, Ajay Misra, Francisco Lopez-Jimenez, Bradley J Erickson
{"title":"A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.","authors":"Elham Mahmoudi, Sanaz Vahdati, Chieh-Ju Chao, Bardia Khosravi, Ajay Misra, Francisco Lopez-Jimenez, Bradley J Erickson","doi":"10.1093/jamia/ocaf056","DOIUrl":"10.1093/jamia/ocaf056","url":null,"abstract":"<p><strong>Background: </strong>Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task.</p><p><strong>Methods: </strong>From consecutive transthoracic echocardiographies performed in our center, we utilized 200 random reports from 2019 for prompt optimization and 1000 from 2023 for evaluation. Five instruction-tuned LLMs (Qwen2.0-72B, Llama3.0-70B, Mixtral8-46.7B, Llama3.0-8B, and Phi3.0-3.8B) were guided by prompt instructions with and without CoT to classify prosthetic valve presence and VHD severity. Performance was evaluated using classification metrics against expert-labeled ground truth. Mean squared error (MSE) was also calculated for predicted severity's deviation from actual severity.</p><p><strong>Results: </strong>With CoT prompting, Llama3.0-70B and Qwen2.0 achieved the highest performance (accuracy: 99.1% and 98.9% for VHD severity; 100% and 99.9% for prosthetic valve; MSE: 0.02 and 0.05, respectively). Smaller models showed lower accuracy for VHD severity (54.1%-85.9%) but maintained high accuracy for prosthetic valve detection (>96%). Chain of thought reasoning yielded higher accuracy for larger models while increasing processing time from 2-25 to 67-154 seconds per report. Based on CoT reasonings, the wrong predictions were mainly due to model outputs being influenced by irrelevant information in the text or failure to follow the prompt instructions.</p><p><strong>Conclusions: </strong>Our study demonstrates the near-perfect performance of open-source LLMs for automated echocardiography report interpretation with the purpose of registry formation and disease surveillance. While larger models achieved exceptional accuracy through prompt optimization, practical implementation requires balancing performance with computational efficiency.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1120-1129"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12257941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051988","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}
引用次数: 0
The administrative burden of medication affordability resources: an environmental scan with implications for health informatics to advance health equity. 药物可负担性资源的行政负担:对促进卫生公平的卫生信息学影响的环境扫描。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf087
Marcy G Antonio, Jennylee Swallow, Rachel Richesson, Christine Carethers, Antoinette B Coe, Divya Jahagirdar, Yung-Yi Huang, Tammy Toscos, Mindy Flanagan, Tiffany C Veinot
{"title":"The administrative burden of medication affordability resources: an environmental scan with implications for health informatics to advance health equity.","authors":"Marcy G Antonio, Jennylee Swallow, Rachel Richesson, Christine Carethers, Antoinette B Coe, Divya Jahagirdar, Yung-Yi Huang, Tammy Toscos, Mindy Flanagan, Tiffany C Veinot","doi":"10.1093/jamia/ocaf087","DOIUrl":"10.1093/jamia/ocaf087","url":null,"abstract":"<p><strong>Objective: </strong>To characterize and demonstrate how to reduce the administrative burden experienced by patients when navigating medication affordability resources in the United States.</p><p><strong>Materials and methods: </strong>Informed by administrative burden theory, we conducted an environmental scan of medication affordability resources for atrial fibrillation, and four common comorbidities (diabetes, heart failure, hypertension, and lipid disorder). We systematically searched for resources (eg, patient assistance programs, savings cards and nonprofit support) and extracted information about types, eligibility criteria, needed documentation, and application processes.</p><p><strong>Results: </strong>We identified 66 resources across 12 categories across the five conditions. The resources' varied eligibility criteria, application processes, and requirements for providing sensitive financial documents could introduce multiple administrative costs for patients.</p><p><strong>Discussion: </strong>The volume and complexity of medication affordability resources and related application processes may create substantial administrative burden for patients that could prevent their use-especially when prescribed multiple medications.</p><p><strong>Conclusion: </strong>Medication affordability resource informatics tools that reduce administrative burden could advance equitable medication access.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"32 7","pages":"1206-1218"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334268","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}
引用次数: 0
Supporting rapid innovation in research data capture and management: the REDCap external module framework. 支持研究数据捕获和管理的快速创新:REDCap外部模块框架。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf073
Alex C Cheng, Stephany N Duda, Kyle McGuffin, Mark McEver, Rob Taylor, Günther A Rezniczek, Andrew Martin, Eduardo Morales, Paul A Harris
{"title":"Supporting rapid innovation in research data capture and management: the REDCap external module framework.","authors":"Alex C Cheng, Stephany N Duda, Kyle McGuffin, Mark McEver, Rob Taylor, Günther A Rezniczek, Andrew Martin, Eduardo Morales, Paul A Harris","doi":"10.1093/jamia/ocaf073","DOIUrl":"10.1093/jamia/ocaf073","url":null,"abstract":"<p><strong>Objectives: </strong>Establishing a robust and secure framework allowing creation and sharing of custom features within the REDCap electronic data capture platform.</p><p><strong>Materials and methods: </strong>In partnership with REDCap Consortium members, we developed a framework for creating external modules enabling project-specific REDCap custom functionality (EM Framework). The EM Framework includes guidance and standard processes for developers to ensure basic functionality, compatibility, and security across REDCap instances. The EM Framework also includes an optional dissemination mechanism, the REDCap Repository of External Modules (Repo), for developers to easily share their work with other institutions in the REDCap Consortium.</p><p><strong>Results: </strong>From the EM Framework's launch in 2017 through 2024, 356 external modules have been published to the Repo by software developers at 59 institutions. These modules have been used on 29 485 projects at 2107 institutions in 67 countries. Over time, features from 22 of these external modules have been integrated into the core REDCap code serving 7700+ REDCap Consortium members in 160 countries.</p><p><strong>Discussion: </strong>The EM Framework permits developers to create, test, and deploy custom features to their local REDCap platform. It further enables a process to distribute these features to other REDCap administrators across the Consortium.</p><p><strong>Conclusion: </strong>The EM Framework has enhanced innovation in electronic data capture and dissemination of those innovations to a global research community.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1149-1156"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057082","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}
引用次数: 0
External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older. 65岁及以上社区居民1年死亡率专有风险模型的外部验证
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf062
Erica Frechman, Byron C Jaeger, Marc Kowalkowski, Jeff D Williamson, Kristin M Lenoir, Jessica A Palakshappa, Brian J Wells, Kathryn E Callahan, Nicholas M Pajewski, Jennifer L Gabbard
{"title":"External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older.","authors":"Erica Frechman, Byron C Jaeger, Marc Kowalkowski, Jeff D Williamson, Kristin M Lenoir, Jessica A Palakshappa, Brian J Wells, Kathryn E Callahan, Nicholas M Pajewski, Jennifer L Gabbard","doi":"10.1093/jamia/ocaf062","DOIUrl":"10.1093/jamia/ocaf062","url":null,"abstract":"<p><strong>Objective: </strong>To examine the discrimination, calibration, and algorithmic fairness of the Epic End of Life Care Index (EOL-CI).</p><p><strong>Materials and methods: </strong>We assessed the EOL-CI's performance by estimating area under the receiver operating characteristic curve (AUC), sensitivity, and positive and negative predictive values in community-dwelling adults ≥65 years of age in a single health system in the Southeastern United States. Algorithmic fairness was examined by comparing the model's performance across sex, race, and ethnicity subgroups. Using a machine learning approach, we also explored local re-calibration of the EOL-CI considering additional information on past hospitalizations and frailty.</p><p><strong>Results: </strong>Among 215 731 patients (median age = 74 years, 57% female, 12% of Black race), 10% were classified as medium risk (15-44) and 3% as high risk (≥45) by the EOL-CI. The observed 1-year mortality rate was 3%. The EOL-CI had an AUC 0.82 for 1-year mortality, with a positive predictive value of 22%. Predictive performance was generally similar across sex and race subgroups, though the EOL-CI displayed better performance with increasing age and in older adults with 2 or more outpatient encounters in the past 24 months. Local re-calibration of the EOL-CI was required to provide absolute estimates of mortality risk, and calibration was further improved when the EOL-CI was augmented with data on inpatient hospitalizations and frailty.</p><p><strong>Discussion: </strong>The EOL-CI demonstrates reasonable discrimination, albeit with better performance in older adults and in those with greater health system contact.</p><p><strong>Conclusion: </strong>Local refinement and calibration of the EOL-CI score is required to provide direct estimates of prognosis, with the goal of making the EOL-CI a more a valuable tool at the point of care for identifying patients who would benefit from targeted palliative care interventions and proactive care planning.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1110-1119"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041934","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}
引用次数: 0
Semi-automated pipeline to accelerate multi-site flowsheet alignment and concept mapping in electronic health records. 半自动管道,加速多站点流程对齐和电子健康记录中的概念映射。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf076
Hao Fan, Sarah C Rossetti, Jennifer Thate, Rosemary Mugoya, Albert M Lai, Po-Yin Yen
{"title":"Semi-automated pipeline to accelerate multi-site flowsheet alignment and concept mapping in electronic health records.","authors":"Hao Fan, Sarah C Rossetti, Jennifer Thate, Rosemary Mugoya, Albert M Lai, Po-Yin Yen","doi":"10.1093/jamia/ocaf076","DOIUrl":"10.1093/jamia/ocaf076","url":null,"abstract":"<p><strong>Objectives: </strong>Health-care institutions customize electronic health record (EHR) configurations to reflect their unique workflows and patient care priorities. Ensuring EHR alignment across sites facilitates seamless information exchange. We developed a pipeline for EHR flowsheet alignment between health-care organizations. The pipeline is augmented by mapping flowsheet data fields to concepts in the Clinical Care Classification (CCC) nursing terminology.</p><p><strong>Materials and methods: </strong>Flowsheet templates and measures from 2 study sites were transformed into template-measure (T-M) pairs. They were aligned through exact, lexical, or semantic matching. Lexical matches were assessed using Jaccard similarity and fuzzy matching methods. Semantic alignment was determined using cosine similarity between large language model-generated embeddings of T-M pairs and CCC concepts to rank and recommend the top n concepts in CCC. Concept mappings were evaluated based on whether concepts were mapped consistently within the CCC hierarchy.</p><p><strong>Results: </strong>We totally aligned 31 255 unique T-M pairs in acute care units and 27 012 T-M pairs in intensive care units from 2 study sites. When restricted to the top-ranked CCC concept (n = 1), we achieved a 63% flowsheet alignment rate with a 53% concept mapping rate. Expanding to the top 3 concepts (n = 3) improved alignment to 96.5% and concept mapping to 96%.</p><p><strong>Discussion and conclusion: </strong>Electronic health record data field alignment with concept mapping offers opportunities to standardize data elements presented in flowsheets across health-care sites. We demonstrated the feasibility of leveraging a semi-automated pipeline to streamline the EHR flowsheet alignment and accelerate the manual concept mapping process.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1140-1148"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081789","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}
引用次数: 0
CDEMapper: enhancing National Institutes of Health common data element use with large language models. CDEMapper:增强美国国立卫生研究院公共数据元素与大型语言模型的使用。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf064
Yan Wang, Jimin Huang, Huan He, Vincent Zhang, Yujia Zhou, Xubing Hao, Pritham Ram, Lingfei Qian, Qianqian Xie, Ruey-Ling Weng, Fongci Lin, Yan Hu, Licong Cui, Xiaoqian Jiang, Hua Xu, Na Hong
{"title":"CDEMapper: enhancing National Institutes of Health common data element use with large language models.","authors":"Yan Wang, Jimin Huang, Huan He, Vincent Zhang, Yujia Zhou, Xubing Hao, Pritham Ram, Lingfei Qian, Qianqian Xie, Ruey-Ling Weng, Fongci Lin, Yan Hu, Licong Cui, Xiaoqian Jiang, Hua Xu, Na Hong","doi":"10.1093/jamia/ocaf064","DOIUrl":"10.1093/jamia/ocaf064","url":null,"abstract":"<p><strong>Objective: </strong>Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop a CDE mapping tool to bridge the gap between local data elements and National Institutes of Health (NIH) CDEs.</p><p><strong>Methods: </strong>We propose CDEMapper, a large language model (LLM)-powered mapping tool designed to assist in mapping local data elements to NIH CDEs. CDEMapper has 3 core modules: (1) CDE indexing and embeddings. NIH CDEs were indexed and embedded to support semantic search; (2) CDE recommendations. The tool combines Elasticsearch (BM25 methods) with GPT services to recommend candidate CDEs and their permissible values; and (3) Human review. Users review and select the best match for their data elements and value sets. We evaluate the tool's recommendation accuracy and usability against manual annotations and testing.</p><p><strong>Results: </strong>CDEMapper offers a publicly available, LLM-powered, and intuitive user interface that consolidates essential and advanced mapping services into a streamlined pipeline. The evaluation results demonstrated that the augmented BM25 with GPT embeddings and a GPT ranker achieved the overall best performance. The usability test also highlighted the effectiveness and efficiency of our tool.</p><p><strong>Discussions and conclusions: </strong>This work opens up the potential of using LLMs to assist with CDE mapping when aligning local data elements with NIH CDEs. Additionally, this effort helps researchers better understand the gaps between their data elements and NIH CDEs while promoting CDE reusability.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1130-1139"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005421","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}
引用次数: 0
Towards an estimate of the impact of censorship on biomedical literature. 对审查制度对生物医学文献影响的估计。
IF 4.7 2区 医学
Journal of the American Medical Informatics Association Pub Date : 2025-07-01 DOI: 10.1093/jamia/ocaf089
Clair Kronk, Os Keyes, Megh Marathe
{"title":"Towards an estimate of the impact of censorship on biomedical literature.","authors":"Clair Kronk, Os Keyes, Megh Marathe","doi":"10.1093/jamia/ocaf089","DOIUrl":"10.1093/jamia/ocaf089","url":null,"abstract":"<p><strong>Objective: </strong>To determine how much of the current biomedical literature would be flagged or require modification in relation to the presence of terms from leaked lists prepared by the Centers for Disease Control (CDC), the National Science Foundation (NSF), and the National Security Administration (NSA) in early 2025.</p><p><strong>Materials and methods: </strong>We searched PubMed (from 1996 to 2024) for all records that match at least one of the given terms, combined the terms and analyzed yearly and total frequency.</p><p><strong>Results: </strong>At least 36.3% of all biomedical literature analyzed, representing more than 10 million records, would be flagged for review or modification with the given term lists. It is conservatively estimated that such term lists could impact more than 2.7 million biomedical publications over the next four years.</p><p><strong>Discussion: </strong>Censorship of scientific findings and the use of term lists to judge the content of scientific materials could significantly impede scientific progress.</p><p><strong>Conclusion: </strong>Future research should investigate the long-term implications of, and interim strategies used to navigate, the imposition of censorship on the production and dissemination of scientific knowledge.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"1199-1205"},"PeriodicalIF":4.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227401","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}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信