AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献_第5页

Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes. 评估大型语言模型在眼科临床自由文本笔记中命名实体识别的性能。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Iyad Majid, Vaibhav Mishra, Rohith Ravindranath, Sophia Y Wang

引用次数: 0

Improving Emergency Department Visit Risk Prediction: Exploring the Operational Utility of Applied Patient Portal Messages. 改进急诊科就诊风险预测：探索应用患者门户信息的操作效用。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Hanna Kiani, Sohaib Hassan, Julian Z Genkins, Jasmine Bilir, Julia Kadie, Tran Le, Jo-Anne Suffoletto, Jonathan H Chen

引用次数: 0

Translating Nursing Data into Computational Metrics: An Evaluation Guideline for Inpatient Intravenous and Subcutaneous Insulin Management. 将护理数据转化为计算指标：住院患者静脉注射和皮下胰岛素管理的评估指南。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Varsha Varkhedi, Kenrick Cato, David Albers, Victoria L Tiase, Shalmali Joshi, Jennifer Thate, Kathryn Connell, William Hull, Amy Finnegan, Sarah C Rossetti

{"title":"Translating Nursing Data into Computational Metrics: An Evaluation Guideline for Inpatient Intravenous and Subcutaneous Insulin Management.","authors":"Varsha Varkhedi, Kenrick Cato, David Albers, Victoria L Tiase, Shalmali Joshi, Jennifer Thate, Kathryn Connell, William Hull, Amy Finnegan, Sarah C Rossetti","doi":"","DOIUrl":"","url":null,"abstract":"A challenge in utilizing electronic health record data for artificial intelligence models is contextualization, including understanding differences between missing data and missed care. Our team aims to develop knowledge graphs and computational models that account for these contexts, such as when data is missing (nurses being unable to document), but acceptable nursing care was delivered. We developed evaluation guidelines for intravenous and subcutaneous insulin management to establish a binary variable derived from EHR data representing minimally acceptable safe and quality nursing care for use in computational modeling. These guidelines were developed by our nurse informatics team based on best practices and validated by three nurse subject matter experts. The resulting evaluation guidelines are agnostic to institutional policies and focus on evaluating minimally acceptable safe and quality levels of care to inform inferences about missing data versus missed nursing care. Future work includes data-driven validations and expanding to other clinical scenarios.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1295-1304"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273125","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}

引用次数: 0

EHR-Based Social Needs Screening and Referral in Primary Care: Clinician and Staff Perspectives on Practices, Barriers, and Benefits. 基于电子病历的社会需求筛查和初级保健转诊：临床医生和工作人员对实践、障碍和利益的看法。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Ziqing Ji, Patrick Wedgeworth, Kathleen Mertens, Sara L Jackson, Nkem O Akinsoto, Jared W Klein, Margaret L Isaac, Andrea L Hartzler

{"title":"EHR-Based Social Needs Screening and Referral in Primary Care: Clinician and Staff Perspectives on Practices, Barriers, and Benefits.","authors":"Ziqing Ji, Patrick Wedgeworth, Kathleen Mertens, Sara L Jackson, Nkem O Akinsoto, Jared W Klein, Margaret L Isaac, Andrea L Hartzler","doi":"","DOIUrl":"","url":null,"abstract":"Identifying social drivers of health (SDoH) can help healthcare professionals connect patients with community resources that impact health outcomes. However, screening and referral require time and effort, highlighting the potential for electronic health record (EHR) support to improve efficacy. We surveyed primary care clinicians and staff (n=122) about current SDoH screening and referral practices, as well as perceived barriers and benefits for standardizing those practices with EHR support. Although the majority currently screen and refer patients for SDoH at least occasionally, most respondents document in the EHR using unstructured formats and rate current practices as only moderately feasible and acceptable. While the majority feel competent with SDoH screening, key barriers included time constraints, lack of dedicated staff, and a desire for access to more community resources. Findings underscore the need for an EHR-based standardized screening and referral implementation with flexibility to adapt workflows in different clinic settings and clinical positions.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"536-545"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273114","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}

引用次数: 0

From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support. 从食物到诊所：将食物映射到UMLS以实现营养决策支持。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Indra Neil Sarkar

引用次数: 0

Healthy Lifestyles and Self-Improvement Videos on YouTube: A Thematic Analysis of Teen-Targeted Social Media Content. YouTube上的健康生活方式和自我提升视频：针对青少年的社交媒体内容的专题分析。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Kyuha Jung, Tyler Kim, Yunan Chen

引用次数: 0

Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge. 临床实践中不断变化的信息需求：生成式人工智能在解决临床医生对特定情境知识需求方面的演变作用。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Sachleen K Tuteja, Elise L Boventer, Abdulaziz Alkattan, Noemie Elhadad, Sarah C Rossetti

{"title":"Shifting Information Needs in Clinical Practice: The Evolving Role of Generative AI in Addressing Clinician Demands for Context-Specific Knowledge.","authors":"Sachleen K Tuteja, Elise L Boventer, Abdulaziz Alkattan, Noemie Elhadad, Sarah C Rossetti","doi":"","DOIUrl":"","url":null,"abstract":"This study explores clinicians' evolving information needs and evaluates the potential of Generative Artificial Intelligence (Gen AI) to address these gaps by reassessing and extending the Currie et al. (2003) taxonomy. Despite advancements in electronic health records (EHRs), unresolved information needs persist, impacting clinical efficiency and patient care. A cross-sectional survey conducted at Columbia University Irving Medical Center (CUIMC) analyzed clinician-generated Gen AI prompts, comparing them against the 2003 taxonomy. Findings reveal that while 80% of prompts align with existing categories, 20% represent emerging needs, including AI-driven workflow optimization and fairness-related inquiries. These findings highlight the necessity of adapting clinical decision support frameworks to integrate AI-driven solutions, ensuring that modern tools meet evolving clinician needs. By formally extending the Currie et al. taxonomy, this study provides a foundational framework for leveraging Gen AI to bridge long-standing information gaps and enhance patient outcomes in an increasingly complex healthcare environment.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1277-1284"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273076","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}

引用次数: 0

Automating Adjudication of Cardiovascular Events Using Large Language Models. 使用大型语言模型自动判定心血管事件。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Sonish Sivarajkumar, Kimia Ameri, Chuqin Li, Yanshan Wang, Min Jiang

{"title":"Automating Adjudication of Cardiovascular Events Using Large Language Models.","authors":"Sonish Sivarajkumar, Kimia Ameri, Chuqin Li, Yanshan Wang, Min Jiang","doi":"","DOIUrl":"","url":null,"abstract":"Cardiovascular event adjudication is essential in clinical trials but relies on manual chart review that is slow, variable, and expensive. We present a two-stage framework that automates adjudication of cardiovascular deaths using large language models (LLMs). First, a few-shot LLM extracts structured evidence (event, span, negation, date) from unstructured clinical documents. Second, a Tree-of-Thoughts adjudicator aligns its reasoning with clinical endpoint committee (CEC) guidelines to classify deaths as cardiovascular or non-cardiovascular and produce an auditable rationale. On Lilly clinical-trial data, extraction achieved precision 0.96, recall 0.71 (F1 0.82), and adjudication attained 0.68 accuracy (GPT-4 ToT), outperforming a summarizer-plus-adjudicator baseline. We introduce CLEART, a rubric-based automated score that quantifies rationale quality across clarity, consistency, detail, guideline adherence, relevance, and timeline accuracy (overall 0.67), highlighting temporal reasoning and relevance as key areas for improvement. This approach can reduce adjudication time and variability while increasing transparency.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1219-1228"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273197","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}

引用次数: 0

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements. 通过检索增强生成改进大型语言模型的电子健康记录处理：膳食补充剂的案例研究。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Zaifu Zhan, Shuang Zhou, Jiawen Deng, Rui Zhang

{"title":"Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.","authors":"Zaifu Zhan, Shuang Zhou, Jiawen Deng, Rui Zhang","doi":"","DOIUrl":"","url":null,"abstract":"Large language models (LLMs) excel in natural language processing (NLP) but struggle with domain-specific complexities in electronic health records (EHRs). We demonstrate that retrieval-augmented generation (RAG) enhances LLMs for dietary supplement (DS) information extraction. By testing models like Llama-3 with diverse retrievers on tasks including entity recognition and usage classification, task-aligned retrieval outperforms reliance on model size or specialization. Smaller general models paired with optimized retrievers match or exceed specialized counterparts-structured retrieval aids complex tasks (e.g., triple extraction), while semantic retrieval improves classification. Results challenge assumptions that larger or domain-specific models are superior, emphasizing dynamic knowledge integration over brute-force scaling. This approach offers practical strategies for clinical NLP, enabling efficient EHR analysis without massive resources. Prioritizing retrieval strategies over model size advances tools for evidence-based healthcare, highlighting adaptability and cost-effectiveness in real-world medical applications.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"1511-1518"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273093","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}

引用次数: 0

Exploring the Implementation Experience and Use of CONCERN Early Warning System in a Rural Community Hospital: A Mixed Method Convergent Approach. 探讨CONCERN预警系统在农村社区医院的实施经验与应用：一种混合融合的方法。

AMIA ... Annual Symposium proceedings. AMIA Symposium Pub Date : 2025-05-22 eCollection Date: 2024-01-01

Youngjin Lee, Min-Jeoung Kang, Veysel K Baris, Graham Lowenthal, Sarah C Rossetti, Kenrick D Cato, Rachel Y Lee, Janna Kramer, Richard Huffam, Patricia C Dykes

{"title":"Exploring the Implementation Experience and Use of CONCERN Early Warning System in a Rural Community Hospital: A Mixed Method Convergent Approach.","authors":"Youngjin Lee, Min-Jeoung Kang, Veysel K Baris, Graham Lowenthal, Sarah C Rossetti, Kenrick D Cato, Rachel Y Lee, Janna Kramer, Richard Huffam, Patricia C Dykes","doi":"","DOIUrl":"","url":null,"abstract":"The Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that analyzes nursing documentation patterns to detect early signs of patient deterioration, with proven effectiveness in reducing the risk of in-hospital mortality and length of stay. This study extends the evaluation of CONCERN EWS beyond acute care settings to a rural community hospital, assessing user experience, system utilization, and accuracy in identifying patient deterioration. The study examined accuracy in a rural setting using a mixed-methods approach-qualitative interviews, quantitative data analysis, and clinical record reviews. Findingssuggestthat CONCERNEWS enhancesearly recognition of clinical deterioration andisusable in the context of busy acute care nursing workflows. These results support its adaptability to facilitate strengthening nursing surveillance, clinical decision-making, and patient safety in rural healthcare settings as well.","PeriodicalId":72180,"journal":{"name":"AMIA ... Annual Symposium proceedings. AMIA Symposium","volume":"2024 ","pages":"724-733"},"PeriodicalIF":0.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12919451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147273107","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}

引用次数: 0