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Evaluating the validity of the nursing statements algorithmically generated based on the International Classifications of Nursing Practice for respiratory nursing care using large language models J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-17 Hyeoneui Kim, Hyewon Park, Sunghoon Kang, Jinsol Kim, Jeongha Kim, Jinsun Jung, Ricky Taira
Objective This study aims to facilitate the creation of quality standardized nursing statements in South Korea’s hospitals using algorithmic generation based on the International Classifications of Nursing Practice (ICNP) and evaluation through Large Language Models. Materials and Methods We algorithmically generated 15 972 statements related to acute respiratory care using 117 concepts and concept
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Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-17 Cheng Peng, Xi Yang, Aokun Chen, Zehao Yu, Kaleb E Smith, Anthony B Costa, Mona G Flores, Jiang Bian, Yonghui Wu
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning. Methods We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion
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Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-16 Gondy Leroy, Jennifer G Andrews, Madison KeAlohi-Preece, Ajay Jaswani, Hyunju Song, Maureen Kelly Galindo, Sydney A Rice
Objective Machine learning (ML) is increasingly employed to diagnose medical conditions, with algorithms trained to assign a single label using a black-box approach. We created an ML approach using deep learning that generates outcomes that are transparent and in line with clinical, diagnostic rules. We demonstrate our approach for autism spectrum disorders (ASD), a neurodevelopmental condition with
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Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-16 Cathy Shyr, Lina Sulieman, Paul A Harris
Objective With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment. Materials and Methods We matched All of Us participants with available
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Measuring interpersonal firearm violence: natural language processing methods to address limitations in criminal charge data J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-12 Julie M Kafka, Julia P Schleimer, Ott Toomet, Kaidi Chen, Alice Ellyson, Ali Rowhani-Rahbar
Objective Firearm violence constitutes a public health crisis in the United States, but comprehensive data infrastructure is lacking to study this problem. To address this challenge, we used natural language processing (NLP) to classify court record documents from alleged violent crimes as firearm-related or non-firearm-related. Materials and Methods We accessed and digitized court records from the
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PresRecST: a novel herbal prescription recommendation algorithm for real-world patients with integration of syndrome differentiation and treatment planning J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-10 Xin Dong, Chenxi Zhao, Xinpeng Song, Lei Zhang, Yu Liu, Jun Wu, Yiran Xu, Ning Xu, Jialing Liu, Haibin Yu, Kuo Yang, Xuezhong Zhou
Objectives Herbal prescription recommendation (HPR) is a hot topic and challenging issue in field of clinical decision support of traditional Chinese medicine (TCM). However, almost all previous HPR methods have not adhered to the clinical principles of syndrome differentiation and treatment planning of TCM, which has resulted in suboptimal performance and difficulties in application to real-world
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Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-08 Jifan Gao, Guanhua Chen, Ann P O’Rourke, John Caskey, Kyle A Carey, Madeline Oguss, Anne Stey, Dmitriy Dligach, Timothy Miller, Anoop Mayampurath, Matthew M Churpek, Majid Afshar
Objective The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data. Materials and
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Can large language models provide secondary reliable opinion on treatment options for dermatological diseases? J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-05 Usman Iqbal, Leon Tsung-Ju Lee, Annisa Ristya Rahmanti, Leo Anthony Celi, Yu-Chuan Jack Li
Objective To investigate the consistency and reliability of medication recommendations provided by ChatGPT for common dermatological conditions, highlighting the potential for ChatGPT to offer second opinions in patient treatment while also delineating possible limitations. Materials and Methods In this mixed-methods study, we used survey questions in April 2023 for drug recommendations generated by
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Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-04 Thomas Petit-Jean, Christel Gérardin, Emmanuelle Berthelot, Gilles Chatellier, Marie Frank, Xavier Tannier, Emmanuelle Kempf, Romain Bey
Objective To develop and validate a natural language processing (NLP) pipeline that detects 18 conditions in French clinical notes, including 16 comorbidities of the Charlson index, while exploring a collaborative and privacy-enhancing workflow. Materials and Methods The detection pipeline relied both on rule-based and machine learning algorithms, respectively, for named entity recognition and entity
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The National Healthcare Safety Network’s digital quality measures: CDC’s automated measures for surveillance of patient safety J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-02 Nadine Shehab, Liora Alschuler, Sean McILvenna, Zabrina Gonzaga, Andrew Laing, David deRoode, Raymund B Dantes, Kristina Betz, Shuai Zheng, Sheila Abner, Elizabeth Stutler, Rick Geimer, Andrea L Benin
Objective This article presents the National Healthcare Safety Network (NHSN)’s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources
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Genetically guided precision medicine clinical decision support tools: a systematic review J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-04-01 Darren Johnson, Guilherme Del Fiol, Kensaku Kawamoto, Katrina M Romagnoli, Nathan Sanders, Grace Isaacson, Elden Jenkins, Marc S Williams
Objectives Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM). Materials and Methods A
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The potential and limitations of large language models in identification of the states of motivations for facilitating health behavior change J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-25 Michelle Bak, Jessie Chin
Importance The study highlights the potential and limitations of the Large Language Models (LLMs) in recognizing different states of motivation to provide appropriate information for behavior change. Following the Transtheoretical Model (TTM), we identified the major gap of LLMs in responding to certain states of motivation through validated scenario studies, suggesting future directions of LLMs research
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Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-23 Feng Chen, Liqin Wang, Julie Hong, Jiaqi Jiang, Li Zhou
Objectives Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be overlooked. This study reviews methods to handle various biases in AI models developed using EHR data. Materials and Methods We conducted a systematic review
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Ensemble pretrained language models to extract biomedical knowledge from literature J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-23 Zhao Li, Qiang Wei, Liang-Chin Huang, Jianfu Li, Yan Hu, Yao-Shun Chuang, Jianping He, Avisha Das, Vipina Kuttichi Keloth, Yuntao Yang, Chiamaka S Diala, Kirk E Roberts, Cui Tao, Xiaoqian Jiang, W Jim Zheng, Hua Xu
Objectives The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science
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Genomics in nephrology: identifying informatics opportunities to improve diagnosis of genetic kidney disorders using a human-centered design approach J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-18 Katrina M Romagnoli, Zachary M Salvati, Darren K Johnson, Heather M Ramey, Alexander R Chang, Marc S Williams
Background Genomic kidney conditions often have a long lag between onset of symptoms and diagnosis. To design a real time genetic diagnosis process that meets the needs of nephrologists, we need to understand the current state, barriers, and facilitators nephrologists and other clinicians who treat kidney conditions experience, and identify areas of opportunity for improvement and innovation. Methods
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Generalizing Parkinson’s disease detection using keystroke dynamics: a self-supervised approach J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-18 Shikha Tripathi, Alejandro Acien, Ashley A Holmes, Teresa Arroyo-Gallego, Luca Giancardo
Objective Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson’s disease (PD) and psychomotor impairment with minimal burden on the user. However, this typically requires datasets with clinically confirmed labels collected in standardized environments, which is challenging, especially for a
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An interpretable predictive deep learning platform for pediatric metabolic diseases J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-18 Hamed Javidi, Arshiya Mariam, Lina Alkhaled, Kevin M Pantalone, Daniel M Rotroff
Objectives Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the development of these long-term complications. Materials and Methods No clinically available tools are currently in widespread use that can predict the onset
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Leveraging large language models for generating responses to patient messages—a subjective analysis J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-18 Siru Liu, Allison B McCoy, Aileen P Wright, Babatunde Carew, Julian Z Genkins, Sean S Huang, Josh F Peterson, Bryan Steitz, Adam Wright
Objective This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal. Materials and Methods Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large
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Assessing the impact of transitioning to 11th revision of the International Classification of Diseases (ICD-11) on comorbidity indices J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-15 Jean Noel Nikiema, Djeneba Thiam, Azadeh Bayani, Alexandre Ayotte, Nadia Sourial, Michèle Bally
Objectives This study aimed to support the implementation of the 11th Revision of the International Classification of Diseases (ICD-11). We used common comorbidity indices as a case study for proactively assessing the impact of transitioning to ICD-11 for mortality and morbidity statistics (ICD-11-MMS) on real-world data analyses. Materials and Methods Using the MIMIC IV database and a table of mappings
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A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-15 Guillem García Subies, Álvaro Barbero Jiménez, Paloma Martínez Fernández
Objectives This comparative analysis aims to assess the efficacy of encoder Language Models for clinical tasks in the Spanish language. The primary goal is to identify the most effective resources within this context Importance This study highlights a critical gap in NLP resources for the Spanish language, particularly in the clinical sector. Given the vast number of Spanish speakers globally and the
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Markov modeling for cost-effectiveness using federated health data network J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-13 Markus Haug, Marek Oja, Maarja Pajusalu, Kerli Mooses, Sulev Reisberg, Jaak Vilo, Antonio Fernández Giménez, Thomas Falconer, Ana Danilović, Filip Maljkovic, Dalia Dawoud, Raivo Kolde
Objective To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. Materials and Methods We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international
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A span-based model for extracting overlapping PICO entities from RCT publications J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-12 Gongbo Zhang, Yiliang Zhou, Yan Hu, Hua Xu, Chunhua Weng, Yifan Peng
Objectives Extracting PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method, PICOX, to extract overlapping PICO entities. Materials and Methods PICOX first identifies entities by assessing whether a word marks the beginning or conclusion of an entity. Then, it uses a multi-label classifier to assign one or more PICO labels
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Learning competing risks across multiple hospitals: one-shot distributed algorithms J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-08 Dazheng Zhang, Jiayi Tong, Naimin Jing, Yuchen Yang, Chongliang Luo, Yiwen Lu, Dimitri A Christakis, Diana Güthe, Mady Hornig, Kelly J Kelleher, Keith E Morse, Colin M Rogerson, Jasmin Divers, Raymond J Carroll, Christopher B Forrest, Yong Chen
Objectives To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children’s hospitals, we quantified the impacts of a wide range of risk factors on the
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Ensuring useful adoption of generative artificial intelligence in healthcare J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-07 Jenelle A Jindal, Matthew P Lungren, Nigam H Shah
Objectives This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI. Materials and Methods We reviewed how technology has historically been deployed in healthcare, and evaluated recent examples of deployments of both traditional AI and generative AI (GenAI) with a lens on value. Results Traditional
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Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-07 Siru Liu, Allison B McCoy, Aileen P Wright, Scott D Nelson, Sean S Huang, Hasan B Ahmad, Sabrina E Carro, Jacob Franklin, James Brogan, Adam Wright
Objectives To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts. Materials and Methods We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts
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Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-07 François Grolleau, François Petit, Stéphane Gaudry, Élise Diard, Jean-Pierre Quenot, Didier Dreyfuss, Viet-Thi Tran, Raphaël Porcher
Objective The timely initiation of renal replacement therapy (RRT) for acute kidney injury (AKI) requires sequential decision-making tailored to individuals’ evolving characteristics. To learn and validate optimal strategies for RRT initiation, we used reinforcement learning on clinical data from routine care and randomized controlled trials. Materials and methods We used the MIMIC-III database for
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Large language models and generative AI in telehealth: a responsible use lens J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-05 Javad Pool, Marta Indulska, Shazia Sadiq
Objective This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. Materials
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Designing for caregiving networks: a case study of primary caregivers of children with medical complexity J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-01 Eleanore Rae Scheer, Nicole E Werner, Ryan J Coller, Carrie L Nacht, Lauren Petty, Mengwei Tang, Mary Ehlenbach, Michelle M Kelly, Sara Finesilver, Gemma Warner, Barbara Katz, Jessica Keim-Malpass, Christopher D Lunsford, Lisa Letzkus, Shaalini Sanjiv Desai, Rupa S Valdez
Objective The study aimed to characterize the experiences of primary caregivers of children with medical complexity (CMC) in engaging with other members of the child’s caregiving network, thereby informing the design of health information technology (IT) for the caregiving network. Caregiving networks include friends, family, community members, and other trusted individuals who provide resources, information
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Methodologies and key considerations for implementing the International Classification of Diseases-11th revision morbidity coding: insights from a national pilot study in China J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-03-01 Meng Zhang, Yipeng Wang, Robert Jakob, Shanna Su, Xue Bai, Xiaotong Jing, Xin Xue, Aimin Liao, Naishi Li, Yi Wang
Objective The aim of this study was to disseminate insights from a nationwide pilot of the International Classification of Diseases-11th revision (ICD-11). Materials and methods The strategies and methodologies employed to implement the ICD-11 morbidity coding in 59 hospitals in China are described. The key considerations for the ICD-11 implementation were summarized based on feedback obtained from
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Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-29 Sharon E Davis, Peter J Embí, Michael E Matheny
Background As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting from evolving clinical environments strain the longevity of AI models as predictive accuracy and associated utility deteriorate over time. Objective Responsible
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Taiyi: a bilingual fine-tuned large language model for diverse biomedical tasks J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-29 Ling Luo, Jinzhong Ning, Yingwen Zhao, Zhijun Wang, Zeyuan Ding, Peng Chen, Weiru Fu, Qinyu Han, Guangtao Xu, Yunzhi Qiu, Dinghao Pan, Jiru Li, Hao Li, Wenduo Feng, Senbo Tu, Yuqi Liu, Zhihao Yang, Jian Wang, Yuanyuan Sun, Hongfei Lin
Objective Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks. Materials
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Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using the OHDSI network J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-27 Behzad Naderalvojoud, Catherine M Curtin, Chen Yanover, Tal El-Hay, Byungjin Choi, Rae Woong Park, Javier Gracia Tabuenca, Mary Pat Reeve, Thomas Falconer, Keith Humphreys, Steven M Asch, Tina Hernandez-Boussard
Background Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both
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Clinical risk prediction using language models: benefits and considerations J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-27 Angeela Acharya, Sulabh Shrestha, Anyi Chen, Joseph Conte, Sanja Avramovic, Siddhartha Sikdar, Antonios Anastasopoulos, Sanmay Das
Objective The use of electronic health records (EHRs) for clinical risk prediction is on the rise. However, in many practical settings, the limited availability of task-specific EHR data can restrict the application of standard machine learning pipelines. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving
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BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-27 François Remy, Kris Demuynck, Thomas Demeester
Objective In this study, we investigate the potential of large language models (LLMs) to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical domains. Materials and Methods Drawing on the wealth of the Unified Medical Language System knowledge graph and harnessing cutting-edge LLMs, we propose a new state-of-the-art approach for obtaining high-fidelity
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Identifying the capabilities for creating next-generation registries: a guide for data leaders and a case for “registry science” J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-24 Steven E Labkoff, Yuri Quintana, Leon Rozenblit
Objective The increasing demands for curated, high-quality research data are driving the emergence of a novel registry type. The need to assemble, curate, and export this data grows, and the conventional simplicity of registry models is driving the need for advanced, multimodal data registries—the dawn of the next-generation registry. Materials and methods The article provides an outline of the technology
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Concerted adoption as an emerging strategy for digital transformation of healthcare—lessons from Australia, Canada, and England J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-24 Kathrin Cresswell, Clair Sullivan, Jeremy Theal, Hajar Mozaffar, Robin Williams
Objectives With an increasing focus on the digitalization of health and care settings, there is significant scope to learn from international approaches to promote concerted adoption of electronic health records. Materials and methods We review three large-scale initiatives from Australia, Canada, and England, and extract common lessons for future health and social care transformation strategy. Results
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Question answering systems for health professionals at the point of care—a systematic review J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-17 Gregory Kell, Angus Roberts, Serge Umansky, Linglong Qian, Davide Ferrari, Frank Soboczenski, Byron C Wallace, Nikhil Patel, Iain J Marshall
Objectives Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. Materials and methods We
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Stressful life events in electronic health records: a scoping review J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-13 Dmitry Scherbakov, Abolfazl Mollalo, Leslie Lenert
Objectives Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care. Materials and Methods Three
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Transformer-based time-to-event prediction for chronic kidney disease deterioration J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-13 Moshe Zisser, Dvir Aran
Objective Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture
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Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-13 Seung-Yup (Joshua) Lee, Mohammed Alzeen, Abdulaziz Ahmed
Objectives The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and provide insights into the extent and nature of the disparities in the ED setting. Materials and Methods The study analyzed a cross-sectional dataset encompassing ED visits from January 2019 to April 2021. The study utilized analytical techniques, including
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The impact of nuance DAX ambient listening AI documentation: a cohort study J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-12 Tyler Haberle, Courtney Cleveland, Greg L Snow, Chris Barber, Nikki Stookey, Cari Thornock, Laurie Younger, Buzzy Mullahkhel, Diego Ize-Ludlow
Objective To assess the impact of the use of an ambient listening/digital scribing solution (Nuance Dragon Ambient eXperience (DAX)) on caregiver engagement, time spent on Electronic Health Record (EHR) including time after hours, productivity, attributed panel size for value-based care providers, documentation timeliness, and Current Procedural Terminology (CPT) submissions. Materials and Methods
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Perspectives of community-based organizations on digital health equity interventions: a key informant interview study J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-07 Katherine K Kim, Uba Backonja
Background Health and healthcare are increasingly dependent on internet and digital solutions. Medically underserved communities that experience health disparities are often those who are burdened by digital disparities. While digital equity and digital health equity are national priorities, there is limited evidence about how community-based organizations (CBOs) consider and develop interventions
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Utilization of electronic health record sex and gender demographic fields: a metadata and mixed methods analysis J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-03 Dinah Foer, David M Rubins, Vi Nguyen, Alex McDowell, Meg Quint, Mitchell Kellaway, Sari L Reisner, Li Zhou, David W Bates
Objectives Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata. Materials and methods Patients ≥18 years of age in the Mass General Brigham health system with a first Legal Sex entry (registration requirement) between January 8, 2018
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Human technology intermediation to reduce cognitive load: understanding healthcare staff members’ practices to facilitate telehealth access in a Federally Qualified Health Center patient population J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-01 Alicia K Williamson, Marcy G Antonio, Sage Davis, Vaishnav Kameswaran, Tawanna R Dillahunt, Lorraine R Buis, Tiffany C Veinot
Objectives The aim of this study was to investigate how healthcare staff intermediaries support Federally Qualified Health Center (FQHC) patients’ access to telehealth, how their approaches reflect cognitive load theory (CLT) and determine which approaches FQHC patients find helpful and whether their perceptions suggest cognitive load (CL) reduction. Materials and Methods Semistructured interviews
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Job search strategies and early careers of clinical informatics fellowship alumni (2016-2022) J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-02-01 Ellen Kim, Melissa Van Cain, Jonathan D Hron
Objective To report on clinical informatics (CI) fellows’ job search and early careers. Materials and Methods In the summer of 2022, we performed a voluntary and anonymous survey of 242 known clinical informatics fellowship alumni from 2016 to 2022. The survey included questions about their initial job search process; first job, salary, and informatics time after training; and early career progression
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Search still matters: information retrieval in the era of generative AI J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-30 William Hersh
Objective Information retrieval (IR, also known as search) systems are ubiquitous in modern times. How does the emergence of generative artificial intelligence (AI), based on large language models (LLMs), fit into the IR process? Process This perspective explores the use of generative AI in the context of the motivations, considerations, and outcomes of the IR process with a focus on the academic use
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Implementation of an electronic health record-integrated instant messaging system in an academic health system J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-30 Brian Kwan, John F Bell, Christopher A Longhurst, Nicole H Goldhaber, Brian Clay
Objectives Effective communication amongst healthcare workers simultaneously promotes optimal patient outcomes when present and is deleterious to outcomes when absent. The advent of electronic health record (EHR)-embedded secure instantaneous messaging systems has provided a new conduit for provider communication. This manuscript describes the experience of one academic medical center with deployment
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Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-26 Satvik Tripathi, Rithvik Sukumaran, Tessa S Cook
Purpose This article explores the potential of large language models (LLMs) to automate administrative tasks in healthcare, alleviating the burden on clinicians caused by electronic medical records. Potential LLMs offer opportunities in clinical documentation, prior authorization, patient education, and access to care. They can personalize patient scheduling, improve documentation accuracy, streamline
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Biometric contrastive learning for data-efficient deep learning from electrocardiographic images J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-25 Veer Sangha, Akshay Khunte, Gregory Holste, Bobak J Mortazavi, Zhangyang Wang, Evangelos K Oikonomou, Rohan Khera
Objective Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECGs). However, traditional supervised learning is limited by the need for large amounts of labeled data. We report the development of Biometric Contrastive Learning (BCL), a self-supervised pretraining approach for label-efficient deep learning on ECG images. Materials and Methods Using pairs of ECGs from
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Harnessing the potential of large language models in medical education: promise and pitfalls J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-25 Trista M Benítez, Yueyuan Xu, J Donald Boudreau, Alfred Wei Chieh Kow, Fernando Bello, Le Van Phuoc, Xiaofei Wang, Xiaodong Sun, Gilberto Ka-Kit Leung, Yanyan Lan, Yaxing Wang, Davy Cheng, Yih-Chung Tham, Tien Yin Wong, Kevin C Chung
Objectives To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum. Process Narrative review of published literature contextualized by current reports of LLM application in medical education. Conclusions LLMs like OpenAI’s ChatGPT can potentially revolutionize traditional teaching methodologies
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Bridging the digital health divide—patient experiences with mobile integrated health and facilitated telehealth by community-level indicators of health disparity J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-25 Brock Daniels, Christina McGinnis, Leah Shafran Topaz, Peter Greenwald, Meghan Reading Turchioe, Ruth Marie Masterson Creber, Rahul Sharma
Objective Evaluate the impact of community tele-paramedicine (CTP) on patient experience and satisfaction relative to community-level indicators of health disparity. Materials and Methods This mixed-methods study evaluates patient-reported satisfaction and experience with CTP, a facilitated telehealth program combining in-home paramedic visits with video visits by emergency physicians. Anonymous post-CTP
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Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-25 Wei-Qi Wei, Robb Rowley, Angela Wood, Jacqueline MacArthur, Peter J Embi, Spiros Denaxas
Introduction Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms
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Comparison of phenomic profiles in the All of Us Research Program against the US general population and the UK Biobank J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-24 Chenjie Zeng, David J Schlueter, Tam C Tran, Anav Babbar, Thomas Cassini, Lisa A Bastarache, Josh C Denny
Importance Knowledge gained from cohort studies has dramatically advanced both public and precision health. The All of Us Research Program seeks to enroll 1 million diverse participants who share multiple sources of data, providing unique opportunities for research. It is important to understand the phenomic profiles of its participants to conduct research in this cohort. Objectives More than 280 000
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Evaluating the ChatGPT family of models for biomedical reasoning and classification J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-23 Shan Chen, Yingya Li, Sheng Lu, Hoang Van, Hugo J W L Aerts, Guergana K Savova, Danielle S Bitterman
Objective Large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates ChatGPT family of models (GPT-3.5, GPT-4) in biomedical tasks beyond question-answering. Materials and Methods We evaluated model performance with 11 122 samples for two fundamental tasks in
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Measuring quality-of-care in treatment of young children with attention-deficit/hyperactivity disorder using pre-trained language models J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-21 Malvika Pillai, Jose Posada, Rebecca M Gardner, Tina Hernandez-Boussard, Yair Bannett
Objective To measure pediatrician adherence to evidence-based guidelines in the treatment of young children with attention-deficit/hyperactivity disorder (ADHD) in a diverse healthcare system using natural language processing (NLP) techniques. Materials and Methods We extracted structured and free-text data from electronic health records (EHRs) of all office visits (2015-2019) of children aged 4-6
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A messaging standard for environmental inspections: is it time? J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-21 Clifford S Mitchell, Tim Callahan, Eamon Flynn
Environmental health (EH) services in the United States lag behind other areas of public health and health care with respect to information system interoperability and data sharing. This is partly due to an absence of well-defined use cases, the lack of direct economic drivers and resources to improve, the multiple jurisdictional elements that govern EH services across the United States, and no central
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A powerful partnership: researchers and patients working together to develop a patient-facing summary of clinical trial outcome data J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-19 Emily Ruzich, Jason Ritchie, France Ginchereau Sowell, Aliyah Mansur, Pip Griffiths, Hannah Birkett, Diane Harman, Jayne Spink, David James, Matthew Reaney
Objective Availability of easy-to-understand patient-reported outcome (PRO) trial data may help individuals make more informed healthcare decisions. Easily interpretable, patient-centric PRO data summaries and visualizations are therefore needed. This three-stage study explored graphical format preferences, understanding, and interpretability of clinical trial PRO data presented to people with prostate
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The Business Process Management for Healthcare (BPM+ Health) Consortium: motivation, methodology, and deliverables for enabling clinical knowledge interoperability (CKI) J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-18 Robert Lario, Richard Soley, Stephen White, John Butler, Guilherme Del Fiol, Karen Eilbeck, Stanley Huff, Kensaku Kawamoto
Objectives To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability. Methods We used the BPM+ Health and Object Management Group (OMG) standards development methodology to develop new languages, following
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Overview of the 8th social media mining for health applications (#SMM4H) shared tasks at the AMIA 2023 annual symposium J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-14 Ari Z Klein, Juan M Banda, Yuting Guo, Ana Lucia Schmidt, Dongfang Xu, Ivan Flores Amaro, Raul Rodriguez-Esteban, Abeed Sarker, Graciela Gonzalez-Hernandez
The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of five tasks that represented various
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Exploring long-term breast cancer survivors’ care trajectories using dynamic time warping-based unsupervised clustering J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-09 Alexia Giannoula, Mercè Comas, Xavier Castells, Francisco Estupiñán-Romero, Enrique Bernal-Delgado, Ferran Sanz, Maria Sala
Objectives Long-term breast cancer survivors (BCS) constitute a complex group of patients, whose number is estimated to continue rising, such that, a dedicated long-term clinical follow-up is necessary. Materials and Methods A dynamic time warping-based unsupervised clustering methodology is presented in this article for the identification of temporal patterns in the care trajectories of 6214 female