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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
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OHDSI Standardized Vocabularies—a large-scale centralized reference ontology for international data harmonization J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-04 Christian Reich, Anna Ostropolets, Patrick Ryan, Peter Rijnbeek, Martijn Schuemie, Alexander Davydov, Dmitry Dymshyts, George Hripcsak
Importance The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive
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Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-02 Caitlin G Allen, Grace Neil, Chanita Hughes Halbert, Katherine R Sterba, Paul J Nietert, Brandon Welch, Leslie Lenert
Introduction This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians’ perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers. Objectives By identifying design and implementation features
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Do patients who read visit notes on the patient portal have a higher rate of “loop closure” on diagnostic tests and referrals in primary care? A retrospective cohort study J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2024-01-02 Sigall K Bell, Maelys J Amat, Timothy S Anderson, Mark D Aronson, James C Benneyan, Leonor Fernandez, Dru A Ricci, Talya Salant, Gordon D Schiff, Umber Shafiq, Sara J Singer, Scot B Sternberg, Cancan Zhang, Russell S Phillips
Objectives The 2021 US Cures Act may engage patients to help reduce diagnostic errors/delays. We examined the relationship between patient portal registration with/without note reading and test/referral completion in primary care. Materials and methods Retrospective cohort study of patients with visits from January 1, 2018 to December 31, 2021, and order for (1) colonoscopy, (2) dermatology referral
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Deploying a national clinical text processing infrastructure J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-26 Kimberly F McManus, Johnathon Michael Stringer, Neal Corson, Samah Fodeh, Steven Steinhardt, Forrest L Levin, Asqar S Shotqara, Joseph D’Auria, Elliot M Fielstein, Glenn T Gobbel, John Scott, Jodie A Trafton, Tamar H Taddei, Joseph Erdos, Suzanne R Tamang
Objectives Clinical text processing offers a promising avenue for improving multiple aspects of healthcare, though operational deployment remains a substantial challenge. This case report details the implementation of a national clinical text processing infrastructure within the Department of Veterans Affairs (VA). Methods Two foundational use cases, cancer case management and suicide and overdose
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From illness management to quality of life: rethinking consumer health informatics opportunities for progressive, potentially fatal illnesses J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-23 Marcy G Antonio, Tiffany C Veinot
Objectives Investigate how people with chronic obstructive pulmonary disease (COPD)—an example of a progressive, potentially fatal illness—are using digital technologies (DTs) to address illness experiences, outcomes and social connectedness. Materials and Methods A transformative mixed methods study was conducted in Canada with people with COPD (n = 77) or with a progressive lung condition (n = 6)
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Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-19 Joshua C Smith, Brian D Williamson, David J Cronkite, Daniel Park, Jill M Whitaker, Michael F McLemore, Joshua T Osmanski, Robert Winter, Arvind Ramaprasan, Ann Kelley, Mary Shea, Saranrat Wittayanukorn, Danijela Stojanovic, Yueqin Zhao, Sengwee Toh, Kevin B Johnson, David M Aronoff, David S Carrell
Objectives Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential
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Guidance For Reporting Analyses of Metadata on Electronic Health Record Use J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-18 Adam Rule, Thomas Kannampallil, Michelle R Hribar, Adam C Dziorny, Robert Thombley, Nate C Apathy, Julia Adler-Milstein
Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication
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A scoping review of empathy recognition in text using natural language processing J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-14 Vishal Anand Shetty, Shauna Durbin, Meghan S Weyrich, Airín Denise Martínez, Jing Qian, David L Chin
Objective To provide a scoping review of studies on empathy recognition in text using natural language processing (NLP) that can inform an approach to identifying physician empathic communication over patient portal messages. Materials and methods We searched 6 databases to identify relevant studies published through May 1, 2023. The study selection was conducted through a title screening, an abstract
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Retrospective analysis of the impact of electronic medical record alerts on low value care in a pediatric hospital J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-11 Joanna Lawrence, Mike South, Harriet Hiscock, Daniel Capurro, Anurag Sharma, Jemimah Ride
Objectives Hospital costs continue to rise unsustainably. Up to 20% of care is wasteful including low value care (LVC). This study aimed to understand whether electronic medical record (EMR) alerts are effective at reducing pediatric LVC and measure the impact on hospital costs. Materials and Methods Using EMR data over a 76-month period, we evaluated changes in 4 LVC practices following the implementation
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Electronic health record-supported implementation of an evidence-based pathway for perioperative surgical care J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-11 JunBo Wu, Christina T Yuan, Rachel Moyal-Smith, Elizabeth C Wick, Michael A Rosen
Objectives Enhanced recovery pathways (ERPs) are evidence-based approaches to improving perioperative surgical care. However, the role of electronic health records (EHRs) in their implementation is unclear. We examine how EHRs facilitate or hinder ERP implementation. Materials and Methods We conducted interviews with informaticians and clinicians from US hospitals participating in an ERP implementation
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Evaluation framework for conversational agents with artificial intelligence in health interventions: a systematic scoping review J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-09 Hang Ding, Joshua Simmich, Atiyeh Vaezipour, Nicole Andrews, Trevor Russell
Objectives Conversational agents (CAs) with emerging artificial intelligence present new opportunities to assist in health interventions but are difficult to evaluate, deterring their applications in the real world. We aimed to synthesize existing evidence and knowledge and outline an evaluation framework for CA interventions. Materials and Methods We conducted a systematic scoping review to investigate
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Longitudinal clustering of Life’s Essential 8 health metrics: application of a novel unsupervised learning method in the CARDIA study J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-09 Peter Graffy, Lindsay Zimmerman, Yuan Luo, Jingzhi Yu, Yuni Choi, Rachel Zmora, Donald Lloyd-Jones, Norrina Bai Allen
Objective Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics. Materials and methods Life’s Essential 8 (LE8) variables, demographics, and CVD events were queried over 15
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Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-08 Wei Feng, Honghan Wu, Hui Ma, Zhenhuan Tao, Mengdie Xu, Xin Zhang, Shan Lu, Cheng Wan, Yun Liu
Objective Due to heterogeneity and limited medical data in primary healthcare services (PHS), assessing the psychological risk of type 2 diabetes mellitus (T2DM) patients in PHS is difficult. Using unsupervised contrastive pre-training, we proposed a deep learning framework named depression and anxiety prediction (DAP) to predict depression and anxiety in T2DM patients. Materials and Methods The DAP
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Digital literacy in undergraduate pharmacy education: a scoping review J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-07 Mashael Alowais, Georgina Rudd, Victoria Besa, Hamde Nazar, Tejal Shah, Clare Tolley
Objectives Conduct a scoping review to identify the approaches used to integrate digital literacy into undergraduate pharmacy programs across different countries, focusing on methods for education, training, and assessment. Materials and methods Following the Joanna Briggs Institute methodology, we searched 5 electronic databases in June 2022: MEDLINE (Ovid), PubMed, Embase, Scopus, and CINAHL. Three
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Confidence Score: A Data-Driven Measure for Inclusive Systematic Reviews Considering Unpublished Preprints J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-06 Jiayi Tong, Chongliang Luo, Yifei Sun, Rui Duan, Elle Saine, Lifeng Lin, Yifan Peng, Yiwen Lu, Anchita Batra, Anni Pan, Olivia Wang, Ruowang Li, Arielle Marks-Anglin, Yuchen Yang, Xu Zuo, Yulun Liu, Jiang Bian, Stephen E Kimmel, Keith Hamilton, Adam Cuker, Rebecca A Hubbard, Hua Xu, Yong Chen
Objectives COVID-19, since its emergence in December 2019, has globally impacted research. Over 360,000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv and bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role and impact of preprints on COVID-19 research and evidence synthesis remain uncertain. Materials and Methods We propose a
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Academic machine learning researchers’ ethical perspectives on algorithm development for health care: a qualitative study J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-05 Max Kasun, Katie Ryan, Jodi Paik, Kyle Lane-McKinley, Laura Bodin Dunn, Laura Weiss Roberts, Jane Paik Kim
Objectives We set out to describe academic machine learning (ML) researchers’ ethical considerations regarding the development of ML tools intended for use in clinical care. Materials and Methods We conducted in-depth, semistructured interviews with a sample of ML researchers in medicine (N = 10) as part of a larger study investigating stakeholders’ ethical considerations in the translation of ML tools
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Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-01 Nephi A Walton, Radha Nagarajan, Chen Wang, Murat Sincan, Robert R Freimuth, David B Everman, Derek C Walton, Scott P McGrath, Dominick J Lemas, Panayiotis V Benos, Alexander V Alekseyenko, Qianqian Song, Ece Gamsiz Uzun, Casey Overby Taylor, Alper Uzun, Thomas Nate Person, Nadav Rappoport, Zhongming Zhao, Marc S Williams
Objective Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association—Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. Process A list of relevant factors was developed through GenTBI workgroup discussions in multiple
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Methods for studying medication safety following electronic health record implementation in acute care: a scoping review J. Am. Med. Inform. Assoc. (IF 6.4) Pub Date : 2023-12-01 Nichole Pereira, Jonathan P Duff, Tracy Hayward, Tamizan Kherani, Nadine Moniz, Chrystale Champigny, Andrew Carson-Stevens, Paul Bowie, Rylan Egan
Objectives The objective of this scoping review is to map methods used to study medication safety following electronic health record (EHR) implementation. Patterns and methodological gaps can provide insight for future research design. Materials and methods We used the Joanna Briggs Institute scoping review methodology and a custom data extraction table to summarize the following data: (1) study demographics