Journal of the American Medical Informatics Association最新文献

{"title":"To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.","authors":"Maxwell Salvatore, Ritoban Kundu, Xu Shi, Christopher R Friese, Seunggeun Lee, Lars G Fritsche, Alison M Mondul, David Hanauer, Celeste Leigh Pearce, Bhramar Mukherjee","doi":"10.1093/jamia/ocae098","DOIUrl":"10.1093/jamia/ocae098","url":null,"abstract":"<p><strong>Objectives: </strong>To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.</p><p><strong>Materials and methods: </strong>We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results.</p><p><strong>Results: </strong>For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.</p><p><strong>Discussion: </strong>Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis.</p><p><strong>Conclusion: </strong>EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A system-wide approach to digital equity: the Digital Access Coordinator program in primary care.","authors":"Jorge A Rodriguez, Michelle Zelen, Jessica Szulak, Katie Moore, Lee Park","doi":"10.1093/jamia/ocae104","DOIUrl":"10.1093/jamia/ocae104","url":null,"abstract":"<p><strong>Introduction: </strong>The transition to digital tools prompted by the pandemic made evident digital disparities. To address digital literacy gaps, we implemented a system-wide digital navigation program.</p><p><strong>Methods: </strong>The Digital Access Coordinator (DAC) program consists of 12 multilingual navigators who support patients in enrolling and using the patient portal and digital tools. We implemented the program in our primary care network which consists of 1.25 million patients across 1211 clinicians.</p><p><strong>Results: </strong>From May 2021 to November 2022, the DACs completed outreach to 16 045 patients. Of the 13 413 patients they reached, they successfully enrolled 8193 (61%) patients in the patient portal. Of those patients they enrolled, most patients were of Other race, Hispanic ethnicity, and were English-speaking (44%) and Spanish-speaking patients (44%). Using our embedded model, we increased enrollment across 7 clinics (mean increase: 21.3%, standard deviation: 9.2%). Additionally, we identified key approaches for implementing a digital navigation program.</p><p><strong>Conclusion: </strong>Organizations can support patient portal enrollment, a key part of digital health equity, by creating and prioritizing digital navigation programs.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.","authors":"Sunyang Fu, Liwei Wang, Huan He, Andrew Wen, Nansu Zong, Anamika Kumari, Feifan Liu, Sicheng Zhou, Rui Zhang, Chenyu Li, Yanshan Wang, Jennifer St Sauver, Hongfang Liu, Sunghwan Sohn","doi":"10.1093/jamia/ocae101","DOIUrl":"10.1093/jamia/ocae101","url":null,"abstract":"<p><strong>Background: </strong>Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process.</p><p><strong>Objectives: </strong>This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks.</p><p><strong>Materials and methods: </strong>We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator.</p><p><strong>Results: </strong>The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies.</p><p><strong>Conclusion: </strong>The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140917396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of a health information technology safety classification system in the Veterans Health Administration's Informatics Patient Safety Office.","authors":"Danielle Kato, Joe Lucas, Dean F Sittig","doi":"10.1093/jamia/ocae107","DOIUrl":"10.1093/jamia/ocae107","url":null,"abstract":"<p><strong>Objective: </strong>Implement the 5-type health information technology (HIT) patient safety concern classification system for HIT patient safety issues reported to the Veterans Health Administration's Informatics Patient Safety Office.</p><p><strong>Materials and methods: </strong>A team of informatics safety analysts retrospectively classified 1 year of HIT patient safety issues by type of HIT patient safety concern using consensus discussions. The processes established during retrospective classification were then applied to incoming HIT safety issues moving forward.</p><p><strong>Results: </strong>Of 140 issues retrospectively reviewed, 124 met the classification criteria. The majority were HIT failures (eg, software defects) (33.1%) or configuration and implementation problems (29.8%). Unmet user needs and external system interactions accounted for 20.2% and 10.5%, respectively. Absence of HIT safety features accounted for 2.4% of issues, and 4% did not have enough information to classify.</p><p><strong>Conclusion: </strong>The 5-type HIT safety concern classification framework generated actionable categories helping organizations effectively respond to HIT patient safety risks.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140960495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Acute myocardial infarction prognosis prediction with reliable and interpretable artificial intelligence system.","authors":"Minwook Kim, Donggil Kang, Min Sun Kim, Jeong Cheon Choe, Sun-Hack Lee, Jin Hee Ahn, Jun-Hyok Oh, Jung Hyun Choi, Han Cheol Lee, Kwang Soo Cha, Kyungtae Jang, WooR I Bong, Giltae Song, Hyewon Lee","doi":"10.1093/jamia/ocae114","DOIUrl":"10.1093/jamia/ocae114","url":null,"abstract":"<p><strong>Objective: </strong>Predicting mortality after acute myocardial infarction (AMI) is crucial for timely prescription and treatment of AMI patients, but there are no appropriate AI systems for clinicians. Our primary goal is to develop a reliable and interpretable AI system and provide some valuable insights regarding short, and long-term mortality.</p><p><strong>Materials and methods: </strong>We propose the RIAS framework, an end-to-end framework that is designed with reliability and interpretability at its core and automatically optimizes the given model. Using RIAS, clinicians get accurate and reliable predictions which can be used as likelihood, with global and local explanations, and \"what if\" scenarios to achieve desired outcomes as well.</p><p><strong>Results: </strong>We apply RIAS to AMI prognosis prediction data which comes from the Korean Acute Myocardial Infarction Registry. We compared FT-Transformer with XGBoost and MLP and found that FT-Transformer has superiority in sensitivity and comparable performance in AUROC and F1 score to XGBoost. Furthermore, RIAS reveals the significance of statin-based medications, beta-blockers, and age on mortality regardless of time period. Lastly, we showcase reliable and interpretable results of RIAS with local explanations and counterfactual examples for several realistic scenarios.</p><p><strong>Discussion: </strong>RIAS addresses the \"black-box\" issue in AI by providing both global and local explanations based on SHAP values and reliable predictions, interpretable as actual likelihoods. The system's \"what if\" counterfactual explanations enable clinicians to simulate patient-specific scenarios under various conditions, enhancing its practical utility.</p><p><strong>Conclusion: </strong>The proposed framework provides reliable and interpretable predictions along with counterfactual examples.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11187491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141159027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the utility of using the All of Us Research Program as a resource to study military service members and veterans.","authors":"Ben Porter","doi":"10.1093/jamia/ocae153","DOIUrl":"https://doi.org/10.1093/jamia/ocae153","url":null,"abstract":"<p><strong>Objectives: </strong>To illustrate the utility of the All of Us Research Program for studying military and veteran health.</p><p><strong>Materials and methods: </strong>Results were derived from the All of Us Researcher Workbench Controlled Tier v7. Specific variables examined were family history of post-traumatic stress disorder (PTSD), medical encounters, and body mass index/body size.</p><p><strong>Results: </strong>There are 37 363 military and veteran participants enrolled in the All of Us Research Program. The population is older (M = 63.3 years), White (71.3%), and male (83.2%), consistent with military and veteran populations. Participants reported a high prevalence of PTSD (13.4%), obesity (40.2%), and abdominal obesity (77.1%).</p><p><strong>Discussion and conclusion: </strong>The breadth and depth of health data from service members and veterans enrolled in the All of Us Research Program allow researchers to address pressing health questions in these populations. Future enrollment and data releases will make this an increasingly powerful and useful study for understanding military and veteran health.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of All of Us data to increase health literacy and research skills in high school students.","authors":"Katrina Go Yamazaki, Amy Taylor, Asih Asikin-Garmager, Sharon Han, Laura Bartlett","doi":"10.1093/jamia/ocae150","DOIUrl":"https://doi.org/10.1093/jamia/ocae150","url":null,"abstract":"<p><strong>Objective: </strong>This case study describes how an All of Us engagement project returned value to community by strengthening high school students' capacity to serve as health advocates.</p><p><strong>Materials and methods: </strong>Project activities included health literacy education and research projects on the influence of environmental, societal, and lifestyle factors on community health disparities. The research project involved use of the Photovoice method and All of Us data. At project's end, students presented their research to the community.</p><p><strong>Results: </strong>The project's success was measured by students' participation in the research poster session and comparison of pre- and post-project scores from the Health Literacy Assessment Scale for Adolescent. Data analysis suggests the project succeeded in meeting its goal of increasing students' health literacy.</p><p><strong>Discussion and conclusion: </strong>Through education and research activities, students learned about community health issues and the importance of participation in medical research programs, like All of Us, to address issues.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.","authors":"Shikha Tripathi, Alejandro Acien, Ashley A Holmes, Teresa Arroyo-Gallego, Luca Giancardo","doi":"10.1093/jamia/ocae050","DOIUrl":"10.1093/jamia/ocae050","url":null,"abstract":"<p><strong>Objective: </strong>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 large subject pool. This study validates the efficacy of a self-supervised learning method in reducing the reliance on labels and evaluates its generalizability.</p><p><strong>Materials and methods: </strong>We propose a new type of self-supervised loss combining Barlow Twins loss, which attempts to create similar feature representations with reduced feature redundancy for samples coming from the same subject, and a Dissimilarity loss, which promotes uncorrelated features for samples generated by different subjects. An encoder is first pre-trained using this loss on unlabeled data from an uncontrolled setting, then fine-tuned with clinically validated data. Our experiments test the model generalizability with controls and subjects with PD on 2 independent datasets.</p><p><strong>Results: </strong>Our approach showed better generalization compared to previous methods, including a feature engineering strategy, a deep learning model pre-trained on Parkinsonian signs, and a traditional supervised model.</p><p><strong>Discussion: </strong>The absence of standardized data acquisition protocols and the limited availability of annotated datasets compromise the generalizability of supervised models. In these contexts, self-supervised models offer the advantage of learning more robust patterns from the data, bypassing the need for ground truth labels.</p><p><strong>Conclusion: </strong>This approach has the potential to accelerate the clinical validation of touchscreen typing software for neurodegenerative diseases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11105137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140144458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices.","authors":"","doi":"10.1093/jamia/ocae068","DOIUrl":"10.1093/jamia/ocae068","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11105136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care.","authors":"Satvik Tripathi, Rithvik Sukumaran, Tessa S Cook","doi":"10.1093/jamia/ocad258","DOIUrl":"10.1093/jamia/ocad258","url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Potential: </strong>LLMs offer opportunities in clinical documentation, prior authorization, patient education, and access to care. They can personalize patient scheduling, improve documentation accuracy, streamline insurance prior authorization, increase patient engagement, and address barriers to healthcare access.</p><p><strong>Caution: </strong>However, integrating LLMs requires careful attention to security and privacy concerns, protecting patient data, and complying with regulations like the Health Insurance Portability and Accountability Act (HIPAA). It is crucial to acknowledge that LLMs should supplement, not replace, the human connection and care provided by healthcare professionals.</p><p><strong>Conclusion: </strong>By prudently utilizing LLMs alongside human expertise, healthcare organizations can improve patient care and outcomes. Implementation should be approached with caution and consideration to ensure the safe and effective use of LLMs in the clinical setting.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11105142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139565249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}