JAMIA Open最新文献

{"title":"Predicting risk of metastases and recurrence in soft-tissue sarcomas via Radiomics and Formal Methods.","authors":"Roberto Casale,&nbsp;Giulia Varriano,&nbsp;Antonella Santone,&nbsp;Carmelo Messina,&nbsp;Chiara Casale,&nbsp;Salvatore Gitto,&nbsp;Luca Maria Sconfienza,&nbsp;Maria Antonietta Bali,&nbsp;Luca Brunese","doi":"10.1093/jamiaopen/ooad025","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad025","url":null,"abstract":"<p><strong>Objective: </strong>Soft-tissue sarcomas (STSs) of the extremities are a group of malignancies arising from the mesenchymal cells that may develop distant metastases or local recurrence. In this article, we propose a novel methodology aimed to predict metastases and recurrence risk in patients with these malignancies by evaluating magnetic resonance radiomic features that will be formally verified through formal logic models.</p><p><strong>Materials and methods: </strong>This is a retrospective study based on a public dataset evaluating MRI scans T2-weighted fat-saturated or short tau inversion recovery and patients having \"metastases/local recurrence\" (group B) or \"no metastases/no local recurrence\" (group A) as clinical outcomes. Once radiomic features are extracted, they are included in formal models, on which is automatically verified the logic property written by a radiologist and his computer scientists coworkers.</p><p><strong>Results: </strong>Evaluating the Formal Methods efficacy in predicting distant metastases/local recurrence in STSs (group A vs group B), our methodology showed a sensitivity and specificity of 0.81 and 0.67, respectively; this suggests that radiomics and formal verification may be useful in predicting future metastases or local recurrence development in soft tissue sarcoma.</p><p><strong>Discussion: </strong>Authors discussed about the literature to consider Formal Methods as a valid alternative to other Artificial Intelligence techniques.</p><p><strong>Conclusions: </strong>An innovative and noninvasive rigourous methodology can be significant in predicting local recurrence and metastases development in STSs. Future works can be the assessment on multicentric studies to extract objective disease information, enriching the connection between the radiomic quantitative analysis and the radiological clinical evidences.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad025"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9903176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural language processing-driven state machines to extract social factors from unstructured clinical documentation.","authors":"Katie S Allen,&nbsp;Dan R Hood,&nbsp;Jonathan Cummins,&nbsp;Suranga Kasturi,&nbsp;Eneida A Mendonca,&nbsp;Joshua R Vest","doi":"10.1093/jamiaopen/ooad024","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad024","url":null,"abstract":"<p><strong>Objective: </strong>This study sought to create natural language processing algorithms to extract the presence of social factors from clinical text in 3 areas: (1) housing, (2) financial, and (3) unemployment. For generalizability, finalized models were validated on data from a separate health system for generalizability.</p><p><strong>Materials and methods: </strong>Notes from 2 healthcare systems, representing a variety of note types, were utilized. To train models, the study utilized n-grams to identify keywords and implemented natural language processing (NLP) state machines across all note types. Manual review was conducted to determine performance. Sampling was based on a set percentage of notes, based on the prevalence of social need. Models were optimized over multiple training and evaluation cycles. Performance metrics were calculated using positive predictive value (PPV), negative predictive value, sensitivity, and specificity.</p><p><strong>Results: </strong>PPV for housing rose from 0.71 to 0.95 over 3 training runs. PPV for financial rose from 0.83 to 0.89 over 2 training iterations, while PPV for unemployment rose from 0.78 to 0.88 over 3 iterations. The test data resulted in PPVs of 0.94, 0.97, and 0.95 for housing, financial, and unemployment, respectively. Final specificity scores were 0.95, 0.97, and 0.95 for housing, financial, and unemployment, respectively.</p><p><strong>Discussion: </strong>We developed 3 rule-based NLP algorithms, trained across health systems. While this is a less sophisticated approach, the algorithms demonstrated a high degree of generalizability, maintaining >0.85 across all predictive performance metrics.</p><p><strong>Conclusion: </strong>The rule-based NLP algorithms demonstrated consistent performance in identifying 3 social factors within clinical text. These methods may be a part of a strategy to measure social factors within an institution.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad024"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5b/2f/ooad024.PMC10112959.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9739089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of the electronic medical record to screen for high-risk geriatric patients in the emergency department.","authors":"Saket Saxena,&nbsp;Stephen Meldon,&nbsp;Ardeshir Z Hashmi,&nbsp;McKinsey Muir,&nbsp;Jeffrey Ruwe","doi":"10.1093/jamiaopen/ooad021","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad021","url":null,"abstract":"<p><p>Older adults with multimorbidities have the highest rate of emergency department (ED) usage. These patients are typically on numerous medications, may have underlying dementia, and often present with falls and delirium. Identifying these high-risk older adults for possible intervention is challenging in the ED setting since available screening methods are manual and resource-intensive. The objective is to study the electronic medical record (EMR) use for identifying high-risk older adults in ED. This feasibility study is conducted in an academic ED with 67 000 total and 24% geriatric (age ≥ 65 years) annual visits, American College of Emergency Physician (ACEP) accredited Level 1 Geriatric Emergency Department with an ED-based geriatric consultation program by incorporating criteria from existing manual geriatric screening instruments and the 4M framework into an automated EMR screen to identify high-risk geriatric patients. ED providers are then alerted by an EMR Best Practice Alert (BPA) if high-risk status is identified. Initial development and impact on geriatric ED consults are reported. During the study period, 7450 patient encounters occurred; 1836 (24.6%) encounters involved patients who were 65 years or older. A total of 1398 (76.1%) high-risk ED encounters resulted in BPA alerts using the EMR automated screen. BPA alerts resulted in 82 (5.9%) geriatric evaluations. We conclude that using the EMR to automate screening for older adults for high-risk geriatric conditions in the ED is feasible. An automated EMR screen with a BPA to ED providers identified a well-defined cohort of older patients appropriate for further ED geriatric evaluation.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad021"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c9/44/ooad021.PMC10085629.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9304502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Common data model for sickle cell disease surveillance: considerations and implications.","authors":"Matthew P Smeltzer,&nbsp;Sarah L Reeves,&nbsp;William O Cooper,&nbsp;Brandon K Attell,&nbsp;John J Strouse,&nbsp;Clifford M Takemoto,&nbsp;Julie Kanter,&nbsp;Krista Latta,&nbsp;Allison P Plaxco,&nbsp;Robert L Davis,&nbsp;Daniel Hatch,&nbsp;Camila Reyes,&nbsp;Kevin Dombkowski,&nbsp;Angela Snyder,&nbsp;Susan Paulukonis,&nbsp;Ashima Singh,&nbsp;Mariam Kayle","doi":"10.1093/jamiaopen/ooad036","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad036","url":null,"abstract":"<p><strong>Objective: </strong>Population-level data on sickle cell disease (SCD) are sparse in the United States. The Centers for Disease Control and Prevention (CDC) is addressing the need for SCD surveillance through state-level Sickle Cell Data Collection Programs (SCDC). The SCDC developed a pilot common informatics infrastructure to standardize processes across states.</p><p><strong>Materials and methods: </strong>We describe the process for establishing and maintaining the proposed common informatics infrastructure for a rare disease, starting with a common data model and identify key data elements for public health SCD reporting.</p><p><strong>Results: </strong>The proposed model is constructed to allow pooling of table shells across states for comparison. Core Surveillance Data reports are compiled based on aggregate data provided by states to CDC annually.</p><p><strong>Discussion and conclusion: </strong>We successfully implemented a pilot SCDC common informatics infrastructure to strengthen our distributed data network and provide a blueprint for similar initiatives in other rare diseases.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad036"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/dc/b2/ooad036.PMC10224800.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9918058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer's and Parkinson's diseases.","authors":"Juan M Banda,&nbsp;Nigam H Shah,&nbsp;Vyjeyanthi S Periyakoil","doi":"10.1093/jamiaopen/ooad043","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad043","url":null,"abstract":"<p><strong>Objective: </strong>Biases within probabilistic electronic phenotyping algorithms are largely unexplored. In this work, we characterize differences in subgroup performance of phenotyping algorithms for Alzheimer's disease and related dementias (ADRD) in older adults.</p><p><strong>Materials and methods: </strong>We created an experimental framework to characterize the performance of probabilistic phenotyping algorithms under different racial distributions allowing us to identify which algorithms may have differential performance, by how much, and under what conditions. We relied on rule-based phenotype definitions as reference to evaluate probabilistic phenotype algorithms created using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation framework.</p><p><strong>Results: </strong>We demonstrate that some algorithms have performance variations anywhere from 3% to 30% for different populations, even when not using race as an input variable. We show that while performance differences in subgroups are not present for all phenotypes, they do affect some phenotypes and groups more disproportionately than others.</p><p><strong>Discussion: </strong>Our analysis establishes the need for a robust evaluation framework for subgroup differences. The underlying patient populations for the algorithms showing subgroup performance differences have great variance between model features when compared with the phenotypes with little to no differences.</p><p><strong>Conclusion: </strong>We have created a framework to identify systematic differences in the performance of probabilistic phenotyping algorithms specifically in the context of ADRD as a use case. Differences in subgroup performance of probabilistic phenotyping algorithms are not widespread nor do they occur consistently. This highlights the great need for careful ongoing monitoring to evaluate, measure, and try to mitigate such differences.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad043"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/63/87/ooad043.PMC10307941.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9801319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Weakly supervised spatial relation extraction from radiology reports.","authors":"Surabhi Datta,&nbsp;Kirk Roberts","doi":"10.1093/jamiaopen/ooad027","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad027","url":null,"abstract":"<p><strong>Objective: </strong>Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports.</p><p><strong>Materials and methods: </strong>Our weak supervision approach is based on data programming that uses rules (or labeling functions) relying on domain-specific dictionaries and radiology language characteristics to generate weak labels. The labels correspond to different spatial relations that are critical to understanding radiology reports. These weak labels are then used to fine-tune a pretrained Bidirectional Encoder Representations from Transformers (BERT) model.</p><p><strong>Results: </strong>Our weakly supervised BERT model provided satisfactory results in extracting spatial relations without manual annotations for training (spatial trigger F1: 72.89, relation F1: 52.47). When this model is further fine-tuned on manual annotations (relation F1: 68.76), performance surpasses the fully supervised state-of-the-art.</p><p><strong>Discussion: </strong>To our knowledge, this is the first work to automatically create detailed weak labels corresponding to radiological information of clinical significance. Our data programming approach is (1) adaptable as the labeling functions can be updated with relatively little manual effort to incorporate more variations in radiology language reporting formats and (2) generalizable as these functions can be applied across multiple radiology subdomains in most cases.</p><p><strong>Conclusions: </strong>We demonstrate a weakly supervision model performs sufficiently well in identifying a variety of relations from radiology text without manual annotations, while exceeding state-of-the-art results when annotated data are available.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad027"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10122604/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9390301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical use of artificial intelligence requires AI-capable organizations.","authors":"Laurie Lovett Novak,&nbsp;Regina G Russell,&nbsp;Kim Garvey,&nbsp;Mehool Patel,&nbsp;Kelly Jean Thomas Craig,&nbsp;Jane Snowdon,&nbsp;Bonnie Miller","doi":"10.1093/jamiaopen/ooad028","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad028","url":null,"abstract":"<p><p>Artificial intelligence-based algorithms are being widely implemented in health care, even as evidence is emerging of bias in their design, problems with implementation, and potential harm to patients. To achieve the promise of using of AI-based tools to improve health, healthcare organizations will need to be AI-capable, with internal and external systems functioning in tandem to ensure the safe, ethical, and effective use of AI-based tools. Ideas are starting to emerge about the organizational routines, competencies, resources, and infrastructures that will be required for safe and effective deployment of AI in health care, but there has been little empirical research. Infrastructures that provide legal and regulatory guidance for managers, clinician competencies for the safe and effective use of AI-based tools, and learner-centric resources such as clear AI documentation and local health ecosystem impact reviews can help drive continuous improvement.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad028"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/35/30/ooad028.PMC10155810.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9423542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An EHR-based method to structure, standardize, and automate clinical documentation tasks for pharmacists to generate extractable outcomes.","authors":"Kimberly A Sanders,&nbsp;Daniel Wolverton,&nbsp;Marina Stamopoulos,&nbsp;Rada Zunich,&nbsp;Joshua Niznik,&nbsp;Stefanie P Ferreri","doi":"10.1093/jamiaopen/ooad034","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad034","url":null,"abstract":"<p><p>As the recognition of team-based care grows and pharmacists increase in patient care interventions, it is important that tools to track clinical services are easily accessible and well-integrated into workflow for all providers. We describe and discuss feasibility and implementation of data tools in an electronic health record to evaluate a pragmatic clinical pharmacy intervention focused on deprescribing in aged adults delivered at multiple clinical sites in a large academic health system. Of the data tools utilized, we were able to demonstrate clear documentation frequency of certain phrases during the intervention period for 574 patients receiving opioids and 537 patients receiving benzodiazepines. Although clinical decision support and documentation tools exist, they are underutilized or cumbersome to integrate into primary health care and strategies, such as employed, are a solution. This communication incorporates the importance of clinical pharmacy information systems in research design.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad034"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9523707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grounded in reality: artificial intelligence in medical education.","authors":"Jacob Krive,&nbsp;Miriam Isola,&nbsp;Linda Chang,&nbsp;Tushar Patel,&nbsp;Max Anderson,&nbsp;Radhika Sreedhar","doi":"10.1093/jamiaopen/ooad037","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad037","url":null,"abstract":"<p><strong>Background: </strong>In a recent survey, medical students expressed eagerness to acquire competencies in the use of artificial intelligence (AI) in medicine. It is time that undergraduate medical education takes the lead in helping students develop these competencies. We propose a solution that integrates competency-driven AI instruction in medical school curriculum.</p><p><strong>Methods: </strong>We applied constructivist and backwards design principles to design online learning assignments simulating the real-world work done in the healthcare industry. Our innovative approach assumed no technical background for students, yet addressed the need for training clinicians to be ready to practice in the new digital patient care environment. This modular 4-week AI course was implemented in 2019, integrating AI with evidence-based medicine, pathology, pharmacology, tele-monitoring, quality improvement, value-based care, and patient safety.</p><p><strong>Results: </strong>This educational innovation was tested in 2 cohorts of fourth year medical students who demonstrated an improvement in knowledge with an average quiz score of 97% and in skills with an average application assignment score of 89%. Weekly reflections revealed how students learned to transition from theory to practice of AI and how these concepts might apply to their upcoming residency training programs and future medical practice.</p><p><strong>Conclusions: </strong>We present an innovative product that achieves the objective of competency-based education of students regarding the role of AI in medicine. This course can be integrated in the preclinical years with a focus on foundational knowledge, vocabulary, and concepts, and in clinical years with a focus on application of core knowledge to real-world scenarios.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad037"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/21/b1/ooad037.PMC10234762.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9579180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing an electronic self-report method for improving quality of ethnicity and race data in the Veterans Health Administration.","authors":"Erin Almklov,&nbsp;Alicia J Cohen,&nbsp;Lauren E Russell,&nbsp;Maria K Mor,&nbsp;Michael J Fine,&nbsp;Leslie R M Hausmann,&nbsp;Ernest Moy,&nbsp;Donna L Washington,&nbsp;Kenneth T Jones,&nbsp;Judith A Long,&nbsp;James Pittman","doi":"10.1093/jamiaopen/ooad020","DOIUrl":"https://doi.org/10.1093/jamiaopen/ooad020","url":null,"abstract":"<p><strong>Objective: </strong>Evaluate self-reported electronic screening (<i>eScreening</i>) in a VA Transition Care Management Program (TCM) to improve the accuracy and completeness of administrative ethnicity and race data.</p><p><strong>Materials and methods: </strong>We compared missing, declined, and complete (neither missing nor declined) rates between (1) <i>TCM-eScreening</i> (ethnicity and race entered into electronic tablet directly by patient using eScreening), (2) <i>TCM-EHR</i> (Veteran-completed paper form plus interview, data entered by staff), and (3) <i>Standard-EHR</i> (multiple processes, data entered by staff). The TCM-eScreening (<i>n</i> = 7113) and TCM-EHR groups (<i>n</i> = 7113) included post-9/11 Veterans. Standard-EHR Veterans included all non-TCM Gulf War and post-9/11 Veterans at VA San Diego (<i>n</i> = 92 921).</p><p><strong>Results: </strong><i>Ethnicity</i>: TCM-eScreening had lower rates of missingness than TCM-EHR and Standard-EHR (3.0% vs 5.3% and 8.6%, respectively, <i>P</i> < .05), but higher rates of \"decline to answer\" (7% vs 0.5% and 1.2%, <i>P</i> < .05). TCM-EHR had higher data completeness than TCM-eScreening and Standard-EHR (94.2% vs 90% and 90.2%, respectively, <i>P</i> < .05). <i>Race</i>: No differences between TCM-eScreening and TCM-EHR for missingness (3.5% vs 3.4%, <i>P</i> > .05) or data completeness (89.9% vs 91%, <i>P</i> > .05). Both had better data completeness than Standard-EHR (<i>P</i> < .05), which despite the lowest rate of \"decline to answer\" (3%) had the highest missingness (10.3%) and lowest overall completeness (86.6%). There was strong agreement between TCM-eScreening and TCM-EHR for ethnicity (Kappa = .92) and for Asian, Black, and White Veteran race (Kappas = .87 to .97), but lower agreement for American Indian/Alaska Native (Kappa = .59) and Native Hawaiian/Other Pacific Islander (Kappa = .50) Veterans.</p><p><strong>Conculsions: </strong>eScreening is a promising method for improving ethnicity and race data accuracy and completeness in VA.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"6 2","pages":"ooad020"},"PeriodicalIF":2.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097454/pdf/ooad020.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9316687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}