Journal of the American Medical Informatics Association最新文献

{"title":"Returning value from the All of Us Research Program to PhD-level nursing students using ChatGPT as programming support: results from a mixed-methods experimental feasibility study.","authors":"Meghan Reading Turchioe, Sergey Kisselev, Ruilin Fan, Suzanne Bakken","doi":"10.1093/jamia/ocae208","DOIUrl":"10.1093/jamia/ocae208","url":null,"abstract":"<p><strong>Objective: </strong>We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.</p><p><strong>Materials and methods: </strong>9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process.</p><p><strong>Results: </strong>The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate.</p><p><strong>Discussion: </strong>ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources.</p><p><strong>Conclusion: </strong>Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2974-2979"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631052/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793941","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":"Research to classrooms: a co-designed curriculum brings All of Us data to secondary schools.","authors":"Louisa A Stark, Kristin E Fenker, Harini Krishnan, Molly Malone, Rebecca J Peterson, Regina Cowan, Jeremy Ensrud, Hector Gamboa, Crstina Gayed, Patricia Refino, Tia Tolk, Teresa Walters, Yong Crosby, Rubin Baskir","doi":"10.1093/jamia/ocae167","DOIUrl":"10.1093/jamia/ocae167","url":null,"abstract":"<p><strong>Objectives: </strong>We describe new curriculum materials for engaging secondary school students in exploring the \"big data\" in the NIH All of Us Research Program's Public Data Browser and the co-design processes used to collaboratively develop the materials. We also describe the methods used to develop and validate assessment items for studying the efficacy of the materials for student learning as well as preliminary findings from these studies.</p><p><strong>Materials and methods: </strong>Secondary-level biology teachers from across the United States participated in a 2.5-day Co-design Summer Institute. After learning about the All of Us Research Program and its Data Browser, they collaboratively developed learning objectives and initial ideas for learning experiences related to exploring the Data Browser and big data. The Genetic Science Learning Center team at the University of Utah further developed the educators' ideas. Additional teachers and their students participated in classroom pilot studies to validate a 22-item instrument that assesses students' knowledge. Educators completed surveys about the materials and their experiences.</p><p><strong>Results: </strong>The \"Exploring Big Data with the All of Us Data Browser\" curriculum module includes 3 data exploration guides that engage students in using the Data Browser, 3 related multimedia pieces, and teacher support materials. Pilot testing showed substantial growth in students' understanding of key big data concepts and research applications.</p><p><strong>Discussion and conclusion: </strong>Our co-design process provides a model for educator engagement. The new curriculum module serves as a model for introducing secondary students to big data and precision medicine research by exploring diverse real-world datasets.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2837-2848"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564952","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":"Use of calibration to improve the precision of estimates obtained from All of Us data.","authors":"Vivian Hsing-Chun Wang, Julie Holm, José A Pagán","doi":"10.1093/jamia/ocae181","DOIUrl":"10.1093/jamia/ocae181","url":null,"abstract":"<p><strong>Objectives: </strong>To highlight the use of calibration weighting to improve the precision of estimates obtained from All of Us data and increase the return of value to communities from the All of Us Research Program.</p><p><strong>Materials and methods: </strong>We used All of Us (2017-2022) data and raking to obtain prevalence estimates in two examples: discrimination in medical settings (N = 41 875) and food insecurity (N = 82 266). Weights were constructed using known population proportions (age, sex, race/ethnicity, region of residence, annual household income, and home ownership) from the 2020 National Health Interview Survey.</p><p><strong>Results: </strong>About 37% of adults experienced discrimination in a medical setting. About 20% of adults who had not seen a doctor reported being food insecure compared with 14% of adults who regularly saw a doctor.</p><p><strong>Conclusions: </strong>Calibration using raking is cost-effective and may lead to more precise estimates when analyzing All of Us data.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2985-2988"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141564953","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":"Socioeconomic disparities in kidney transplant access for patients with end-stage kidney disease within the All of Us Research Program.","authors":"Jiayuan Wang, Kellie C Cho, Ekamol Tantisattamo","doi":"10.1093/jamia/ocae178","DOIUrl":"10.1093/jamia/ocae178","url":null,"abstract":"<p><strong>Objectives: </strong>Disparity in kidney transplant access has been demonstrated by a disproportionately low rate of kidney transplantation in socioeconomically disadvantaged patients. However, the information is not from national representative populations with end-stage kidney disease (ESKD). We aim to examine whether socioeconomic disparity for kidney transplant access exists by utilizing data from the All of Us Research Program.</p><p><strong>Materials and methods: </strong>We analyzed data of adult ESKD patients using the All of Us Researcher Workbench. The association of socioeconomic data including types of health insurance, levels of education, and household incomes with kidney transplant access was evaluated by multivariable logistic regression analysis adjusted by baseline demographic, medical comorbidities, and behavioral information.</p><p><strong>Results: </strong>Among 4078 adults with ESKD, mean diagnosis age was 54 and 51.64% were male. The majority had Medicare (39.6%), were non-graduate college (75.79%), and earned $10 000-24 999 annual income (20.16%). After adjusting for potential confounders, insurance status emerged as a significant predictor of kidney transplant access. Individuals covered by Medicaid (adjusted odds ratio [AOR] 0.45; 95% confidence interval [CI], 0.35-0.58; P-value < .001) or uninsured (AOR 0.21; 95% CI, 0.12-0.37; P-value < .001) exhibited lower odds of transplantation compared to those with private insurance.</p><p><strong>Discussion/conclusion: </strong>Our findings reveal the influence of insurance status and socioeconomic factors on access to kidney transplantation among ESKD patients. Addressing these disparities through expanded insurance coverage and improved healthcare access is vital for promoting equitable treatment and enhancing health outcomes in vulnerable populations.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2781-2788"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121002","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":"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":"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":" ","pages":"3001-3007"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","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":"Fair prediction of 2-year stroke risk in patients with atrial fibrillation.","authors":"Jifan Gao, Philip Mar, Zheng-Zheng Tang, Guanhua Chen","doi":"10.1093/jamia/ocae170","DOIUrl":"10.1093/jamia/ocae170","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.</p><p><strong>Materials and methods: </strong>Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation.</p><p><strong>Results: </strong>Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach.</p><p><strong>Discussion: </strong>Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice.</p><p><strong>Conclusions: </strong>Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2820-2828"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499494","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":"Cigarette smoking, e-cigarette use, and sociodemographic correlates of mental health and tobacco-related disease risk in the All of Us Research Program.","authors":"Thomas R Kirchner, Danning Tian, Jian Li, Pranjal Srivastava, Yihao Zheng","doi":"10.1093/jamia/ocae237","DOIUrl":"10.1093/jamia/ocae237","url":null,"abstract":"<p><strong>Significance: </strong>Research on the conditions under which electronic cigarette (EC) use produces a net reduction in the population harm attributable to combusted cigarette (CC) use requires the triangulation of information from cohort(s) of smokers, non-smokers, EC users, and dual-users of all varieties.</p><p><strong>Materials and methods: </strong>This project utilizes data from the All of Us Research Program to contrast a panel of wellness and disease-risk indicators across a range of self-reported tobacco-use profiles, including smokers, current, and former EC users. This article focuses on the tobacco use history and current tobacco use status among All of Us participants enrolled between May 2017 and February 2023 (Registered Controlled Tier Curated Data Repository [CDR] v7).</p><p><strong>Results: </strong>The present analytic sample included an unweighted total of N = 412 211 individuals with information on ever-use of both CC and EC. Among them, 155 901 individuals have a history of CC use, with 65 206 identified as current smokers. EC usage is reported by 64 002 individuals, with 16 619 being current users. Model predicted analyses identified distinct patterns in CC and EC usage across demographic and socioeconomic variables, with younger ages favoring ECs.</p><p><strong>Discussion: </strong>Age was observed to significantly affect EC usage, and gender differences reveal that males were significantly more likely to use CC and/or EC than females or African Americans of any gender. Higher educational achievement and income were associated with lower use of both CC and EC, while lower levels of mental health were observed to increase the likelihood of using CC and EC products.</p><p><strong>Conclusion: </strong>Findings suggest the potential for the All of Us Research Program for investigation of causal factors driving both behavioral use transitions and cessation outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2829-2836"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134296","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":"Health disparities in the risk of severe acidosis: real-world evidence from the All of Us cohort.","authors":"Allison E Gatz, Chenxi Xiong, Yao Chen, Shihui Jiang, Chi Mai Nguyen, Qianqian Song, Xiaochun Li, Pengyue Zhang, Michael T Eadon, Jing Su","doi":"10.1093/jamia/ocae256","DOIUrl":"10.1093/jamia/ocae256","url":null,"abstract":"<p><strong>Objective: </strong>To assess the health disparities across social determinants of health (SDoH) domains for the risk of severe acidosis independent of demographical and clinical factors.</p><p><strong>Materials and methods: </strong>A retrospective case-control study (n = 13 310, 1:4 matching) is performed using electronic health records (EHRs), SDoH surveys, and genomics data from the All of Us participants. The propensity score matching controls confounding effects due to EHR data availability. Conditional logistic regressions are used to estimate odds ratios describing associations between SDoHs and the risk of acidosis events, adjusted for demographic features, and clinical conditions.</p><p><strong>Results: </strong>Those with employer-provided insurance and those with Medicaid plans show dramatically different risks [adjusted odds ratio (AOR): 0.761 vs 1.41]. Low-income groups demonstrate higher risk (household income less than $25k, AOR: 1.3-1.57) than high-income groups ($100-$200k, AOR: 0.597-0.867). Other high-risk factors include impaired mobility (AOR: 1.32), unemployment (AOR: 1.32), renters (AOR: 1.41), other non-house-owners (AOR: 1.7), and house instability (AOR: 1.25). Education was negatively associated with acidosis risk.</p><p><strong>Discussion: </strong>Our work provides real-world evidence of the comprehensive health disparities due to socioeconomic and behavioral contributors in a cohort enriched in minority groups or underrepresented populations.</p><p><strong>Conclusions: </strong>SDoHs are strongly associated with systematic health disparities in the risk of severe metabolic acidosis. Types of health insurance, household income levels, housing status and stability, employment status, educational level, and mobility disability play significant roles after being adjusted for demographic features and clinical conditions. Comprehensive solutions are needed to improve equity in healthcare and reduce the risk of severe acidosis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2932-2939"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479225","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":"Engagement with health research summaries via digital communication to All of Us participants.","authors":"Janna Ter Meer, Royan Kamyar, Christina Orlovsky, Ting-Yang Hung, Tamara Benrey, Ethan Dinh-Luong, Giorgio Quer, Julia Moore Vogel","doi":"10.1093/jamia/ocae185","DOIUrl":"10.1093/jamia/ocae185","url":null,"abstract":"<p><strong>Objective: </strong>Summaries of health research can be a complementary way to return value to participants. We assess how research participants engage with summaries via email communication and how this can be improved.</p><p><strong>Materials and methods: </strong>We look at correlations between demographic subgroups and engagement in a longitudinal dataset of 305 626 participants (77% are classified as underrepresented in biomedical research) from the All of Us Research Program. We compare this against engagement with other program communications and use impact evaluations (N = 421 510) to measure the effect of tailoring communication by (1) eliciting content preferences, (2) Spanish focused content, (3) informational videos, and (4) article content in the email subject line.</p><p><strong>Results: </strong>Between March 2020 and October 2021, research summaries reached 67% of enrolled participants, outperforming other program communication (60%) and return of results (31%), which have a high uptake rate but have been extended to a subset of eligible participants. While all demographic subgroups engage with research summaries, participants with higher income, educational attainment, White, and older than 45 years open and click content most often. Surfacing article content in the email subject line and Spanish focused content had negative effects on engagement. Video and social media content and eliciting preferences led to a small directional increase in clicks.</p><p><strong>Discussion: </strong>Further individualization of tailoring efforts may be needed to drive larger engagement effects (eg, delivering multiple articles in line with stated preferences, expanding preference options). Our findings are likely a conservative representation of engagement effects, given the coarseness of our click rate measure.</p><p><strong>Conclusions: </strong>Health research summaries show promise as a way to return value to research participants, especially if individual-level results cannot be returned. Personalization of communication requires testing to determine whether efforts are having the expected effect.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2908-2915"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762142","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":"Returning value to the community through the All of Us Research Program Data Sandbox model.","authors":"Elizabeth Cohn, Frida Esther Kleiman, Shayaa Muhammad, S Scott Jones, Nakisa Pourkey, Louise Bier","doi":"10.1093/jamia/ocae174","DOIUrl":"10.1093/jamia/ocae174","url":null,"abstract":"<p><strong>Objective: </strong>The All of Us Research Program aims to return value to participants by developing research capacity in communities. We describe a novel set of introductory exercises (Data Sandboxes) and specialized trainings to orient researchers to the Researcher Workbench to foster health equity research.</p><p><strong>Materials and methods: </strong>We developed a tailored training to familiarize researchers with the All of Us Research Program: (1) orientation, (2) tailored \"data treasure hunt\" using the Public Data Browser, and (3) overview of the analyses tools and platform.</p><p><strong>Results: </strong>Participants' pre- and post-knowledge of the contents and structure of the All of Us dataset scores increased significantly after training. These trainings effectively engaged researchers in exploring this rich dataset.</p><p><strong>Conclusion: </strong>We describe ways of orienting and familiarizing a wide variety of researchers with the All of Us Research Program dataset, sparking their interest, and \"jump-starting\" their research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2980-2984"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793942","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}