Journal of the American Medical Informatics Association最新文献

{"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":"https://doi.org/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":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"https://doi.org/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":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.","authors":"Han Yang, Sicheng Zhou, Zexi Rao, Chen Zhao, Erjia Cui, Chetan Shenoy, Anne H Blaes, Nishitha Paidimukkala, Jinhua Wang, Jue Hou, Rui Zhang","doi":"10.1093/jamia/ocae199","DOIUrl":"https://doi.org/10.1093/jamia/ocae199","url":null,"abstract":"<p><strong>Objective: </strong>This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables.</p><p><strong>Materials and methods: </strong>We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period.</p><p><strong>Results: </strong>The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors.</p><p><strong>Discussion: </strong>The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness.</p><p><strong>Conclusion: </strong>This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767875","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":"An evaluation of the All of Us Research Program database to examine cumulative stress.","authors":"Shawna Beese, Demetrius A Abshire, Trey L DeJong, Jason T Carbone","doi":"10.1093/jamia/ocae201","DOIUrl":"https://doi.org/10.1093/jamia/ocae201","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US).</p><p><strong>Materials and methods: </strong>We evaluated the All of Us database to determine sample size significance for original-10 allostatic load biomarkers, Allostatic Load Index-5 (ALI-5), Allostatic Load Five, and Cohen's Perceived Stress Scale (PSS). We conducted a priori, post hoc, and sensitivity power analyses to determine sample sizes for conducting null hypothesis significance tests.</p><p><strong>Results: </strong>The maximum number of responses available for each measure is 21 participants for the original-10 allostatic load biomarkers, 150 for the ALI-5, 22 476 for Allostatic Load Five, and n = 90 583 for the PSS.</p><p><strong>Discussion: </strong>The NIH All of Us Research Program is well-suited for studying allostatic load using the Allostatic Load Five and psychological stress using PSS.</p><p><strong>Conclusion: </strong>Improving biomarker data collection in All of Us will facilitate more nuanced examinations of allostatic load among US adults.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767874","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":"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":"https://doi.org/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":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"allofus: an R package to facilitate use of the All of Us Researcher Workbench.","authors":"Louisa H Smith, Robert Cavanaugh","doi":"10.1093/jamia/ocae198","DOIUrl":"https://doi.org/10.1093/jamia/ocae198","url":null,"abstract":"<p><strong>Objectives: </strong>Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies.</p><p><strong>Target audience: </strong>All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package.</p><p><strong>Scope: </strong>We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753237","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":"Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research.","authors":"Louisa H Smith, Wanjiang Wang, Brianna Keefe-Oates","doi":"10.1093/jamia/ocae195","DOIUrl":"https://doi.org/10.1093/jamia/ocae195","url":null,"abstract":"<p><strong>Objectives: </strong>The National Institutes of Health's All of Us Research Program addresses gaps in biomedical research by collecting health data from diverse populations. Pregnant individuals have historically been underrepresented in biomedical research, and pregnancy-related research is often limited by data availability, sample size, and inadequate representation of the diversity of pregnant people. All of Us integrates a wealth of health-related data, providing a unique opportunity to conduct comprehensive pregnancy-related research. We aimed to identify pregnancy episodes with high-quality electronic health record (EHR) data in All of Us Research Program data and evaluate the program's utility for pregnancy-related research.</p><p><strong>Materials and methods: </strong>We used a previously published algorithm to identify pregnancy episodes in All of Us EHR data. We described these pregnancies, validated them with All of Us survey data, and compared them to national statistics.</p><p><strong>Results: </strong>Our study identified 18 970 pregnancy episodes from 14 234 participants; other possible pregnancy episodes had low-quality or insufficient data. Validation against people who reported a current pregnancy on an All of Us survey found low false positive and negative rates. Demographics were similar in some respects to national data; however, Asian-Americans were underrepresented, and older, highly educated pregnant people were overrepresented.</p><p><strong>Discussion: </strong>Our approach demonstrates the capacity of All of Us to support pregnancy research and reveals the diversity of the pregnancy cohort. However, we noted an underrepresentation among some demographics. Other limitations include measurement error in gestational age and limited data on non-live births.</p><p><strong>Conclusion: </strong>The wide variety of data in the All of Us program, encompassing EHR, survey, genomic, and fitness tracker data, offers a valuable resource for studying pregnancy, yet care must be taken to avoid biases.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753239","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":"Model-based estimation of individual-level social determinants of health and its applications in All of Us.","authors":"Bo Young Kim, Rebecca Anthopolos, Hyungrok Do, Judy Zhong","doi":"10.1093/jamia/ocae168","DOIUrl":"10.1093/jamia/ocae168","url":null,"abstract":"<p><strong>Objectives: </strong>We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.</p><p><strong>Materials and methods: </strong>Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.</p><p><strong>Results: </strong>Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.</p><p><strong>Discussion and conclusion: </strong>The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604437","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":"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":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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":"https://doi.org/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":""},"PeriodicalIF":4.7,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}