Alison W Xin, Dylan M Nielson, Karolin Rose Krause, Guilherme Fiorini, Nick Midgley, Francisco Pereira, Juan Antonio Lossio-Ventura
Objective: We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative enough to accurately label these experiences.
Materials and methods: Data were drawn from interviews, where text segments were annotated with different outcome labels. Five different open-source LLMs were evaluated to classify outcomes from the coding framework. Classification experiments were carried out in the original interview transcripts. Furthermore, we repeated those experiments for versions of the data produced by breaking those segments into conversation turns, or keeping non-interviewer utterances (monologues).
Results: We used classification models to predict 31 outcomes and 8 derived labels, for 3 different text segmentations. Area under the ROC curve scores ranged between 0.6 and 0.9 for the original segmentation and 0.7 and 1.0 for the monologues and turns.
Discussion: LLM-based classification models could identify outcomes important to adolescents, such as friendships or academic and vocational functioning, in text transcripts of patient interviews. By using clinical data, we also aim to better generalize to clinical settings compared to studies based on public social media data.
Conclusion: Our results demonstrate that fine-grained therapy outcome coding in psychotherapeutic text is feasible, and can be used to support the quantification of important outcomes for downstream uses.
{"title":"Using large language models to detect outcomes in qualitative studies of adolescent depression.","authors":"Alison W Xin, Dylan M Nielson, Karolin Rose Krause, Guilherme Fiorini, Nick Midgley, Francisco Pereira, Juan Antonio Lossio-Ventura","doi":"10.1093/jamia/ocae298","DOIUrl":"https://doi.org/10.1093/jamia/ocae298","url":null,"abstract":"<p><strong>Objective: </strong>We aim to use large language models (LLMs) to detect mentions of nuanced psychotherapeutic outcomes and impacts than previously considered in transcripts of interviews with adolescent depression. Our clinical authors previously created a novel coding framework containing fine-grained therapy outcomes beyond the binary classification (eg, depression vs control) based on qualitative analysis embedded within a clinical study of depression. Moreover, we seek to demonstrate that embeddings from LLMs are informative enough to accurately label these experiences.</p><p><strong>Materials and methods: </strong>Data were drawn from interviews, where text segments were annotated with different outcome labels. Five different open-source LLMs were evaluated to classify outcomes from the coding framework. Classification experiments were carried out in the original interview transcripts. Furthermore, we repeated those experiments for versions of the data produced by breaking those segments into conversation turns, or keeping non-interviewer utterances (monologues).</p><p><strong>Results: </strong>We used classification models to predict 31 outcomes and 8 derived labels, for 3 different text segmentations. Area under the ROC curve scores ranged between 0.6 and 0.9 for the original segmentation and 0.7 and 1.0 for the monologues and turns.</p><p><strong>Discussion: </strong>LLM-based classification models could identify outcomes important to adolescents, such as friendships or academic and vocational functioning, in text transcripts of patient interviews. By using clinical data, we also aim to better generalize to clinical settings compared to studies based on public social media data.</p><p><strong>Conclusion: </strong>Our results demonstrate that fine-grained therapy outcome coding in psychotherapeutic text is feasible, and can be used to support the quantification of important outcomes for downstream uses.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814632","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}
Izabelle Humes, Cathy Shyr, Moira Dillon, Zhongjie Liu, Jennifer Peterson, Chris St Jeor, Jacqueline Malkes, Hiral Master, Brandy Mapes, Romuladus Azuine, Nakia Mack, Bassent Abdelbary, Joyonna Gamble-George, Emily Goldmann, Stephanie Cook, Fatemeh Choupani, Rubin Baskir, Sydney McMaster, Chris Lunt, Karriem Watson, Minnkyong Lee, Sophie Schwartz, Ruchi Munshi, David Glazer, Eric Banks, Anthony Philippakis, Melissa Basford, Dan Roden, Paul A Harris
Objectives: The All of Us Research Program is a precision medicine initiative aimed at establishing a vast, diverse biomedical database accessible through a cloud-based data analysis platform, the Researcher Workbench (RW). Our goal was to empower the research community by co-designing the implementation of SAS in the RW alongside researchers to enable broader use of All of Us data.
Materials and methods: Researchers from various fields and with different SAS experience levels participated in co-designing the SAS implementation through user experience interviews.
Results: Feedback and lessons learned from user testing informed the final design of the SAS application.
Discussion: The co-design approach is critical for reducing technical barriers, broadening All of Us data use, and enhancing the user experience for data analysis on the RW.
Conclusion: Our co-design approach successfully tailored the implementation of the SAS application to researchers' needs. This approach may inform future software implementations on the RW.
目标:我们所有人研究计划是一项精准医学计划,旨在建立一个庞大、多样的生物医学数据库,可通过基于云的数据分析平台--研究者工作台(RW)进行访问。我们的目标是通过与研究人员共同设计 RW 中 SAS 的实施来增强研究社区的能力,从而更广泛地使用 All of Us 数据:来自不同领域、具有不同 SAS 经验水平的研究人员通过用户体验访谈参与了 SAS 实施的共同设计:结果:从用户测试中获得的反馈和经验教训为 SAS 应用程序的最终设计提供了依据:讨论:共同设计方法对于减少技术障碍、扩大 "我们所有人 "数据的使用范围以及增强用户在 RW 上进行数据分析的体验至关重要:我们的共同设计方法成功地使 SAS 应用程序的实施符合研究人员的需求。这种方法可为未来在 RW 上实施软件提供参考。
{"title":"Empowering the biomedical research community: Innovative SAS deployment on the All of Us Researcher Workbench.","authors":"Izabelle Humes, Cathy Shyr, Moira Dillon, Zhongjie Liu, Jennifer Peterson, Chris St Jeor, Jacqueline Malkes, Hiral Master, Brandy Mapes, Romuladus Azuine, Nakia Mack, Bassent Abdelbary, Joyonna Gamble-George, Emily Goldmann, Stephanie Cook, Fatemeh Choupani, Rubin Baskir, Sydney McMaster, Chris Lunt, Karriem Watson, Minnkyong Lee, Sophie Schwartz, Ruchi Munshi, David Glazer, Eric Banks, Anthony Philippakis, Melissa Basford, Dan Roden, Paul A Harris","doi":"10.1093/jamia/ocae216","DOIUrl":"10.1093/jamia/ocae216","url":null,"abstract":"<p><strong>Objectives: </strong>The All of Us Research Program is a precision medicine initiative aimed at establishing a vast, diverse biomedical database accessible through a cloud-based data analysis platform, the Researcher Workbench (RW). Our goal was to empower the research community by co-designing the implementation of SAS in the RW alongside researchers to enable broader use of All of Us data.</p><p><strong>Materials and methods: </strong>Researchers from various fields and with different SAS experience levels participated in co-designing the SAS implementation through user experience interviews.</p><p><strong>Results: </strong>Feedback and lessons learned from user testing informed the final design of the SAS application.</p><p><strong>Discussion: </strong>The co-design approach is critical for reducing technical barriers, broadening All of Us data use, and enhancing the user experience for data analysis on the RW.</p><p><strong>Conclusion: </strong>Our co-design approach successfully tailored the implementation of the SAS application to researchers' needs. This approach may inform future software implementations on the RW.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2994-3000"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631098/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972205","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}
Han Yang, Sicheng Zhou, Zexi Rao, Chen Zhao, Erjia Cui, Chetan Shenoy, Anne H Blaes, Nishitha Paidimukkala, Jinhua Wang, Jue Hou, Rui Zhang
Objective: 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.
Materials and methods: 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.
Results: 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.
Discussion: 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.
Conclusion: 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.
研究目的本研究利用 "我们所有人研究计划"(All of Us)数据集的丰富多样性,设计出乳腺癌(BC)幸存者心血管疾病(CVD)的预测模型。这项工作的核心是创建一个强大的数据集成管道,该管道可综合电子健康记录(EHR)、患者调查和基因组数据,同时维护不同人口统计学变量之间的公平性:我们开发了一个通用数据处理管道,用于处理和合并 "我们所有人 "数据集的异构数据源,解决数据缺失和数据差异问题,并将不同的数据模式整合到一个连贯的分析框架中。利用包括电子病历、生活方式和健康的社会决定因素 (SDoH) 数据在内的复合特征集,我们采用自适应拉索和随机森林回归模型来预测 6 种心血管疾病的结果。在 10 年的时间里,我们使用 c 指数和随时间变化的接收者工作特征曲线下面积对模型进行了评估:结果:自适应套索模型在大多数心血管疾病结果中表现出一致的性能,而随机森林模型在预测短暂性脑缺血发作等结果时表现尤为突出,因为它结合了完整的多模型特征集。特征重要性分析表明,年龄和既往冠心病事件是预测心血管疾病结果的主要因素,而SDoH聚类标签则突出了社会因素的细微影响:基于 Cox 的预测模型和随机森林回归模型的开发代表了 "我们所有人 "在整合电子病历和患者调查以提高精准医疗方面的广泛应用。SDoH聚类标签的加入揭示了社会行为因素对患者预后的重大影响,强调了预测模型中综合健康决定因素的重要性。尽管取得了这些进步,但仍存在一些局限性,包括未纳入基因数据、心血管疾病分类过宽,以及需要进行公平性分析以确保模型在不同人群中的公平表现。未来的工作应完善临床和社会变量测量,采用先进的估算技术,并探索更多的预测算法,以提高模型的精确性和公平性:本研究证明了 "我们所有人 "的多样化数据集在开发多模式预测模型以预测不列颠哥伦比亚省幸存者心血管疾病方面的作用。数据整合管道和后续预测模型为未来个性化医疗保健研究奠定了方法论基础。
{"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":"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":"2800-2810"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631116/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fred Willie Zametkin LaPolla, Marco Barber Grossi, Sharon Chen, Tai Wei Guo, Kathryn Havranek, Olivia Jebb, Minh Thu Nguyen, Sneha Panganamamula, Noah Smith, Shree Sundaresh, Jonathan Yu, Gabrielle Mayer
Objectives: The goal of this case report is to detail experiences and challenges experienced in the training of Primary Care residents in secondary analysis using All of Us Researcher Workbench. At our large, urban safety net hospital, Primary Care/Internal Medicine residents in their third year undergo a research intensive block, the Research Practicum, where they work as a team to conduct secondary data analysis on a dataset with faculty facilitation. In 2023, this research block focused on use of the All of Us Researcher Workbench for secondary data analysis.
Materials and methods: Two groups of 5 residents underwent training to access the All of Us Researcher Workbench, and each group explored available data with a faculty facilitator and generated original research questions. Two blocks of residents successfully completed their research blocks and created original presentations on "social isolation and A1C" levels and "medical discrimination and diabetes management."
Results: Departmental faculty were satisfied with the depth of learning and data exploration. In focus groups, some residents noted that for those without interest in performing research, the activity felt extraneous to their career goals, while others were glad for the opportunity to publish. In both blocks, residents highlighted dissatisfaction with the degree to which the All of Us Researcher Workbench was representative of patients they encounter in a large safety net hospital.
Discussion: Using the All of Us Researcher Workbench provided residents with an opportunity to explore novel questions in a massive data source. Many residents however noted that because the population described in the All of Us Researcher Workbench appeared to be more highly educated and less racially diverse than patients they encounter in their practice, research may be hard to generalize in a community health context. Additionally, given that the data required knowledge of 1 of 2 code-based data analysis languages (R or Python) and work within an idiosyncratic coding environment, residents were heavily reliant on a faculty facilitator to assist with analysis.
Conclusion: Using the All of Us Researcher Workbench for research training allowed residents to explore novel questions and gain first-hand exposure to opportunities and challenges in secondary data analysis.
{"title":"All of whom? Limitations encountered using All of Us Researcher Workbench in a Primary Care residents secondary data analysis research training block.","authors":"Fred Willie Zametkin LaPolla, Marco Barber Grossi, Sharon Chen, Tai Wei Guo, Kathryn Havranek, Olivia Jebb, Minh Thu Nguyen, Sneha Panganamamula, Noah Smith, Shree Sundaresh, Jonathan Yu, Gabrielle Mayer","doi":"10.1093/jamia/ocae162","DOIUrl":"10.1093/jamia/ocae162","url":null,"abstract":"<p><strong>Objectives: </strong>The goal of this case report is to detail experiences and challenges experienced in the training of Primary Care residents in secondary analysis using All of Us Researcher Workbench. At our large, urban safety net hospital, Primary Care/Internal Medicine residents in their third year undergo a research intensive block, the Research Practicum, where they work as a team to conduct secondary data analysis on a dataset with faculty facilitation. In 2023, this research block focused on use of the All of Us Researcher Workbench for secondary data analysis.</p><p><strong>Materials and methods: </strong>Two groups of 5 residents underwent training to access the All of Us Researcher Workbench, and each group explored available data with a faculty facilitator and generated original research questions. Two blocks of residents successfully completed their research blocks and created original presentations on \"social isolation and A1C\" levels and \"medical discrimination and diabetes management.\"</p><p><strong>Results: </strong>Departmental faculty were satisfied with the depth of learning and data exploration. In focus groups, some residents noted that for those without interest in performing research, the activity felt extraneous to their career goals, while others were glad for the opportunity to publish. In both blocks, residents highlighted dissatisfaction with the degree to which the All of Us Researcher Workbench was representative of patients they encounter in a large safety net hospital.</p><p><strong>Discussion: </strong>Using the All of Us Researcher Workbench provided residents with an opportunity to explore novel questions in a massive data source. Many residents however noted that because the population described in the All of Us Researcher Workbench appeared to be more highly educated and less racially diverse than patients they encounter in their practice, research may be hard to generalize in a community health context. Additionally, given that the data required knowledge of 1 of 2 code-based data analysis languages (R or Python) and work within an idiosyncratic coding environment, residents were heavily reliant on a faculty facilitator to assist with analysis.</p><p><strong>Conclusion: </strong>Using the All of Us Researcher Workbench for research training allowed residents to explore novel questions and gain first-hand exposure to opportunities and challenges in secondary data analysis.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3008-3012"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452050","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}
Theresa A Koleck, Caitlin Dreisbach, Chen Zhang, Susan Grayson, Maichou Lor, Zhirui Deng, Alex Conway, Peter D R Higgins, Suzanne Bakken
Objectives: Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.
Target audience: This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.
Scope: We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.
目标:将健康的社会决定因素纳入健康结果研究将使研究人员能够研究健康不平等问题。我们所有人研究计划有可能成为丰富的健康社会决定因素数据来源。然而,我们需要用户友好型的建议来对 "我们所有人的社会决定因素健康调查 "进行评分和解释,以便通过提高研究人员使用 "我们所有人的研究中心 "研究人员工作台的能力来为社区创造价值。我们创建了一份用户指南,旨在为研究人员提供健康状况社会决定因素调查的概述、对参与者回复进行评分和解释的建议,以及易于执行的 R 和 Python 函数:本用户指南的目标受众是 "我们所有人 "研究中心(All of Us Research Hub)研究人员工作台(Researcher Workbench)的注册用户,该工作台是一个支持 "我们所有人 "数据分析的云平台,目前正在使用或计划使用健康社会决定因素调查进行分析:我们介绍了作为健康社会决定因素调查一部分而评估的 14 个构造,并总结了构造的可操作性。我们提供了 30 项参考文献的建议,用于对参与者的回答进行评分和解释分数,其中 8 个构像有多个选项。然后,我们将通过 R 和 Python 函数示例来重新标注回答和结构式评分,这些函数可直接在研究者工作台的 Jupyter Notebook 或 RStudio 中实现。完整的源代码可在补充文件和 GitHub 中获取。最后,我们将讨论与研究人员健康社会决定因素调查相关的心理测量注意事项。
{"title":"User guide for Social Determinants of Health Survey data in the All of Us Research Program.","authors":"Theresa A Koleck, Caitlin Dreisbach, Chen Zhang, Susan Grayson, Maichou Lor, Zhirui Deng, Alex Conway, Peter D R Higgins, Suzanne Bakken","doi":"10.1093/jamia/ocae214","DOIUrl":"10.1093/jamia/ocae214","url":null,"abstract":"<p><strong>Objectives: </strong>Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.</p><p><strong>Target audience: </strong>This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.</p><p><strong>Scope: </strong>We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3032-3041"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631056/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082352","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 communities from the All of Us Research Program through innovative approaches for data use, analysis, dissemination, and research capacity building.","authors":"Suzanne Bakken, Elaine Sang, Berry de Brujin","doi":"10.1093/jamia/ocae276","DOIUrl":"10.1093/jamia/ocae276","url":null,"abstract":"","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"31 12","pages":"2773-2780"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808410","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}
Ivann Agapito, Tu Hoang, Michael Sayer, Ali Naqvi, Pranav M Patel, Aya F Ozaki
Importance and objective: Identifying sources of sex-based disparities is the first step in improving clinical outcomes for female patients. Using All of Us data, we examined the association of biological sex with cost-related medication adherence (CRMA) issues in patients with cardiovascular comorbidities.
Materials and methods: Retrospective data collection identified the following patients: 18 and older, completing personal medical history surveys, having hypertension (HTN), ischemic heart disease (IHD), or heart failure (HF) with medication use history consistent with these diagnoses. Implementing univariable and adjusted logistic regression, we assessed the influence of biological sex on 7 different patient-reported CRMA outcomes within HTN, IHD, and HF patients.
Results: Our study created cohorts of HTN (n = 3891), IHD (n = 5373), and HF (n = 2151) patients having CRMA outcomes data. Within each cohort, females were significantly more likely to report various cost-related medication issues: being unable to afford medications (HTN hazards ratio [HR]: 1.68, confidence interval [CI]: 1.33-2.13; IHD HR: 2.33, CI: 1.72-3.16; HF HR: 1.82, CI: 1.22-2.71), skipping doses (HTN HR: 1.76, CI: 1.30-2.39; IHD HR: 2.37, CI: 1.69-3.64; HF HR: 3.15, CI: 1.87-5.31), taking less medication (HTN HR: 1.86, CI: 1.37-2.45; IHD HR: 2.22, CI: 1.53-3.22; HF HR: 2.99, CI: 1.78-5.02), delaying filling prescriptions (HTN HR: 1.83, CI: 1.43-2.39; IHD HR: 2.02, CI: 1.48-2.77; HF HR: 2.99, CI: 1.79-5.03), and asking for lower cost medications (HTN HR: 1.41, CI: 1.16-1.72; IHD HR: 1.75, CI: 1.37-2.22; HF HR: 1.61, CI: 1.14-2.27).
Discussion and conclusion: Our results clearly demonstrate CRMA issues disproportionately affect female patients with cardiovascular comorbidities, which may contribute to the larger sex-based disparities in cardiovascular care. These findings call for targeted interventions and strategies to address these disparities and ensure equitable access to cardiovascular medications and care for all patients.
{"title":"Sex-based disparities with cost-related medication adherence issues in patients with hypertension, ischemic heart disease, and heart failure.","authors":"Ivann Agapito, Tu Hoang, Michael Sayer, Ali Naqvi, Pranav M Patel, Aya F Ozaki","doi":"10.1093/jamia/ocae203","DOIUrl":"10.1093/jamia/ocae203","url":null,"abstract":"<p><strong>Importance and objective: </strong>Identifying sources of sex-based disparities is the first step in improving clinical outcomes for female patients. Using All of Us data, we examined the association of biological sex with cost-related medication adherence (CRMA) issues in patients with cardiovascular comorbidities.</p><p><strong>Materials and methods: </strong>Retrospective data collection identified the following patients: 18 and older, completing personal medical history surveys, having hypertension (HTN), ischemic heart disease (IHD), or heart failure (HF) with medication use history consistent with these diagnoses. Implementing univariable and adjusted logistic regression, we assessed the influence of biological sex on 7 different patient-reported CRMA outcomes within HTN, IHD, and HF patients.</p><p><strong>Results: </strong>Our study created cohorts of HTN (n = 3891), IHD (n = 5373), and HF (n = 2151) patients having CRMA outcomes data. Within each cohort, females were significantly more likely to report various cost-related medication issues: being unable to afford medications (HTN hazards ratio [HR]: 1.68, confidence interval [CI]: 1.33-2.13; IHD HR: 2.33, CI: 1.72-3.16; HF HR: 1.82, CI: 1.22-2.71), skipping doses (HTN HR: 1.76, CI: 1.30-2.39; IHD HR: 2.37, CI: 1.69-3.64; HF HR: 3.15, CI: 1.87-5.31), taking less medication (HTN HR: 1.86, CI: 1.37-2.45; IHD HR: 2.22, CI: 1.53-3.22; HF HR: 2.99, CI: 1.78-5.02), delaying filling prescriptions (HTN HR: 1.83, CI: 1.43-2.39; IHD HR: 2.02, CI: 1.48-2.77; HF HR: 2.99, CI: 1.79-5.03), and asking for lower cost medications (HTN HR: 1.41, CI: 1.16-1.72; IHD HR: 1.75, CI: 1.37-2.22; HF HR: 1.61, CI: 1.14-2.27).</p><p><strong>Discussion and conclusion: </strong>Our results clearly demonstrate CRMA issues disproportionately affect female patients with cardiovascular comorbidities, which may contribute to the larger sex-based disparities in cardiovascular care. These findings call for targeted interventions and strategies to address these disparities and ensure equitable access to cardiovascular medications and care for all patients.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2924-2931"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861446","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}
Objectives: Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools.
Materials and methods: Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers.
Results: Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied.
Discussion: Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools.
Conclusion: The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.
{"title":"Communicating research findings as a return of value to All of Us Research Program participants: insights from staff at Federally Qualified Health Centers.","authors":"Kathryn P Smith, Jenn Holmes, Jennifer Shelley","doi":"10.1093/jamia/ocae207","DOIUrl":"10.1093/jamia/ocae207","url":null,"abstract":"<p><strong>Objectives: </strong>Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools.</p><p><strong>Materials and methods: </strong>Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers.</p><p><strong>Results: </strong>Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied.</p><p><strong>Discussion: </strong>Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools.</p><p><strong>Conclusion: </strong>The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2962-2967"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019381","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}
Suma K Thareja, Xin Yang, Paramita Basak Upama, Aziz Abdullah, Shary Pérez Torres, Linda Jackson Cocroft, Michael Bubolz, Kari McGaughey, Xuelin Lou, Sailaja Kamaraju, Sheikh Iqbal Ahamed, Praveen Madiraju, Anne E Kwitek, Jeffrey Whittle, Zeno Franco
Objective: The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities.
Materials and methods: We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG). An expert community engagement scientist facilitated 6 SIG meetings over the year, explicitly fostering openness and flexibility during conversations. We qualitatively analyzed discussions using a social movement framework tailored for community-based participatory research (CBPR) mobilization.
Results: The SIG evolved through CBPR stages of emergence, coalescence, momentum, and maintenance/integration. Researchers prioritized community needs above personal academic interests while community members kept discussions focused on tangible return of value to communities. One key outcome includes SIG-driven shifts in programmatic and research priorities of the All of Us Research Program in Southeastern Wisconsin. One major challenge was building equitable conversations that balanced scientific rigor and community understanding.
Discussion: Our approach allowed for a rich dialogue to emerge. Points of connection and disconnection between community members and scientists offered important guidance for emerging areas of genomic inquiry.
Conclusion: Our study presents a robust foundation for future efforts to engage diverse communities in CBPR, particularly on healthcare concerns affecting UBR communities.
目标:美国国立卫生研究院(NIH)的 "我们所有人研究计划"(All of Us Research Program)旨在推动个性化医疗的发展,该计划不仅要将患者记录、调查和基因组数据联系起来,还要让参与者参与进来,尤其是那些传统上在生物医学研究领域代表性不足的群体(UBR)。本研究详细介绍了科学家与社区成员(包括许多来自有色人种社区的成员)之间的对话是如何影响当地研究重点的:我们从社区和参与者咨询委员会中招募了地区定量、基础和临床科学家以及对 "我们所有人 "研究有兴趣的社区成员,作为特别兴趣小组(SIG)的成员。在这一年中,一位社区参与科学家专家主持了 6 次 SIG 会议,明确提出要在对话中培养开放性和灵活性。我们使用为社区参与式研究(CBPR)动员量身定制的社会运动框架对讨论进行了定性分析:结果:SIG 经历了 CBPR 的兴起、凝聚、动力和维持/整合阶段。研究人员将社区需求置于个人学术利益之上,而社区成员则将讨论重点放在对社区的实际价值回报上。其中一项重要成果包括,在 SIG 的推动下,威斯康星州东南部的 "我们大家 "研究计划的计划和研究重点发生了变化。一个主要挑战是建立公平的对话,平衡科学的严谨性和社区的理解:我们的方法使丰富的对话得以出现。社区成员与科学家之间的联系点和脱节点为基因组研究的新兴领域提供了重要指导:我们的研究为今后让不同社区参与 CBPR,特别是影响 UBR 社区的医疗保健问题奠定了坚实的基础。
{"title":"Equitable community-based participatory research engagement with communities of color drives All of Us Wisconsin genomic research priorities.","authors":"Suma K Thareja, Xin Yang, Paramita Basak Upama, Aziz Abdullah, Shary Pérez Torres, Linda Jackson Cocroft, Michael Bubolz, Kari McGaughey, Xuelin Lou, Sailaja Kamaraju, Sheikh Iqbal Ahamed, Praveen Madiraju, Anne E Kwitek, Jeffrey Whittle, Zeno Franco","doi":"10.1093/jamia/ocae265","DOIUrl":"10.1093/jamia/ocae265","url":null,"abstract":"<p><strong>Objective: </strong>The NIH All of Us Research Program aims to advance personalized medicine by not only linking patient records, surveys, and genomic data but also engaging with participants, particularly from groups traditionally underrepresented in biomedical research (UBR). This study details how the dialogue between scientists and community members, including many from communities of color, shaped local research priorities.</p><p><strong>Materials and methods: </strong>We recruited area quantitative, basic, and clinical scientists as well as community members from our Community and Participant Advisory Boards with a predetermined interest in All of Us research as members of a Special Interest Group (SIG). An expert community engagement scientist facilitated 6 SIG meetings over the year, explicitly fostering openness and flexibility during conversations. We qualitatively analyzed discussions using a social movement framework tailored for community-based participatory research (CBPR) mobilization.</p><p><strong>Results: </strong>The SIG evolved through CBPR stages of emergence, coalescence, momentum, and maintenance/integration. Researchers prioritized community needs above personal academic interests while community members kept discussions focused on tangible return of value to communities. One key outcome includes SIG-driven shifts in programmatic and research priorities of the All of Us Research Program in Southeastern Wisconsin. One major challenge was building equitable conversations that balanced scientific rigor and community understanding.</p><p><strong>Discussion: </strong>Our approach allowed for a rich dialogue to emerge. Points of connection and disconnection between community members and scientists offered important guidance for emerging areas of genomic inquiry.</p><p><strong>Conclusion: </strong>Our study presents a robust foundation for future efforts to engage diverse communities in CBPR, particularly on healthcare concerns affecting UBR communities.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2940-2951"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142512053","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}
Brandy M Mapes, Rachele S Peterson, Karriem Watson, Melissa Basford, Elizabeth Cohn, Paul A Harris, Joshua C Denny
Objectives: The All of Us Research Program harnesses advances in technology, science, and engagement for precision medicine research. We describe informatics innovations which support that goal and return value to the participant cohort and community.
Materials and methods: Research data from the All of Us Research Program are available to authorized users on the All of Us Researcher Workbench. We describe the technical infrastructure that enables data access and usage for researchers. Participants are considered partners. To ensure return of value, we outline participant access to information.
Results: The All of Us Research Hub allows broad access to data, regardless of background. The innovations described are rooted in the program's core values: participation is open and reflects the diversity of the United States; participants are partners and have access to their information; transparency, security, and privacy are of the highest importance; data are broadly accessible; and the program promotes positive change. We assess research impact and reflect on how All of Us can increase existing return of value to participant communities through future informatics advancements.
Discussion: The program will continue to support efforts to ensure equitable access to data and return of value to participants. Looking ahead, we invite the community to join us.
Conclusion: All of Us research findings can change clinical care, inform guidelines, and set a new bar for data sharing. The ultimate return of value is better care for all.
{"title":"Informatics innovation to provide return of value to participant communities in the All of Us Research Program.","authors":"Brandy M Mapes, Rachele S Peterson, Karriem Watson, Melissa Basford, Elizabeth Cohn, Paul A Harris, Joshua C Denny","doi":"10.1093/jamia/ocae264","DOIUrl":"10.1093/jamia/ocae264","url":null,"abstract":"<p><strong>Objectives: </strong>The All of Us Research Program harnesses advances in technology, science, and engagement for precision medicine research. We describe informatics innovations which support that goal and return value to the participant cohort and community.</p><p><strong>Materials and methods: </strong>Research data from the All of Us Research Program are available to authorized users on the All of Us Researcher Workbench. We describe the technical infrastructure that enables data access and usage for researchers. Participants are considered partners. To ensure return of value, we outline participant access to information.</p><p><strong>Results: </strong>The All of Us Research Hub allows broad access to data, regardless of background. The innovations described are rooted in the program's core values: participation is open and reflects the diversity of the United States; participants are partners and have access to their information; transparency, security, and privacy are of the highest importance; data are broadly accessible; and the program promotes positive change. We assess research impact and reflect on how All of Us can increase existing return of value to participant communities through future informatics advancements.</p><p><strong>Discussion: </strong>The program will continue to support efforts to ensure equitable access to data and return of value to participants. Looking ahead, we invite the community to join us.</p><p><strong>Conclusion: </strong>All of Us research findings can change clinical care, inform guidelines, and set a new bar for data sharing. The ultimate return of value is better care for all.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":"31 12","pages":"3042-3046"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142808407","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}