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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amanda M Lam, Mariana C Singletary, Theresa Cullen
Objective: This communication presents the results of defining a tribal health jurisdiction by a combination of tribal affiliation and case address.
Methods: Through a county-tribal partnership, GIS software and custom code were used to extract tribal data from county data by identifying reservation addresses in county extracts of COVID-19 case records from December 30, 2019, to December 31, 2022 (n = 374,653) and COVID-19 vaccination records from December 1, 2020, to April 18, 2023 (n = 2,355,058).
Results: The tool identified 1.91 times as many case records and 3.76 times as many vaccination records as filtering by tribal affiliation alone.
Discussion and conclusion: This method of identifying communities by patient address, in combination with tribal affiliation and enrollment, can help tribal health jurisdictions attain equitable access to public health data, when done in partnership with a data sharing agreement. This methodology has potential applications for other populations underrepresented in public health and clinical research.
{"title":"A GIS software-based method to identify public health data belonging to address-defined communities.","authors":"Amanda M Lam, Mariana C Singletary, Theresa Cullen","doi":"10.1093/jamia/ocae235","DOIUrl":"https://doi.org/10.1093/jamia/ocae235","url":null,"abstract":"<p><strong>Objective: </strong>This communication presents the results of defining a tribal health jurisdiction by a combination of tribal affiliation and case address.</p><p><strong>Methods: </strong>Through a county-tribal partnership, GIS software and custom code were used to extract tribal data from county data by identifying reservation addresses in county extracts of COVID-19 case records from December 30, 2019, to December 31, 2022 (n = 374,653) and COVID-19 vaccination records from December 1, 2020, to April 18, 2023 (n = 2,355,058).</p><p><strong>Results: </strong>The tool identified 1.91 times as many case records and 3.76 times as many vaccination records as filtering by tribal affiliation alone.</p><p><strong>Discussion and conclusion: </strong>This method of identifying communities by patient address, in combination with tribal affiliation and enrollment, can help tribal health jurisdictions attain equitable access to public health data, when done in partnership with a data sharing agreement. This methodology has potential applications for other populations underrepresented in public health and clinical research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057033","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}
Anastasia Krithara, Anastasios Nentidis, Eirini Vandorou, Georgios Katsimpras, Yannis Almirantis, Magda Arnal, Adomas Bunevicius, Eulalia Farre-Maduell, Maya Kassiss, Vasileios Konstantakos, Sherri Matis-Mitchell, Dimitris Polychronopoulos, Jesus Rodriguez-Pascual, Eleftherios G Samaras, Martina Samiotaki, Despina Sanoudou, Aspasia Vozi, Georgios Paliouras
Objective: This paper presents the novel BioASQ Synergy research process which aims to facilitate the interaction between biomedical experts and automated question answering systems.
Materials and methods: The proposed research allows systems to provide answers to emerging questions, which in turn are assessed by experts. The assessment of the experts is fed back to the systems, together with new questions. With this iteration, we aim to facilitate the incremental understanding of a developing problem and contribute to solution discovery.
Results: The results suggest that the proposed approach can assist researchers to navigate available resources. The experts seem to be very satisfied with the quality of the ideal answers provided by the systems, suggesting that such systems are already useful in answering open research questions.
Discussion: BioASQ Synergy aspire to provide a tool that gives the experts easy and personalised access to the latest findings in a fast growing corpus of material.
Conclusion: In this paper we envisioned BioASQ Synergy as a continuous dialogue between experts and systems to issue open questions. We ran an initial proof-of-concept of the approach, in order to evaluate its usefulness, both from the side of the experts, as well as from the side of the participating systems.
{"title":"BioASQ Synergy: A Dialogue between QA systems and biomedical experts for promoting COVID-19 research.","authors":"Anastasia Krithara, Anastasios Nentidis, Eirini Vandorou, Georgios Katsimpras, Yannis Almirantis, Magda Arnal, Adomas Bunevicius, Eulalia Farre-Maduell, Maya Kassiss, Vasileios Konstantakos, Sherri Matis-Mitchell, Dimitris Polychronopoulos, Jesus Rodriguez-Pascual, Eleftherios G Samaras, Martina Samiotaki, Despina Sanoudou, Aspasia Vozi, Georgios Paliouras","doi":"10.1093/jamia/ocae232","DOIUrl":"https://doi.org/10.1093/jamia/ocae232","url":null,"abstract":"<p><strong>Objective: </strong>This paper presents the novel BioASQ Synergy research process which aims to facilitate the interaction between biomedical experts and automated question answering systems.</p><p><strong>Materials and methods: </strong>The proposed research allows systems to provide answers to emerging questions, which in turn are assessed by experts. The assessment of the experts is fed back to the systems, together with new questions. With this iteration, we aim to facilitate the incremental understanding of a developing problem and contribute to solution discovery.</p><p><strong>Results: </strong>The results suggest that the proposed approach can assist researchers to navigate available resources. The experts seem to be very satisfied with the quality of the ideal answers provided by the systems, suggesting that such systems are already useful in answering open research questions.</p><p><strong>Discussion: </strong>BioASQ Synergy aspire to provide a tool that gives the experts easy and personalised access to the latest findings in a fast growing corpus of material.</p><p><strong>Conclusion: </strong>In this paper we envisioned BioASQ Synergy as a continuous dialogue between experts and systems to issue open questions. We ran an initial proof-of-concept of the approach, in order to evaluate its usefulness, both from the side of the experts, as well as from the side of the participating systems.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047392","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}
Mona Alshahawey, Eissa Jafari, Steven M Smith, Caitrin W McDonough
Background: Hypertension (HTN) remains a significant public health concern and the primary modifiable risk factor for cardiovascular disease, which is the leading cause of death in the United States. We applied our validated HTN computable phenotypes within the All of Us Research Program to uncover prevalence and characteristics of HTN and apparent treatment-resistant hypertension (aTRH) in United States.
Methods: Within the All of Us Researcher Workbench, we built a retrospective cohort (January 1, 2008-July 1, 2023), identifying all adults with available age data, at least one blood pressure (BP) measurement, prescribed at least one antihypertensive medication, and with at least one SNOMED "Essential hypertension" diagnosis code.
Results: We identified 99 461 participants with HTN who met the eligibility criteria. Following the application of our computable phenotypes, an overall population of 81 462 were further categorized to aTRH (14.4%), stable-controlled HTN (SCH) (39.5%), and Other HTN (46.1%). Compared to participants with SCH, participants with aTRH were older, more likely to be of Black or African American race, had higher levels of social deprivation, and a heightened prevalence of comorbidities such as hyperlipidemia and diabetes. Heart failure, chronic kidney disease, and diabetes were the comorbidities most strongly associated with aTRH. β-blockers were the most prescribed antihypertensive medication. At index date, the overall BP control rate was 62%.
Discussion and conclusion: All of Us provides a unique opportunity to characterize HTN in the United States. Consistent findings from this study with our prior research highlight the interoperability of our computable phenotypes.
{"title":"Characterizing apparent treatment resistant hypertension in the United States: insights from the All of Us Research Program.","authors":"Mona Alshahawey, Eissa Jafari, Steven M Smith, Caitrin W McDonough","doi":"10.1093/jamia/ocae227","DOIUrl":"https://doi.org/10.1093/jamia/ocae227","url":null,"abstract":"<p><strong>Background: </strong>Hypertension (HTN) remains a significant public health concern and the primary modifiable risk factor for cardiovascular disease, which is the leading cause of death in the United States. We applied our validated HTN computable phenotypes within the All of Us Research Program to uncover prevalence and characteristics of HTN and apparent treatment-resistant hypertension (aTRH) in United States.</p><p><strong>Methods: </strong>Within the All of Us Researcher Workbench, we built a retrospective cohort (January 1, 2008-July 1, 2023), identifying all adults with available age data, at least one blood pressure (BP) measurement, prescribed at least one antihypertensive medication, and with at least one SNOMED \"Essential hypertension\" diagnosis code.</p><p><strong>Results: </strong>We identified 99 461 participants with HTN who met the eligibility criteria. Following the application of our computable phenotypes, an overall population of 81 462 were further categorized to aTRH (14.4%), stable-controlled HTN (SCH) (39.5%), and Other HTN (46.1%). Compared to participants with SCH, participants with aTRH were older, more likely to be of Black or African American race, had higher levels of social deprivation, and a heightened prevalence of comorbidities such as hyperlipidemia and diabetes. Heart failure, chronic kidney disease, and diabetes were the comorbidities most strongly associated with aTRH. β-blockers were the most prescribed antihypertensive medication. At index date, the overall BP control rate was 62%.</p><p><strong>Discussion and conclusion: </strong>All of Us provides a unique opportunity to characterize HTN in the United States. Consistent findings from this study with our prior research highlight the interoperability of our computable phenotypes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142057034","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}
Cynthia Crystal Tang, Supriya Nagesh, David A Fussell, Justin Glavis-Bloom, Nina Mishra, Charles Li, Gillean Cortes, Robert Hill, Jasmine Zhao, Angellica Gordon, Joshua Wright, Hayden Troutt, Rod Tarrago, Daniel S Chow
Objectives: Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a "colloquial" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM).
Materials and methods: Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability.
Results: Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices.
Conclusion: We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.
{"title":"Generating colloquial radiology reports with large language models.","authors":"Cynthia Crystal Tang, Supriya Nagesh, David A Fussell, Justin Glavis-Bloom, Nina Mishra, Charles Li, Gillean Cortes, Robert Hill, Jasmine Zhao, Angellica Gordon, Joshua Wright, Hayden Troutt, Rod Tarrago, Daniel S Chow","doi":"10.1093/jamia/ocae223","DOIUrl":"https://doi.org/10.1093/jamia/ocae223","url":null,"abstract":"<p><strong>Objectives: </strong>Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a \"colloquial\" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM).</p><p><strong>Materials and methods: </strong>Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability.</p><p><strong>Results: </strong>Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices.</p><p><strong>Conclusion: </strong>We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044248","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}
Lingjie Fan, Junhan Zhao, Yao Hu, Junjie Zhang, Xiyue Wang, Fengyi Wang, Mengyi Wu, Tao Lin
Objective: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques.
Materials and methods: Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features.
Results: Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data.
Discussion: The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI.
Conclusion: This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.
目的:从可穿戴传感器获得的传统体力活动(PA)指标可能无法捕捉到体力活动的累积、从久坐到活动的过渡以及多维模式,从而限制了预测老年人身体功能损伤(PFI)的能力。本研究旨在利用可解释的人工智能技术,从腕部加速度计数据中识别独特的时间模式并开发新型数字生物标志物,用于预测PFI及其亚型:通过时间序列分析技术计算出231个PA特征。使用 6 种带有超参数优化的机器学习(ML)算法开发了 PFI 及其亚型(步行、平衡和四肢力量)的预测模型。采用 SHapley Additive exPlanations 方法对 ML 模型进行解释,并对输入特征的重要性进行排序:时间分析表明,PFI 和健康对照组在上午 9:00 至 11:00 之间存在 PA 峰值差异。将腕部加速度计流数据(WAPAS)特征与人口统计学数据相结合时,表现最好的模型(梯度提升树)的曲线下面积得分为 85.93%,准确率为 81.52%,灵敏度为 77.03%,特异性为 87.50%:讨论:新型数字生物标志物(包括变化量级、傅立叶变换(FFT)系数和聚合(AGG)线性趋势)在预测 PFI 方面优于传统的 PA 指标。这些发现凸显了捕捉 PA 模式的多维性对于 PFI 的重要性:本研究探讨了手腕加速度计数字生物标志物在预测老年人 PFI 及其亚型方面的潜力。带有数字生物标志物的综合 PFI 监测系统将改善目前远程 PFI 监测的状况。
{"title":"Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.","authors":"Lingjie Fan, Junhan Zhao, Yao Hu, Junjie Zhang, Xiyue Wang, Fengyi Wang, Mengyi Wu, Tao Lin","doi":"10.1093/jamia/ocae224","DOIUrl":"https://doi.org/10.1093/jamia/ocae224","url":null,"abstract":"<p><strong>Objective: </strong>Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques.</p><p><strong>Materials and methods: </strong>Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features.</p><p><strong>Results: </strong>Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data.</p><p><strong>Discussion: </strong>The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI.</p><p><strong>Conclusion: </strong>This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044249","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}
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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sukanya Mohapatra, Mirna Issa, Vedrana Ivezic, Rose Doherty, Stephanie Marks, Esther Lan, Shawn Chen, Keith Rozett, Lauren Cullen, Wren Reynolds, Rose Rocchio, Gregg C Fonarow, Michael K Ong, William F Speier, Corey W Arnold
Objectives: Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms.
Materials and methods: We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring.
Results: We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P = .01) and weight (87.5% vs 69.4%, P = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity.
Discussion and conclusion: Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events.
{"title":"Increasing adherence and collecting symptom-specific biometric signals in remote monitoring of heart failure patients: a randomized controlled trial.","authors":"Sukanya Mohapatra, Mirna Issa, Vedrana Ivezic, Rose Doherty, Stephanie Marks, Esther Lan, Shawn Chen, Keith Rozett, Lauren Cullen, Wren Reynolds, Rose Rocchio, Gregg C Fonarow, Michael K Ong, William F Speier, Corey W Arnold","doi":"10.1093/jamia/ocae221","DOIUrl":"https://doi.org/10.1093/jamia/ocae221","url":null,"abstract":"<p><strong>Objectives: </strong>Mobile health (mHealth) regimens can improve health through the continuous monitoring of biometric parameters paired with appropriate interventions. However, adherence to monitoring tends to decay over time. Our randomized controlled trial sought to determine: (1) if a mobile app with gamification and financial incentives significantly increases adherence to mHealth monitoring in a population of heart failure patients; and (2) if activity data correlate with disease-specific symptoms.</p><p><strong>Materials and methods: </strong>We recruited individuals with heart failure into a prospective 180-day monitoring study with 3 arms. All 3 arms included monitoring with a connected weight scale and an activity tracker. The second arm included an additional mobile app with gamification, and the third arm included the mobile app and a financial incentive awarded based on adherence to mobile monitoring.</p><p><strong>Results: </strong>We recruited 111 heart failure patients into the study. We found that the arm including the financial incentive led to significantly higher adherence to activity tracker (95% vs 72.2%, P = .01) and weight (87.5% vs 69.4%, P = .002) monitoring compared to the arm that included the monitoring devices alone. Furthermore, we found a significant correlation between daily steps and daily symptom severity.</p><p><strong>Discussion and conclusion: </strong>Our findings indicate that mobile apps with added engagement features can be useful tools for improving adherence over time and may thus increase the impact of mHealth-driven interventions. Additionally, activity tracker data can provide passive monitoring of disease burden that may be used to predict future events.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037585","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}
Brian Ondov, Harsh B Patel, Ai-Te Kuo, John Kastner, Yunheng Han, Hong Wei, Niklas Elmqvist, Hanan Samet
Objective: The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts.
Materials and methods: We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings.
Results: Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards.
Discussion: We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation.
Conclusion: CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.
{"title":"Visualizing Multilayer Spatiotemporal Epidemiological Data with Animated Geocircles.","authors":"Brian Ondov, Harsh B Patel, Ai-Te Kuo, John Kastner, Yunheng Han, Hong Wei, Niklas Elmqvist, Hanan Samet","doi":"10.1093/jamia/ocae234","DOIUrl":"https://doi.org/10.1093/jamia/ocae234","url":null,"abstract":"<p><strong>Objective: </strong>The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts.</p><p><strong>Materials and methods: </strong>We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings.</p><p><strong>Results: </strong>Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards.</p><p><strong>Discussion: </strong>We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation.</p><p><strong>Conclusion: </strong>CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019382","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}
Joseph H Breeyear, Sabrina L Mitchell, Cari L Nealon, Jacklyn N Hellwege, Brian Charest, Anjali Khakharia, Christopher W Halladay, Janine Yang, Gustavo A Garriga, Otis D Wilson, Til B Basnet, Adriana M Hung, Peter D Reaven, James B Meigs, Mary K Rhee, Yang Sun, Mary G Lynch, Lucia Sobrin, Milam A Brantley, Yan V Sun, Peter W Wilson, Sudha K Iyengar, Neal S Peachey, Lawrence S Phillips, Todd L Edwards, Ayush Giri
Objectives: To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs).
Materials and methods: We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination.
Results: The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR.
Conclusions/discussion: We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.
{"title":"Development of electronic health record based algorithms to identify individuals with diabetic retinopathy.","authors":"Joseph H Breeyear, Sabrina L Mitchell, Cari L Nealon, Jacklyn N Hellwege, Brian Charest, Anjali Khakharia, Christopher W Halladay, Janine Yang, Gustavo A Garriga, Otis D Wilson, Til B Basnet, Adriana M Hung, Peter D Reaven, James B Meigs, Mary K Rhee, Yang Sun, Mary G Lynch, Lucia Sobrin, Milam A Brantley, Yan V Sun, Peter W Wilson, Sudha K Iyengar, Neal S Peachey, Lawrence S Phillips, Todd L Edwards, Ayush Giri","doi":"10.1093/jamia/ocae213","DOIUrl":"10.1093/jamia/ocae213","url":null,"abstract":"<p><strong>Objectives: </strong>To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs).</p><p><strong>Materials and methods: </strong>We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination.</p><p><strong>Results: </strong>The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR.</p><p><strong>Conclusions/discussion: </strong>We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001161","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}