Journal of the American Medical Informatics Association最新文献

Objectives: To illustrate the utility of the All of Us Research Program for studying military and veteran health.

Materials and methods: Results were derived from the All of Us Researcher Workbench Controlled Tier v7. Specific variables examined were family history of post-traumatic stress disorder (PTSD), medical encounters, and body mass index/body size.

Results: There are 37 363 military and veteran participants enrolled in the All of Us Research Program. The population is older (M = 63.3 years), White (71.3%), and male (83.2%), consistent with military and veteran populations. Participants reported a high prevalence of PTSD (13.4%), obesity (40.2%), and abdominal obesity (77.1%).

Discussion and conclusion: The breadth and depth of health data from service members and veterans enrolled in the All of Us Research Program allow researchers to address pressing health questions in these populations. Future enrollment and data releases will make this an increasingly powerful and useful study for understanding military and veteran health.

Objective: This article outlines a scalable system developed by the All of Us Research Program's Genetic Counseling Resource to vet a large database of healthcare resources for supporting participants with health-related DNA results.

Materials and methods: After a literature review of established evaluation frameworks for health resources, we created SONAR, a 10-item framework and grading scale for health-related participant-facing resources. SONAR was used to review clinical resources that could be shared with participants during genetic counseling.

Results: Application of SONAR shortened resource approval time from 7 days to 1 day. About 256 resources were approved and 8 rejected through SONAR review. Most approved resources were relevant to participants nationwide (60.0%). The most common resource types were related to support groups (20%), cancer care (30.6%), and general educational resources (12.4%). All of Us genetic counselors provided 1161 approved resources during 3005 (38.6%) consults, mainly to local genetic counselors (29.9%), support groups (21.9%), and educational resources (21.0%).

Discussion: SONAR's systematic method simplifies resource vetting for healthcare providers, easing the burden of identifying and evaluating credible resources. Compiling these resources into a user-friendly database allows providers to share these resources efficiently, better equipping participants to complete follow up actions from health-related DNA results.

Conclusion: The All of Us Genetic Counseling Resource connects participants receiving health-related DNA results with relevant follow-up resources on a high-volume, national level. This has been made possible by the creation of a novel resource database and validation system.

Objectives: To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US).

Materials and methods: We evaluated the All of Us database to determine sample size significance for original-10 allostatic load biomarkers, Allostatic Load Index-5 (ALI-5), Allostatic Load Five, and Cohen's Perceived Stress Scale (PSS). We conducted a priori, post hoc, and sensitivity power analyses to determine sample sizes for conducting null hypothesis significance tests.

Results: The maximum number of responses available for each measure is 21 participants for the original-10 allostatic load biomarkers, 150 for the ALI-5, 22 476 for Allostatic Load Five, and n = 90 583 for the PSS.

Discussion: The NIH All of Us Research Program is well-suited for studying allostatic load using the Allostatic Load Five and psychological stress using PSS.

Conclusion: Improving biomarker data collection in All of Us will facilitate more nuanced examinations of allostatic load among US adults.

Objectives: To understand how frailty and healthcare delays differentially mediate the association between sexual and gender minority older adults (OSGM) status and healthcare utilization.

Materials and methods: Data from the All of Us Research Program participants ≥50 years old were analyzed using marginal structural modelling to assess if frailty or healthcare delays mediated OSGM status and healthcare utilization. OSGM status, healthcare delays, and frailty were assessed using survey data. Electronic health record (EHR) data was used to measure the number of medical visits or mental health (MH) visit days, following 12 months from the calculated All of Us Frailty Index. Analyses adjusted for age, race and ethnicity, income, HIV, marital status ± general MH (only MH analyses).

Results: Compared to non-OSGM, OSGM adults have higher rates of medical visits (adjusted rate ratio [aRR]: 1.14; 95% CI: 1.03, 1.24) and MH visits (aRR: 1.85; 95% CI: 1.07, 2.91). Frailty mediated the association between OSGM status medical visits (Controlled direct effect [Rcde] aRR: 1.03, 95% CI [0.87, 1.22]), but not MH visits (Rcde aRR: 0.37 [95% CI: 0.06, 1.47]). Delays mediated the association between OSGM status and MH visit days (Rcde aRR: 2.27, 95% CI [1.15, 3.76]), but not medical visits (Rcde aRR: 1.06 [95% CI: 0.97, 1.17]).

Discussion: Frailty represents a need for medical care among OSGM adults, highlighting the importance of addressing it to improve health and healthcare utilization disparities. In contrast, healthcare delays are a barrier to MH care, underscoring the necessity of targeted strategies to ensure timely MH care for OSGM adults.

Objectives: Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies.

Target audience: All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package.

Scope: We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.

Importance: Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable.

Objectives: Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response.

Materials and methods: Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time.

Results: All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively.

Discussion and conclusion: The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.

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.

Objective: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.

Materials and methods: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process.

Results: The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate.

Discussion: ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources.

Conclusion: Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.

Objective: This study aims to develop machine learning models that provide both accurate and equitable predictions of 2-year stroke risk for patients with atrial fibrillation across diverse racial groups.

Materials and methods: Our study utilized structured electronic health records (EHR) data from the All of Us Research Program. Machine learning models (LightGBM) were utilized to capture the relations between stroke risks and the predictors used by the widely recognized CHADS2 and CHA2DS2-VASc scores. We mitigated the racial disparity by creating a representative tuning set, customizing tuning criteria, and setting binary thresholds separately for subgroups. We constructed a hold-out test set that not only supports temporal validation but also includes a larger proportion of Black/African Americans for fairness validation.

Results: Compared to the original CHADS2 and CHA2DS2-VASc scores, significant improvements were achieved by modeling their predictors using machine learning models (Area Under the Receiver Operating Characteristic curve from near 0.70 to above 0.80). Furthermore, applying our disparity mitigation strategies can effectively enhance model fairness compared to the conventional cross-validation approach.

Discussion: Modeling CHADS2 and CHA2DS2-VASc risk factors with LightGBM and our disparity mitigation strategies achieved decent discriminative performance and excellent fairness performance. In addition, this approach can provide a complete interpretation of each predictor. These highlight its potential utility in clinical practice.

Conclusions: Our research presents a practical example of addressing clinical challenges through the All of Us Research Program data. The disparity mitigation framework we proposed is adaptable across various models and data modalities, demonstrating broad potential in clinical informatics.

Objectives: We describe new curriculum materials for engaging secondary school students in exploring the "big data" in the NIH All of Us Research Program's Public Data Browser and the co-design processes used to collaboratively develop the materials. We also describe the methods used to develop and validate assessment items for studying the efficacy of the materials for student learning as well as preliminary findings from these studies.

Materials and methods: Secondary-level biology teachers from across the United States participated in a 2.5-day Co-design Summer Institute. After learning about the All of Us Research Program and its Data Browser, they collaboratively developed learning objectives and initial ideas for learning experiences related to exploring the Data Browser and big data. The Genetic Science Learning Center team at the University of Utah further developed the educators' ideas. Additional teachers and their students participated in classroom pilot studies to validate a 22-item instrument that assesses students' knowledge. Educators completed surveys about the materials and their experiences.

Results: The "Exploring Big Data with the All of Us Data Browser" curriculum module includes 3 data exploration guides that engage students in using the Data Browser, 3 related multimedia pieces, and teacher support materials. Pilot testing showed substantial growth in students' understanding of key big data concepts and research applications.

Discussion and conclusion: Our co-design process provides a model for educator engagement. The new curriculum module serves as a model for introducing secondary students to big data and precision medicine research by exploring diverse real-world datasets.