Journal of the American Medical Informatics Association最新文献

{"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}

{"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}

{"title":"Model-based estimation of individual-level social determinants of health and its applications in All of Us.","authors":"Bo Young Kim, Rebecca Anthopolos, Hyungrok Do, Judy Zhong","doi":"10.1093/jamia/ocae168","DOIUrl":"10.1093/jamia/ocae168","url":null,"abstract":"<p><strong>Objectives: </strong>We introduce a widely applicable model-based approach for estimating individual-level Social Determinants of Health (SDoH) and evaluate its effectiveness using the All of Us Research Program.</p><p><strong>Materials and methods: </strong>Our approach utilizes aggregated SDoH datasets to estimate individual-level SDoH, demonstrated with examples of no high school diploma (NOHSDP) and no health insurance (UNINSUR) variables. Models are estimated using American Community Survey data and applied to derive individual-level estimates for All of Us participants. We assess concordance between model-based SDoH estimates and self-reported SDoHs in All of Us and examine associations with undiagnosed hypertension and diabetes.</p><p><strong>Results: </strong>Compared to self-reported SDoHs, the area under the curve for NOHSDP is 0.727 (95% CI, 0.724-0.730) and for UNINSUR is 0.730 (95% CI, 0.727-0.733) among the 329 074 All of Us participants, both significantly higher than aggregated SDoHs. The association between model-based NOHSDP and undiagnosed hypertension is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.649. Similarly, the association between model-based NOHSDP and undiagnosed diabetes is concordant with those estimated using self-reported NOHSDP, with a correlation coefficient of 0.900.</p><p><strong>Discussion and conclusion: </strong>The model-based SDoH estimation method offers a scalable and easily standardized approach for estimating individual-level SDoHs. Using the All of Us dataset, we demonstrate reasonable concordance between model-based SDoH estimates and self-reported SDoHs, along with consistent associations with health outcomes. Our findings also underscore the critical role of geographic contexts in SDoH estimation and in evaluating the association between SDoHs and health outcomes.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2880-2889"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141604437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the utility of using the All of Us Research Program as a resource to study military service members and veterans.","authors":"Ben Porter","doi":"10.1093/jamia/ocae153","DOIUrl":"10.1093/jamia/ocae153","url":null,"abstract":"<p><strong>Objectives: </strong>To illustrate the utility of the All of Us Research Program for studying military and veteran health.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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%).</p><p><strong>Discussion and conclusion: </strong>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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2958-2961"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421682","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":"Sounding out solutions: using SONAR to connect participants with relevant healthcare resources.","authors":"Carla McGruder, Kelly Tangney, Deanna Erwin, Jake Plewa, Karyn Onyeneho, Rhonda Moore, Anastasia Wise, Scott Topper, Alicia Y Zhou","doi":"10.1093/jamia/ocae200","DOIUrl":"10.1093/jamia/ocae200","url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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%).</p><p><strong>Discussion: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2811-2819"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141879672","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":"An evaluation of the All of Us Research Program database to examine cumulative stress.","authors":"Shawna Beese, Demetrius A Abshire, Trey L DeJong, Jason T Carbone","doi":"10.1093/jamia/ocae201","DOIUrl":"10.1093/jamia/ocae201","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US).</p><p><strong>Materials and methods: </strong>We evaluated the All of Us database to determine sample size significance for original-10 allostatic load biomarkers, Allostatic Load Index-5 (ALI-5), Allostatic Load Five, and Cohen's Perceived Stress Scale (PSS). We conducted a priori, post hoc, and sensitivity power analyses to determine sample sizes for conducting null hypothesis significance tests.</p><p><strong>Results: </strong>The maximum number of responses available for each measure is 21 participants for the original-10 allostatic load biomarkers, 150 for the ALI-5, 22 476 for Allostatic Load Five, and n = 90 583 for the PSS.</p><p><strong>Discussion: </strong>The NIH All of Us Research Program is well-suited for studying allostatic load using the Allostatic Load Five and psychological stress using PSS.</p><p><strong>Conclusion: </strong>Improving biomarker data collection in All of Us will facilitate more nuanced examinations of allostatic load among US adults.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2968-2973"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing how frailty and healthcare delays mediate the association between sexual and gender minority status and healthcare utilization in the All of Us Research Program.","authors":"Chelsea N Wong, Louisa H Smith, Robert Cavanaugh, Dae H Kim, Carl G Streed, Farzana Kapadia, Brianne Olivieri-Mui","doi":"10.1093/jamia/ocae205","DOIUrl":"10.1093/jamia/ocae205","url":null,"abstract":"<p><strong>Objectives: </strong>To understand how frailty and healthcare delays differentially mediate the association between sexual and gender minority older adults (OSGM) status and healthcare utilization.</p><p><strong>Materials and methods: </strong>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).</p><p><strong>Results: </strong>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]).</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2916-2923"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631129/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793939","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":"allofus: an R package to facilitate use of the All of Us Researcher Workbench.","authors":"Louisa H Smith, Robert Cavanaugh","doi":"10.1093/jamia/ocae198","DOIUrl":"10.1093/jamia/ocae198","url":null,"abstract":"<p><strong>Objectives: </strong>Despite easy-to-use tools like the Cohort Builder, using All of Us Research Program data for complex research questions requires a relatively high level of technical expertise. We aimed to increase research and training capacity and reduce barriers to entry for the All of Us community through an R package, allofus. In this article, we describe functions that address common challenges we encountered while working with All of Us Research Program data, and we demonstrate this functionality with an example of creating a cohort of All of Us participants by synthesizing electronic health record and survey data with time dependencies.</p><p><strong>Target audience: </strong>All of Us Research Program data are widely available to health researchers. The allofus R package is aimed at a wide range of researchers who wish to conduct complex analyses using best practices for reproducibility and transparency, and who have a range of experience using R. Because the All of Us data are transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), researchers familiar with existing OMOP CDM tools or who wish to conduct network studies in conjunction with other OMOP CDM data will also find value in the package.</p><p><strong>Scope: </strong>We developed an initial set of functions that solve problems we experienced across survey and electronic health record data in our own research and in mentoring student projects. The package will continue to grow and develop with the All of Us Research Program. The allofus R package can help build community research capacity by increasing access to the All of Us Research Program data, the efficiency of its use, and the rigor and reproducibility of the resulting research.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"3013-3021"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631081/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.","authors":"Andrew Guide, Shawn Garbett, Xiaoke Feng, Brandy M Mapes, Justin Cook, Lina Sulieman, Robert M Cronin, Qingxia Chen","doi":"10.1093/jamia/ocae217","DOIUrl":"10.1093/jamia/ocae217","url":null,"abstract":"<p><strong>Importance: </strong>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.</p><p><strong>Objectives: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion and conclusion: </strong>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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":"2869-2879"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141977130","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":"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":"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":" ","pages":"2899-2907"},"PeriodicalIF":4.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631089/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}