Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the Research Program.

Purpose: The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework.

Methods: We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire.

Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.

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.

Identifying erroneous height and weight values from adult electronic health records in the All of Us research program.

Introduction: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us).

Methods: We developed reference charts for adult heights and weights that were stratified on participant sex.

Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program.

Objective: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment.

Materials And Methods: We matched All of Us participants with available trials on ClinicalTrials.

The Data and Research Center: Creating a Secure, Scalable, and Sustainable Ecosystem for Biomedical Research.

The Research Program's Data and Research Center (DRC) was established to help acquire, curate, and provide access to one of the world's largest and most diverse datasets for precision medicine research. Already, over 500,000 participants are enrolled in , 80% of whom are underrepresented in biomedical research, and data are being analyzed by a community of over 2,300 researchers. The DRC created this thriving data ecosystem by collaborating with engaged participants, innovative program partners, and empowered researchers.

A Case Report on the Effectiveness of Virtual Monitoring of Postdischarge COVID-19 Positive Patients in a Rural Hospital Setting: A Retrospective Review.

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Importance of missingness in baseline variables: A case study of the All of Us Research Program.

Objective: The All of Us Research Program collects data from multiple information sources, including health surveys, to build a national longitudinal research repository that researchers can use to advance precision medicine. Missing survey responses pose challenges to study conclusions. We describe missingness in All of Us baseline surveys.

Effects of Cost Sharing on Ophthalmic Care Utilization in the Affordable Care Act Marketplace.

Purpose: To determine the distribution and quantity of ophthalmic care consumed on Affordable Care Act (ACA) plans, the demographics of the population utilizing these services, and the relationship between ACA insurance coverage plan tier, cost sharing, and total cost of ophthalmic care consumed.

Methods: This cross-sectional study analyzed ACA individual and small group market claims data from the Wakely Affordable Care Act (WACA) 2018 dataset, which contains detailed claims, enrollment, and premium data from Edge Servers for 3.9 million individual and small group market lives.

Patient Cost Exposure And Use Of Preventive Care Among ACA-Compliant Individual Plans.

The Affordable Care Act (ACA) mandated coverage of common preventive services with zero patient cost sharing. However, patients may still experience high same-day costs when receiving these "zero-dollar" preventive services. Our analysis of on- and off-exchange individual-market health plans during 2016-18 revealed that 21-61 percent of enrollees experienced same-day cost exposure greater than $0 when accessing ACA-mandated free preventive services.

Evaluating automated electronic case report form data entry from electronic health records.

Background: Many clinical trials leverage real-world data. Typically, these data are manually abstracted from electronic health records (EHRs) and entered into electronic case report forms (CRFs), a time and labor-intensive process that is also error-prone and may miss information. Automated transfer of data from EHRs to eCRFs has the potential to reduce data abstraction and entry burden as well as improve data quality and safety.

Identifying Nonpatient Authors of Patient Portal Secure Messages in Oncology: A Proof-of-Concept Demonstration of Natural Language Processing Methods.

Purpose: Patient portal secure messages are not always authored by the patient account holder. Understanding who authored the message is particularly important in an oncology setting where symptom reporting is crucial to patient treatment. Natural language processing has the potential to detect messages not authored by the patient automatically.

The Research Program: Data quality, utility, and diversity.

The Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races.

EHR Data Quality Assessment Tools and Issue Reporting Workflows for the 'All of Us' Research Program Clinical Data Research Network.

The All of Us (AoU) Research Program aggregates electronic health records (EHR) data from 300,00+ participants spanning 50+ distinct data sites. The diversity and size of AoU's data network result in multifaceted obstacles to data integration that may undermine the usability of patient EHR. Consequently, the AoU team implemented data quality tools to regularly evaluate and communicate EHR data quality issues at scale.

Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program.

Objective: A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs.

Pediatric data from the research program: demonstration of pediatric obesity over time.

Objective: To describe and demonstrate use of pediatric data collected by the Research Program.

Materials And Methods: participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI).

Results: We identified 19 729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980.

Association of Immediate Release of Test Results to Patients With Implications for Clinical Workflow.

This cross-sectional study assesses changes in the volume of patient-initiated messages to clinicians associated with release of test results before and after implementation of the 21st Century Cures Act.

Classification and analysis of asynchronous communication content between care team members involved in breast cancer treatment.

Objective: A growing research literature has highlighted the work of managing and triaging clinical messages as a major contributor to professional exhaustion and burnout. The goal of this study was to discover and quantify the distribution of message content sent among care team members treating patients with breast cancer.

Materials And Methods: We analyzed nearly two years of communication data from the electronic health record (EHR) between care team members at Vanderbilt University Medical Center.

Racial, ethnic, and gender differences in obesity and body fat distribution: An All of Us Research Program demonstration project.

Differences in obesity and body fat distribution across gender and race/ethnicity have been extensively described. We sought to replicate these differences and evaluate newly emerging data from the All of Us Research Program (AoU). We compared body mass index (BMI), waist circumference, and waist-to-hip ratio from the baseline physical examination, and alanine aminotransferase (ALT) from the electronic health record in up to 88,195 Non-Hispanic White (NHW), 40,770 Non-Hispanic Black (NHB), 35,640 Hispanic, and 5,648 Asian participants.

Comparison of family health history in surveys vs electronic health record data mapped to the observational medical outcomes partnership data model in the All of Us Research Program.

Objective: Family health history is important to clinical care and precision medicine. Prior studies show gaps in data collected from patient surveys and electronic health records (EHRs). The All of Us Research Program collects family history from participants via surveys and EHRs.

Impact of High-Dose-Rate Brachytherapy Training via Telehealth in Low- and Middle-Income Countries.

Purpose: Our objective was to demonstrate the efficacy of a telehealth training course on high-dose-rate (HDR) brachytherapy for gynecologic cancer treatment for clinicians in low- and middle-income countries (LMICs).

Methods: A 12-week course consisting of 16 live video sessions was offered to 10 cancer centers in the Middle East, Africa, and Nepal. A total of 46 participants joined the course, and 22 participants, on average, attended each session.

Automating the Classification of Complexity of Medical Decision-Making in Patient-Provider Messaging in a Patient Portal.

Background: Patient portals are consumer health applications that allow patients to view their health information. Portals facilitate the interactions between patients and their caregivers by offering secure messaging. Patients communicate different needs through portal messages.

Analysis of Employee Patient Portal Use and Electronic Health Record Access at an Academic Medical Center.

Background: Patient portals provide patients and their caregivers online access to limited health results. Health care employees with electronic health record (EHR) access may be able to view their health information not available in the patient portal by looking in the EHR.

Objective: In this study, we examine how employees use the patient portal when they also have access to the tethered EHR.

Why Patient Portal Messages Indicate Risk of Readmission for Patients with Ischemic Heart Disease.

Online portals enable patients to exchanging messages with healthcare providers. After discharge, patients message providers to ask questions and report problems. Care providers read and respond accordingly, which requires a non trivial amount of human effort and is unlikely to scale up as portals become more popular.

A systematic literature review of machine learning in online personal health data.

Objective: User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.