Impact of electronic health record updates and changes on the delivery and monitoring of interventions in embedded pragmatic clinical trials.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 32 embedded pragmatic clinical trials, and many of these trials rely on data from the electronic health record (EHR) to monitor outcomes and/or use functionality provided by the EHR platform to deliver the intervention. Given the complexity and dynamic nature of EHR systems, study teams have encountered challenges in use of the EHR for these purposes, including challenges related to local implementation of trial interventions, rapid technology evolution, EHR updates, and transitions in EHR systems. In this article, we share case examples and lessons learned, and suggest that teams need to be aware of-and perhaps proactively investigate- possible changes to EHR systems and data that will affect the delivery of interventions and the integrity and safety of pragmatic clinical trials.

Enhancing Postmarketing Surveillance of Medical Products With Large Language Models.

Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations.

Association of Cardiologist Clinic Visits with Cardiovascular Primary Prevention Outcomes Among People with HIV from Underrepresented Racial and Ethnic Groups in the Southern United States.

Background: People with HIV (PWH) are at elevated risk for atherosclerotic cardiovascular disease (ASCVD). Underrepresented racial and ethnic groups (UREGs) with HIV in the southern U.S.

Kidney and Cardiovascular Effectiveness of SGLT2 Inhibitors vs GLP-1 Receptor Agonists in Type 2 Diabetes.

Background: Emerging data suggest that glucagon-like peptide-1 receptor agonists (GLP-1 RAs) improve kidney outcomes for people with type 2 diabetes (T2D). Direct comparisons of the kidney and cardiovascular effectiveness of GLP-1 RA with sodium-glucose cotransporter 2 inhibitors (SGLT2i), a first-line therapy for this population, are needed.

Objectives: The authors compared kidney and cardiovascular outcomes for new users of SGLT2i and GLP-1 RAs with T2D.

Concordance With Screening and Treatment Guidelines for Chronic Kidney Disease in Type 2 Diabetes.

Importance: Chronic kidney disease (CKD) is an often-asymptomatic complication of type 2 diabetes (T2D) that requires annual screening to diagnose. Patient-level factors linked to inadequate screening and treatment can inform implementation strategies to facilitate guideline-recommended CKD care.

Objective: To identify risk factors for nonconcordance with guideline-recommended CKD screening and treatment in patients with T2D.

Collecting patient-reported outcome measures in the electronic health record: Lessons from the NIH pragmatic trials Collaboratory.

The NIH Pragmatic Trials Collaboratory supports the design and conduct of 27 embedded pragmatic clinical trials, and many of the studies collect patient reported outcome measures as primary or secondary outcomes. Study teams have encountered challenges in the collection of these measures, including challenges related to competing health care system priorities, clinician's buy-in for adoption of patient-reported outcome measures, low adoption and reach of technology in low resource settings, and lack of consensus and standardization of patient-reported outcome measure selection and administration in the electronic health record. In this article, we share case examples and lessons learned, and suggest that, when using patient-reported outcome measures for embedded pragmatic clinical trials, investigators must make important decisions about whether to use data collected from the participating health system's electronic health record, integrate externally collected patient-reported outcome data into the electronic health record, or collect these data in separate systems for their studies.

Optimizing data integration in trials that use EHR data: lessons learned from a multi-center randomized clinical trial.

Background: Despite great promise, trials that ascertain patient clinical data from electronic health records (EHR), referred to here as "EHR-sourced" trials, are limited by uncertainty about how existing trial sites and infrastructure can be best used to operationalize study goals. Evidence is needed to support the practical use of EHRs in contemporary clinical trial settings.

Main Text: We describe a demonstration project that used EHR data to complement data collected for a contemporary multi-center pharmaceutical industry outcomes trial, and how a central coordinating center supported participating sites through the technical, governance, and operational aspects of this type of activity.

Equity and bias in electronic health records data.

Embedded pragmatic clinical trials (ePCTs) are conducted during routine clinical care and have the potential to increase knowledge about the effectiveness of interventions under real world conditions. However, many pragmatic trials rely on data from the electronic health record (EHR) data, which are subject to bias from incomplete data, poor data quality, lack of representation from people who are medically underserved, and implicit bias in EHR design. This commentary examines how the use of EHR data might exacerbate bias and potentially increase health inequities.

Assessing the impact of privacy-preserving record linkage on record overlap and patient demographic and clinical characteristics in PCORnet®, the National Patient-Centered Clinical Research Network.

Objective: This article describes the implementation of a privacy-preserving record linkage (PPRL) solution across PCORnet®, the National Patient-Centered Clinical Research Network.

Material And Methods: Using a PPRL solution from Datavant, we quantified the degree of patient overlap across the network and report a de-duplicated analysis of the demographic and clinical characteristics of the PCORnet population.

Results: There were ∼170M patient records across the responding Network Partners, with ∼138M (81%) of those corresponding to a unique patient.

Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them.

Objectives: To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems.

Materials And Methods: We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term.

Establishing a framework for privacy-preserving record linkage among electronic health record and administrative claims databases within PCORnet, the National Patient-Centered Clinical Research Network.

Objective: The aim of this study was to determine whether a secure, privacy-preserving record linkage (PPRL) methodology can be implemented in a scalable manner for use in a large national clinical research network.

Results: We established the governance and technical capacity to support the use of PPRL across the National Patient-Centered Clinical Research Network (PCORnet). As a pilot, four sites used the Datavant software to transform patient personally identifiable information (PII) into de-identified tokens.

Standardizing, harmonizing, and protecting data collection to broaden the impact of COVID-19 research: the rapid acceleration of diagnostics-underserved populations (RADx-UP) initiative.

Objective: The Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) program is a consortium of community-engaged research projects with the goal of increasing access to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests in underserved populations. To accelerate clinical research, common data elements (CDEs) were selected and refined to standardize data collection and enhance cross-consortium analysis.

Materials And Methods: The RADx-UP consortium began with more than 700 CDEs from the National Institutes of Health (NIH) CDE Repository, Disaster Research Response (DR2) guidelines, and the PHENotypes and eXposures (PhenX) Toolkit.

Evaluating fitness-for-use of electronic health records in pragmatic clinical trials: reported practices and recommendations.

Objective: To empirically explore how pragmatic clinical trials (PCTs) that used real-world data (RWD) assessed study-specific fitness-for-use.

Methods: We conducted interviews and surveys with PCT teams who used electronic health record (EHR) data to ascertain endpoints. The survey cataloged key concerns about RWD, activities used to assess data fitness-for-use, and related barriers encountered by study teams.

Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework.

The Sentinel System is a major component of the United States Food and Drug Administration's (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center's initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data.

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory.

Objective: We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research.

Materials And Methods: Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements.

Testing the Use of Data Drawn from the Electronic Health Record to Compare Quality.

Introduction: Health systems spend $1.5 billion annually reporting data on quality, but efficacy and utility for benchmarking are limited due, in part, to limitations of data sources. Our objective was to implement and evaluate measures of pediatric quality for three conditions using electronic health record (EHR)-derived data.

PCORnet® 2020: current state, accomplishments, and future directions.

Objective: To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale.

Study Design And Setting: Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet.

Participant choices for return of genomic results in the eMERGE Network.

Purpose: Secondary findings are typically offered in an all or none fashion when sequencing is used for clinical purposes. This study aims to describe the process of offering categorical and granular choices for results in a large research consortium.

Methods: Within the third phase of the electronic MEdical Records and GEnomics (eMERGE) Network, several sites implemented studies that allowed participants to choose the type of results they wanted to receive from a multigene sequencing panel.