Advancing Clinical Trial Efficiency and Data Accuracy through Direct EHR-to-EDC Integration

Presentation Type: Paper - Regular

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Presentation Time: 03:30 PM - 03:42 PM

Primary Track: Clinical Research Informatics


For study sponsors, clinical trial efficiency and data accuracy are non-negotiable drivers of success. Despite broad digitization in healthcare, redundant data entry persists, and the current reliance on manual transcription from electronic health records (EHRs) into electronic data capture (EDC) systems creates unnecessary complexity—leading to higher costs, site burden, and data quality risks. With the growing maturity of interoperability standards and frameworks, direct EHR-to-EDC integration offers a transformative opportunity to automate data transfer, improve accuracy, and streamline operations. This paper evaluates the impact of direct EHR-to-EDC integration on data quality compared to traditional workflows. Our findings in a multi-study, multi-site assessment demonstrate that manual transcription resulted in an error rate of 8.23% (95% CI: 7.79–8.70), whereas no transcription errors were detected in the integrated EHR-to-EDC workflow (95% CI: 0.00–0.03). These results underscore the potential of interoperability-based automation to improve data reliability and operational efficiency in clinical research.

Speaker(s):
Maryam Garza, PhD, MPH, MMCi
University of Texas Health Science Center at San Antonio

Author(s):
Maryam Garza, PhD, MPH, MMCi - University of Texas Health Science Center at San Antonio; Muayad Hamidi, MBChB - University of Texas Health Science Center at San Antonio; Iddo Peleg, MBA - Yonalink; Sapir Balas, BS - Yonalink; Byeong Yeob Choi, PhD - University of Texas Health Science Center at San Antonio; Meredith Zozus, PhD - UT Health Science Center;

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Completeness of Common Data Elements for Breast Cancer Clinical Trials in Observational Databases

Presentation Type: Paper - Student

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Presentation Time: 03:42 PM - 03:54 PM

Primary Track: Clinical Research Informatics


Clinical trials serve as a gold standard for data that shapes the healthcare landscape in oncology, but variance in clinical trial eligibility criteria presents challenges for using the data in downstream applications. Identifying common data elements (CDEs) present in cancer clinical trials is a critical component to achieve standardized representations of oncology data and evaluate how well different clinical data sources capture cancer information. In this study, we curate a set of CDEs contained in the eligibility criteria of breast cancer clinical trials and evaluate their completeness across different observational databases represented in the Observational Medical Outcomes Partnership (OMOP) Common Data Model. We show that between databases, CDEs are captured with differing levels of completeness across OMOP domains and that there is discordance between the frequency of eligibility criteria CDEs and their completeness in observational databases, which characterizes these databases’ utility for subsequent oncology trial matching efforts.

Speaker(s):
Adit Anand, M.A.
Columbia University

Author(s):
Yilu Fang, MA - Columbia University Department of Biomedical Informatics; Chunhua Weng, PhD - Columbia University; Karthik Natarajan, PhD - Columbia University Dept of Biomedical Informatics;

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Revisiting WARCEF Through Real-World Emulation: Temporal Selection Bias and Shifting Treatment Effects

Presentation Type: Podium Abstract

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Presentation Time: 03:54 PM - 04:06 PM

Primary Track: Clinical Research Informatics


Randomized controlled trials (RCTs) define evidence-based care, yet how their findings influence subsequent real-world treatment patterns remains poorly understood. The WARCEF trial reported no significant difference between Warfarin and Aspirin for patients with heart failure and reduced ejection fraction (HFrEF), leading guidelines to discourage routine anticoagulation in this population. Using electronic health record data from the Mayo Clinic Platform, we emulated the WARCEF trial to evaluate how treatment effects changed before and after the trial’s completion in July 2014. We applied the original trial’s inclusion and exclusion criteria, constructed intention-to-treat cohorts, and assessed all-cause mortality using Cox proportional hazards models with propensity score adjustment. Among 18,765 eligible patients, 4,672 were included before 2014 and 14,092 afterward. Before trial completion, outcomes mirrored WARCEF’s neutral findings, with no significant difference between Warfarin and Aspirin (HR 1.396; p = 0.3477). In contrast, after 2014, Warfarin was associated with substantially higher mortality (HR 2.039; p < 0.001). Sensitivity analyses using alternative cutoff years confirmed a consistent temporal shift, with later years showing progressively greater risk for Warfarin. These results suggest that dissemination of WARCEF and subsequent guideline changes altered prescribing behavior and patient selection, resulting in different real-world treatment effects over time. Our findings highlight the necessity of accounting for temporal practice changes in target trial emulation and demonstrate how real-world data can reveal the downstream clinical impact of major RCTs. This work underscores the importance of aligning real-world cohorts with the original trial context to support valid causal inference.

Speaker(s):
Xiaodi Li, Ph.D.
Mayo Clinic

Author(s):
Xiaodi Li, Ph.D. - Mayo Clinic; Sivaraman Rajaganapathy, Research Fellow/Ph.D. - Mayo Clinic; xinyue Hu, Master - mayo clinic; MunHwan Lee, Ph.D. - Mayo Clinic; Jingna Feng, M.S. - Mayo Clinic; Jianfu Li, PhD - Mayo Clinic; Yue Yu, Ph.D. - Mayo Clinic; Phil Fiero, Bachelor's - Mayo Clinic; Soulmaz Boroumand, Ph.D. - Mayo Clinic; Richard Larsen, M.S. - Mayo Clinic; Jun Chen, Ph.D. - Mayo Clinic; Pengyang Li, M.D., M.Sc. - Virginia Commonwealth University; Xiaoke Liu, M.D., Ph.D. - Mayo Clinic; Cui Tao, PhD - Mayo Clinic; Nansu Zong, Ph.D. - Mayo Clinic;

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KRAS Clinical Trial Finder: A Patient-Centered Platform for Improving Access to Clinical Trial Information

Presentation Type: Paper - Student
Student Paper Competition Nominee

Presentation Time: 04:06 PM - 04:18 PM

Primary Track: Clinical Research Informatics


KRAS mutations are linked to aggressive cancers, yet many patients struggle to locate trials that match their tumor subtype or circumstances. This study presents the design and user-centered evaluation of the KRAS Clinical Trial Finder, a patient-centered platform that improves access to KRAS-specific clinical trial information. A Python pipeline was developed to extract and transform KRAS-related trials from ClinicalTrials.gov into a relational database, which was presented through an interactive R Shiny dashboard offering plain-language summaries and distance calculations. Twenty-eight KRAS Kickers patients and care partners completed four usability tasks, evaluated with the Single Ease Question (SEQ) and System Usability Scale (SUS). Results demonstrated excellent usability (mean SEQ 6.44/7; SUS 91.4) and rapid task completion (mean 66.5 seconds), with 95% expressing willingness to reuse the system. Qualitative analysis identified five improvement domains: visibility, personalization, education, empowerment, and accessibility. Future directions include EHR integration, AI-enabled trial matching, enhanced education, and multilingual support.

Speaker(s):
Christopher Conneran, Biomedical Health Informatics
University of North Carolina at Chapel Hill

Author(s):
Christopher Conneran, Biomedical Health Informatics - University of North Carolina at Chapel Hill; Terri Conneran, BS - KRAS Kickers; Bonnie Chen, BS - University of North Carolina at Chapel Hill; Danny Wu, PhD - University of North Carolina at Chapel Hill;

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Barriers to Women’s Inclusions in Clinical Trials: Examining Systematic Exclusion Linked to Women’s Health Conditions

Presentation Type: Podium Abstract
Presentation Time: 04:18 PM - 04:30 PM

Primary Track: Data Science/Artificial Intelligence


Research clinical trials (RCTs) are the cornerstone of evidence-based medicine. However, women are systematically being excluded from RCT based on health conditions such as pregnancy and menopause. This exclusion can limit generalizability and efficacy of RCT in the real world where such exclusions do not apply. We performed a study to quantify the extent to which women’s health-specific conditions are mentioned in the inclusion and exclusion criteria of RCTs using ClinicalTrials.gov data. From 23,605 RCTs, pregnancy had the highest mention in exclusions with 8462 studies followed by contraception use (n=4,485). Half of active RCT recruiting for lifestyle or cardiovascular disease mentioned women’s health-specific conditions in exclusion. A manual coding of 91 observational studies showed that 26% of RCTs lacked a valid concern for excluding pregnant women. This can have huge implications on RCT generalizability.

Speaker(s):
Lina Sulieman, PhD
Vanderbilt University Medical Center

Author(s):
Lina Sulieman, PhD - Vanderbilt University Medical Center; Felicity Enders, PhD - MayoClinic; Vesna Garovic, MD, PhD - mayo clinic; Paul Harris, PhD - Vanderbilt University; Nicole Woitowich, PhD - Northwestern University;

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REACT (Real-world Emulated Clinical Trials): A Framework for Drug Repurposing Applied to Osteoporotic Fractures

Presentation Type: Paper - Student

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Presentation Time: 04:30 PM - 04:42 PM

Primary Track: Clinical Research Informatics


Osteoporotic fractures remain common despite available therapies, highlighting the need for new, safe, and affordable treatment options. We developed REACT framework, a scalable and disease-agnostic trial-emulation framework designed to identify repurposable therapeutics and safety signals across large observational databases. REACT automates cohort construction, comparator selection, covariate balancing, and effect estimation, enabling high-throughput, reproducible evaluation of candidate drugs using real-world data. Applied to fracture prevention in older adults with osteoporosis, REACT efficiently screened 1,222 marketed drugs and identified 48 balanced treatment contrasts, of which 18 showed statistically significant effects after multiple-testing adjustment. Eleven drugs demonstrated protective associations consistent with mechanisms supporting bone health, while 7 exhibited risk-increasing effects aligning with pathways related to falls or impaired mobility. The convergence of these findings with biological and prior observational evidence underscores REACT’s ability to surface clinically meaningful patterns and its potential as a generalizable platform for accelerating real-world drug discovery and safety surveillance.

Speaker(s):
Jianing Liu, MPH
Ohio State University

Author(s):
Jianing Liu, MPH - Ohio State University; Seungyeon Lee, MS - The Ohio State University; Xianhui Chen, Master - The Ohio State University; Soledad Fernandez - The Ohio State University Dept of Biomedical Informatics; Qing Wu, MD, ScD - Ohio State University; Ping Zhang, PhD, FAMIA - The Ohio State University;

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