Molecularly-Guided Cancer Clinical Trial Matching using FHIR and HL7 Clinical Quality Language: A Proof of Concept

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Biomarkers, Cancer Genetics, Clinical Decision Support
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Introduction: Clinical trials play a crucial role in precision cancer care. Patients generally learn of trials from their physician, and physician recognition of potential matches can be enhanced through decision support tools. But automated trial matching remains challenging, particularly for molecular eligibility criteria.
Objective: We assessed the feasibility of FHIR Genomics plus CQL to enable trial matching, particularly for molecular criteria.
Methods: We developed a prototype that included (1) encoded trial criteria in CQL; (2) synthetic patient clinical and genomic data; (3) trial eligibility computation.
Results: We found that even complex molecular eligibility criteria can be represented in CQL given that the semantics of a criterion are formalized in base FHIR specifications. The proof of concept "CQL for Clinical Trials Matching" is available at [https://elimu.io/downloads/].
Discussion and Conclusions: Proof of concept work suggests FHIR and CQL as viable options for automating clinical trial matching.

Speaker(s):
Bob Dolin, MD
Elimu Informatics

Author(s):
Bob Dolin, MD - Elimu Informatics; Waddah Arafat, MD - UT Southwestern Medical Center; Bret Heale, PhD - Humanized Health Consulting; Edna Shenvi, MD, MAS - Elimu Informatics Inc.; Srikar Chamala, PhD - Children's Hospital Los Angeles / Univ of Southern California;

SeqTrial: Utility Preserving Sequential Clinical Trial Data Generator

Presentation Time: 03:45 PM - 04:00 PM

Abstract Keywords: Deep Learning, Clinical Decision Support, Natural Language Processing, Data Sharing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Clinical trial data used to evaluate new treatments have value beyond the original studies, but limitations in data access due to privacy concerns make further use of these data challenging. Digital twins offer a solution by simulating patient outcomes, providing less restricted data access, reducing costs and increasing sample sizes. However, existing research focuses on synthetic Electronic Healthcare Records (EHRs) and lacks personalized patient record generation. This paper introduces SeqTrial, a framework for generating personalized digital twins for sequential clinical trial event data. The method uses BioBERT word embeddings to capture biomedical term semantics, an attention mechanism to understand visit relationships, and synthesizes digital twins for each patient. SeqTrial generates utility-preserving digital twins capable of estimating clinical outcomes, while addressing data scarcity through self-supervised pretraining. The method demonstrates high fidelity and utility in generating synthetic sequential clinical trial data for patient outcome prediction while ensuring privacy protection. The code is available at https://github.com/trishad2/SeqTrial.git.

Speaker(s):
Jacob Aptekar, MD PhD
Medidata

Author(s):
Trisha Das, Ph.D. Student - University of Illinois Urbana-Champaign; Afrah Shafquat, PhD - Medidata Solutions; Mandis Beigi, PhD; Jacob Aptekar, MD, PhD - Medidata Solutions; Jason Mezey, PhD - Cornell University; Jimeng Sun - University of Illinois at Urbana Champaign;

Combining Rule-based NLP-lite with Rapid Iterative Chart Adjudication for Creation of a Large, Accurately Curated Cohort from EHR data: A Case Study in the Context of a Clinical Trial Emulation

Presentation Time: 04:00 PM - 04:15 PM

Abstract Keywords: Natural Language Processing, Causal Inference, Information Extraction, Internal Medicine or Medical Subspecialty, Informatics Implementation, Human-computer Interaction, Knowledge Representation and Information Modeling, Large Language Models (LLMs)
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

The aim of this work was to create a gold-standard curated cohort of ~10,000 cases from the Veteran Affairs corporate data warehouse for virtual emulation of a randomized clinical trial (CSP#592). The trial had six inclusion/exclusion criteria lacking adequate structured data. We therefore used a hybrid computer/human approach to extract information from clinical notes. Rule-based NLP output was iteratively adjudicated by a panel of trained non-clinician content experts and non-experts using an easy-to-use spreadsheet-based rapid adjudication display. This group-adjudication process iteratively sharpened both the computer algorithm and clinical decision criteria, while simultaneously training the non-experts. The cohort was successfully created with each inclusion/exclusion decision backed by a source document. Less than 0.5% of cases required referral to specialist clinicians. It is likely that such curated datasets capturing specialist reasoning and using a process-supervised approach will acquire greater importance as training tools for future clinical AI applications.

Speaker(s):
Pradeep Mutalik, MD
Yale University School of Medicine

Author(s):
Kei-Hoi Cheung, PhD - Biomedical Informatics and Data Science; Jennifer Green, BA - VA Portland Health Care System; Melissa Buelt-Gebhardt, PhD, ACRP-CP - VA Minneapolis Health Care System; Karen Anderson, BA - Yale University School of Medicine; Vales JeanPaul, MSHS, MBA/HCM - VA Connecticut Health Care System; Linda McDonald, BS, RN - Cooperative Studies Program Coordinating Center, VA Connecticut Health Care Center; Michael Wininger, PhD - Yale University School of Medicine; Yuli Li, MS - VA Cooperative Studies Program Clinical Epidemiology Research Center, VA Connecticut Health Care System; Nallakkandi Rajeevan, PhD - VA Cooperative Studies Program Clinical Epidemiology Research Center, VA Connecticut Health Care System; Peter Jessel, MD - VA Portland Health Care System; Hans Moore, MD, FHRS - VA Washington DC Health Care; Selçuk Adabag, MD - Minneapolis; Merritt Raitt, MD - VA Portland Health Care System; Mihaela Aslan, PhD - VA Cooperative Studies Program Clinical Epidemiology Research Center;

Comparing Recruitment Strategies in a Pragmatic Clinical Trial of Older Adults: Patient Portal Messaging vs. Traditional Postal Mail

Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Patient Engagement and Preferences, Human-computer Interaction, Information Retrieval, Education and Training, Data Sharing
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Patient recruitment is a crucial component of research studies. Various forms of recruitment are often used to recruit different study populations. Previous studies have shown the strengths and weaknesses for both recruitment methods, however, limited studies focused on comparing the two methods in reaching an older study population. This study compares two common methods of recruitment, patient portal messaging and postal mailing, in recruiting adults over the age of 65 in a pragmatic clinical trial. Identical recruitment letters were sent out through these two recruitment methods with varied time and effort. Patients’ response rates were captured during a 2-month period. Moreover, we suggest exploring other factors such as various age groups and racial/ethnic groups could facilitate the research team to learn more about the preferences of different patient populations.

Speaker(s):
PATRICIA C DYKES, PhD, MA, RN
Brigham and Women's Hospital/Harvard Medical School

Author(s):
Mackenzie Kiesman, Bachelor of Arts - Brigham and Women's Hospital; Julia Loewenthal, Doctor of Medicine - Brigham and Women's Hospital; Michael Sainlaire - Brigham and Women's Health; Stuart Lipsitz - Brigham and Women's Hospital; PATRICIA C DYKES, PhD, MA, RN - Brigham and Women's Hospital; Nancy Latham, PhD PT - Brigham and Women's Hosptial;

A Precision Medicine Approach to Curating Research Cohorts of Patients with Long Covid

Presentation Time: 04:30 PM - 04:45 PM

Abstract Keywords: Precision Medicine, Bioinformatics, Population Health
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Current approaches to identifying Long Covid patients do not account for pre-existing conditions that are alternative causes for Long Covid symptoms. In a retrospective case-control study, we built upon the World Health Organization’s (WHO) definition as a diagnosis of exclusion, to curate a precision cohort of PASC patients, using longitudinal electronic health records data from 14 hospitals and 20 community health centers in the New England region. We gathered a Long Covid cohort including 24,360 patients with at least one post-acute sequela of COVID-19, with a positive predictive value of 77 percent.

Speaker(s):
Jonas Hugel, M.Sc.
University Medical Center Göttingen, Department of Medical Informatics

Author(s):
Alaleh Azhir, Master's - Harvard Medical School; Hossein Estiri, PhD - Harvard Medical School; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;

Using Aggregate Electronic Healthcare Record Data to Reproduce Large Scale Prospective Clinical Trials

Presentation Time: 04:45 PM - 05:00 PM

Abstract Keywords: Data Mining, Data Sharing, Real-World Evidence Generation, Chronic Care Management, Clinical Guidelines, Interoperability and Health Information Exchange
Primary Track: Foundations
Programmatic Theme: Clinical Research Informatics

This is a study aimed to validate the findings of the ALLHAT hypertension study using aggregate electronic health record (EHR) data from Epic Cosmos Data Network. Recreating the study criteria using over 62 million patient records, comparisons were made between amlodipine, lisinopril, chlorthalidone, and doxazosin regarding stroke, acute heart failure, and cardiovascular disease. Overall, the EHR data supported most ALLHAT findings, suggesting its potential for retrospective studies and novel conclusions.

Speaker(s):
Allan Kerandi, MD
MetroHealth

Author(s):
Craig Jarrett, MD/MBA - University Hospitals Cleveland; Katherine Liang, MD - MetroHealth; Kiron Nair, MD - MetroHealth; Edward Horwitz, MD - Case Western Reserve University/The MetroHealth System; Crystal Mosca, MD - University Hospitals Cleveland; Jeffrey Sunshine, MD, PhD - University Hospitals Cleveland; David Kaelber, MD, PhD, MPH - Case Western Reserve University/The MetroHealth System;