Multisite FHIR Mapping of EHR Data to Case Report Forms in REDCap

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

Abstract Keywords: Clinical Trials Innovations, FHIR, Data/System Integration, Standardization and Interoperability, Clinical and Research Data Collection, Curation, Preservation, or Sharing
Primary Track: Clinical Research Informatics
Programmatic Theme: Real-World Evidence in Informatics: Bridging the Gap between Research and Practice

Multicenter clinical trials can improve accuracy and efficiency of case report form (CRF) completion with automated data entry from EHRs using FHIR. We compared CRF data filled out by an automated EHR feed with identical forms completed through manual chart review at two institutions in a multicenter platform trial. Most (91%) CRF fields were completed with the automation but many of those (41%) did not align with the manual chart review and required some remapping.
Speaker(s):
Alex Cheng, PhD
Vanderbilt University Medical Center

Author(s):
Alex Cheng, PhD - Vanderbilt University Medical Center; Mary Banasiewicz, BS - Vanderbilt University Medical Center; Kevin Gibbs, MD - Wake Forest University School of Medicine; Leigha Landreth, RN - Wake Forest University School of Medicine; Genesis Briceno, MD - Oregon Health & Science University; Dena Iadanza, BA - Oregon Health & Science University; Akram Khan, MD - Oregon Health & Science University; Adam Lewis, MS,BS - Vanderbilt University Medical Center; Francesco Delacqua, B.Sc. in Software Development - Vanderbilt University Medical Center; Paul Harris, PhD - Vanderbilt University;

Integrating a conceptual consent permission model from the informed consent ontology for software application execution

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

Abstract Keywords: Ontologies, Data Integration, Knowledge Representation, Management, or Engineering, Data/System Integration, Standardization and Interoperability
Primary Track: Clinical Research Informatics
Programmatic Theme: Integrating Clinical Research and Clinical Care Workflows

We developed a simulated process to show a software implementation to facilitate an approach to integrate the Informed Consent Ontology, a reference ontology of informed consent information, to express implicit description and implement conceptual permission from informed consent life cycle. An early study introduced an experimental method to use Semantic Web Rule Language (SWRL) to describe and represent permissions to computational deduce more information from the Informed Consent Ontology (ICO), demonstrated by the use of the All of Us informed consent documents. We show how incomplete information in informed consent documents can be elucidated using a computational model of permissions toward health information technology that integrates ontologies. Future goals entail applying our computational approach for specific sub-domains of the informed consent life cycle, specifically for vaccine informed consent.
Speaker(s):
Muhammad Amith, PhD
University of Texas Medical Branch

Author(s):
Muhammad Amith, PhD - University of Texas Medical Branch; Yongqun He, PhD - University of Michigan; Elise Smith, PhD - University of Texas Medical Branch; Marcelline Harris, PhD, RN, FACMI - University of Michigan; Frank Manion, PhD - Intelligent Medical Objects; Cui Tao, PhD - School of Biomedical Informatics;

From Spreadsheets and One-off Data Formats to Clinical Data Warehouses: Clinical Data Ingestion into i2b2 with Large Language Models

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

Abstract Keywords: Data Transformation/ETL, Machine Learning, Generative AI, and Predictive Modeling, Data/System Integration, Standardization and Interoperability, Enterprise Data Warehouse/Data Lake, Ontologies
Primary Track: Clinical Research Informatics
Programmatic Theme: Harnessing the Power of Large Language Models in Health Data Science

Clinical and phenotypic data available to researchers is often in either spreadsheets or one-off data formats. Bridging these data to clinical data warehouses would enable sophisticated analytics and cohort discovery, but requires significant single-use development effort. We implement a pilot that leverages large language models to generate Python programs to load phenotypic data from the Genomics Research to Elucidate the Genetics of Rare Disease (GREGoR) model into Informatics for Integrating Biology and the Bedside (i2b2).
Speaker(s):
Jeffrey Klann, PhD
Massachusetts General Hospital

Author(s):
Taowei Wang, PhD - Mass General Brigham; Victor Castro, MS - Mass General Brigham; Shawn Murphy, MD, Ph.D. - Massachusetts General Hospital;

Evaluation of a Semantic Web-Based System Bridging FHIR and OMOP CDM Using Clinical Phenotyping Algorithms

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

Abstract Keywords: FHIR, Data Standards, EHR-based Phenotyping
Primary Track: Clinical Research Informatics
Programmatic Theme: Health Data Science and Artificial Intelligence Innovation: From Single-Center to Multi-Site

The FHIR-Ontop-OMOP system still faces several challenges and limitations that need to be addressed to fully realize its potential. After being evaluated using complex EHR-based phenotyping algorithms, we found that the counts for all queries were identical, ensuring faithful transformation of the tested domains. The FHIR-Ontop-OMOP system provides a meaningful use case for this collaboration, demonstrating substantial potential in healthcare AI applications enabled by the interoperability between FHIR and OMOP CDM.
Speaker(s):
Nan Huo, M.D, Ph.D
Mayo Clinic

Data Governance for Sharing and Access to Real-World Data at an Academic Health System, a Case Study

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

Abstract Keywords: Enterprise Data Warehouse/Data Lake, Secondary Use of EHR Data, Data Sharing/Interoperability
Primary Track: Clinical Research Informatics
Programmatic Theme: Emerging Best Practices for Clinical Research Informatics Operations

Data governance, the policies, and procedures for managing data, is a critical factor for secondary use of clinical data for research. The document presents a case study on the establishment and evolution of data governance practices for sharing and accessing real-world data within an academic healthcare organization.
Speaker(s):
Heath Davis, MS, MLIS, FAMIA
University of Iowa, Carver College of Medicine

Building Patient-Facing Technology: A REDCap-Based Approach

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

Abstract Keywords: Patient-centered Research and Care, Sustainable Research Data Infrastructure, Advanced Data Visualization Tools and Techniques, Mobile Health, Wearable Devices and Patient-Generated Health Data
Primary Track: Clinical Research Informatics
Programmatic Theme: Digital Health Technologies for Patient Research

This work describes the architecture design of a patient-facing technology (PFT) based on the Research Electronic Data Capture (REDCap) platform and other tools to support cancer patients in self-tracking and managing medication concerns and symptoms during transitions of care. The design is guided by the Chronic Care Model (CCM) and User-Centered Design (UCD) principles for a personalized application to inform, engage, and empower patients. We describe the evolutional details of four major versions, which represent milestones of our PFT, highlighting how specific objectives were achieved and the barriers encountered. Additionally, patient representatives were involved in the evaluation of prototypes, and potential improvements to the application of the REDCap platform were discussed. REDCap has demonstrated great potential to serve beyond its traditional role as a survey distribution and management tool. This work is intended to provide developers with insights into future PFT architecture development and sustainable research strategies.
Speaker(s):
Yuheng Shi, MS
UTHealth Houston

Yuheng Shi, Master of Science in Computer Science
The University of Texas Health Science Center at Houston

Author(s):
Yuheng Shi, MS - UTHealth Houston; Eric Yang, BS - UTHealth Houston; Katie Gahn, BS - University of Michigan; Heidi Mason, RN, DNP, ACNP-BC - University of Michigan; Yun Jiang, PhD, MS, RN, FAMIA - University of Michigan; Yang Gong, MD, PhD - UTHealth Houston;