Expediting Oncology Evidence Generation by Leveraging EMR-Enabled Clinically Rich Real-World Data at Scale
Author(s)
will sopwith, PhD MPH1, Marina Borges, MSc2, Oliver Russell, MD3, Paula Cardenes, MSc4, Laura Girardat-Rotar, BSc, MPH, PhD5, Benedikt Maissenhaelter, MSc6, Finlay MacDougall, MA3.
1IQVIA, Liverpool, United Kingdom, 2IQVIA, Portugal, 3IQVIA, London, United Kingdom, 4IQVIA, BARCELONA, Spain, 5IQVIA, Basel, Switzerland, 6IQVIA, Munich, Germany.
1IQVIA, Liverpool, United Kingdom, 2IQVIA, Portugal, 3IQVIA, London, United Kingdom, 4IQVIA, BARCELONA, Spain, 5IQVIA, Basel, Switzerland, 6IQVIA, Munich, Germany.
OBJECTIVES: Clinically valuable real-world evidence (RWE) for oncology requires granular, timely data at scale and agility in the generation of results. This research describes how challenges such as extended timelines for large cohorts’ identification and abstraction can be overcome in retrospective research by leveraging an EMR-enabled site-based approach. Technological advantages for scalability and time-efficiencies are described.
METHODS: Hospital sites within IQVIA’s Oncology Evidence Network (OEN) were systematically characterized based on their comprehensive Real-World Data (RWD) infrastructure, including data availability, integration capacity and longitudinal follow-up. Assessment included data volume, optimization of data identification and extraction process and overall suitability to support agile and scalable RWE generation.
RESULTS: 14 sites across 6 countries were assessed. We focused our research on Lung cancer data; a significantly large number of patients (total number 7000, average per site 500) are treated yearly at these institutions, providing access to large cohorts for RWE generation. 11 sites have an electronic data querying system, streamlining study population identification based on structured data fields and reducing the need for manual abstraction. Importantly, no lag between data capture in the EMR and accessibility for research was reported. Consistent availability of disease-relevant data such as longitudinal TNM staging, presence of comorbidities, metastasis diagnosis techniques, biomarker expression (including longitudinal) and treatment information (including concomitant medications) was reported across sites, confirming access to RWD from multiple internal sources/departments.
CONCLUSIONS: Sites’ EMR data provides rich, clinically meaningful and up to date data of value for RWE generation in oncology. Technological maturity of sites enabling digital approaches to identifying and extracting large cohorts’ data is advantageous in overcoming operational challenges associated with manual review of records. This facilitates delivery of RWE studies with clinically-rich data within optimized timelines and with minimized single-investigator selection bias and associated challenges for interpretation. Specific use cases will be presented.
METHODS: Hospital sites within IQVIA’s Oncology Evidence Network (OEN) were systematically characterized based on their comprehensive Real-World Data (RWD) infrastructure, including data availability, integration capacity and longitudinal follow-up. Assessment included data volume, optimization of data identification and extraction process and overall suitability to support agile and scalable RWE generation.
RESULTS: 14 sites across 6 countries were assessed. We focused our research on Lung cancer data; a significantly large number of patients (total number 7000, average per site 500) are treated yearly at these institutions, providing access to large cohorts for RWE generation. 11 sites have an electronic data querying system, streamlining study population identification based on structured data fields and reducing the need for manual abstraction. Importantly, no lag between data capture in the EMR and accessibility for research was reported. Consistent availability of disease-relevant data such as longitudinal TNM staging, presence of comorbidities, metastasis diagnosis techniques, biomarker expression (including longitudinal) and treatment information (including concomitant medications) was reported across sites, confirming access to RWD from multiple internal sources/departments.
CONCLUSIONS: Sites’ EMR data provides rich, clinically meaningful and up to date data of value for RWE generation in oncology. Technological maturity of sites enabling digital approaches to identifying and extracting large cohorts’ data is advantageous in overcoming operational challenges associated with manual review of records. This facilitates delivery of RWE studies with clinically-rich data within optimized timelines and with minimized single-investigator selection bias and associated challenges for interpretation. Specific use cases will be presented.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
RWD82
Topic
Clinical Outcomes, Real World Data & Information Systems, Study Approaches
Topic Subcategory
Data Protection, Integrity, & Quality Assurance, Distributed Data & Research Networks
Disease
Oncology