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Transforming healthcare with real-world data

Flatiron Health at ISPOR 2023

Incorporated into the theme of the ISPOR 2023 conference is the notion of “impacting innovation, value, and healthcare decision-making.” Flatiron Health’s research presence at this conference highlights our ability to do just that. 

On the live stage at ISPOR 2023, researchers and oncologists from Flatiron present exciting new content, including our advances in AI with deep learning, use of real-world data for health equity research, and our synthesis of data quality frameworks as further demonstrated by value assessment applications. At our booth, our top expert scientists stand ready to engage with attendees in concept development and problem solving with real-world evidence. Our RESEARCH presentations explore timely topics such as structural racism insights for diversity planning, validation of health outcomes, and opportunities for leveraging data linkages such as administrative claims with EHR data.  

Our presence this year represents a pivotal milestone in gleaning insights from a broader spectrum of patient experiences than before, thus reimagining the infrastructure of health care and health policy. We maintain our commitment to using data for good—to understand the value of new medicines and increase access around the world.

Our research


Do the characteristics of the site of care influence outcomes? Associations between community practice-level characteristics and real-world overall survival among patients with multiple myeloma

Wang X., et al.

This study included an analysis of various descriptive statistics at the practice-level, such as patient-physician ratios, the number of visits, physicians, and patients with cancer including multiple myeloma (MM), as well as diversity, location, and clinical factors.

Implementation of a real-world data quality framework in an nationwide oncology electronic health record-derived database

Castellanos E., et al.

This study showcases how to apply a framework that ensures RWD quality by considering important factors like accuracy, completeness, and relevance to a large-scale electronic health record (EHR)-based oncology RWD source.

Development of a derived induction failure and relapse (DIFR) variable for acute myeloid leukemia (AML) using real-world (RW) data from an electronic health record (EHR)-derived database

Fullerton C., et al.

To address the challenge of accurately identifying induction failure and relapse events, researchers in this study developed a novel approach that combines structured and abstracted data sources to derive these events with greater accuracy and precision.

Measures of neighborhood structural racism and overall survival among patients with metastatic breast cancer

Pittell H., et al.

Researchers conducted a study that focused on patients with metastatic breast cancer (mBC) and examined several measures of neighborhood structural racism as indicators of racial and economic segregation to determine if these measures were predictive of survival among patients with mBC.

Assessing missing antineoplastic therapy prior to electronic health record (EHR)-derived first line of therapy after advanced non-small cell lung (aNSCLC) diagnosis

Reiss S., et al.

This study used an administrative health claims linked EHR dataset in an aNSCLC population to identify antineoplastic exposure in claims before the first recorded EHR documented exposure.

Racialized economic segregation and inequities in survival among patients with multiple myeloma

Pittell H., et al.

Researchers conducted a study focused on the association between area-level racialized economic segregation (i.e., Index of Concentration at the Extremes (ICE)) between the most and least privileged groups and overall survival rates for patients with multiple myeloma (MM).

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