Unlocking the Power of Real-World Data in Hematology

For years, cancer research has relied heavily on data gathered from clinical trials, which, while rigorous, often represent a limited segment of the patient population. Flatiron Health’s Panoramic datasets address this critical gap by harnessing the power of real-world data (RWD) – information collected during routine clinical practice. This approach provides a more comprehensive and representative picture of the patient experience.

The newly released datasets encompass over 505,000 patient records, spanning five distinct B-cell lymphoma subtypes and multiple myeloma. This represents a six-fold increase in cohort size compared to Flatiron’s previous hematology collections, and forms part of a larger repository exceeding five million cancer patient records – a staggering 1.5 billion data points. This scale allows for the identification of patterns and insights that would be impossible to detect in smaller, more focused studies.

Central to this advancement is Flatiron’s integration of artificial intelligence (AI) and large language models (LLMs). These technologies are not merely used to collect data, but to extract and validate clinical information at an unprecedented scale, ensuring accuracy and reliability. This validation process is crucial, as the quality of RWD has historically been a concern. Flatiron’s validated data quality framework provides a robust solution.

The datasets capture crucial longitudinal details, including measurable residual disease (MRD) testing and CAR-T therapy utilization – two key indicators of modern hematology practice. This depth of information enables researchers to examine treatment patterns, adherence, and outcomes with greater precision, paving the way for personalized therapeutic strategies and more inclusive clinical trial designs.

A Decade of Building an Oncology Evidence Infrastructure

Nathan Hubbard, Chief Executive Officer of Flatiron Health, emphasized that these datasets are the culmination of a decade-long commitment to building a global oncology evidence infrastructure. He highlighted the synergy between the company’s established AI and machine learning capabilities and a dedication to scientific rigor, resulting in more precise and individualized care for patients battling blood cancers.

The enhanced interoperability of these datasets is another significant benefit. Researchers can now seamlessly analyze data across related lymphoma types and track disease transformation over time, providing a more holistic understanding of disease progression. This is particularly valuable in hematologic malignancies, which can often evolve and change over the course of treatment.

Flatiron’s hematology program is specifically designed to support detailed investigations into complex subgroups, such as high-grade B-cell lymphomas with specific genetic rearrangements or multiple myeloma cases with high-risk cytogenetic profiles. Furthermore, the data facilitates the assessment of real-world effectiveness, molecular response, and adverse events – critical dimensions that complement the findings of traditional clinical trials.

By integrating insights from both inpatient and outpatient care, infusion and oral regimens, and cellular therapies, the Panoramic datasets offer a truly comprehensive view of the patient journey – a “panoramic” perspective that was previously fragmented and difficult to obtain. What impact will this holistic view have on future treatment protocols?

Flatiron Health’s commitment extends beyond data collection and analysis. With over 250 publications and 275 research presentations scheduled for global conferences in 2025, including ISPOR Europe and ASH 2025, the company is actively shaping the standards of digital oncology. This proactive dissemination of knowledge underscores their dedication to accelerating innovation in the field.

The company’s broader goal is to responsibly leverage AI and RWD to expand scientific access to rare disease populations, address persistent data gaps, and ultimately, accelerate the development of the next generation of hematology treatments. Could this approach revolutionize the way we approach rare blood cancers?

Frequently Asked Questions About Flatiron Health’s Hematology Datasets

1. What is the primary benefit of Flatiron Health’s new hematology datasets?
   The primary benefit is a significantly expanded and more representative dataset of blood cancer patients, enabling more robust and reliable research into disease patterns, treatment effectiveness, and patient outcomes.
2. How does Flatiron Health ensure the quality of its real-world data?
   Flatiron Health employs a validated data quality framework and leverages AI and machine learning to extract and validate clinical information at scale, ensuring accuracy and reliability.
3. What types of hematologic malignancies are covered by these datasets?
   The datasets encompass five B-cell lymphoma subtypes and multiple myeloma, providing a comprehensive view of these common blood cancers.
4. How can researchers access Flatiron Health’s Panoramic datasets?
   Researchers can explore collaboration opportunities and data access options through the Flatiron Health website.
5. What role does AI play in analyzing these hematology datasets?
   AI and large language models are used to extract, validate, and analyze the vast amount of clinical data, identifying patterns and insights that would be difficult or impossible to detect manually.
6. What is measurable residual disease (MRD) testing and why is it important?
   Measurable residual disease (MRD) testing is a highly sensitive test used to detect any remaining cancer cells after treatment. It’s a key indicator of treatment response and can help predict relapse risk.