MedPath

Flatiron Health Breaks New Ground with Cross-Border Patient-Level Oncology Data Sharing

7 months ago4 min read

Key Insights

  • Flatiron Health has successfully created a multinational oncology database integrating patient-level real-world data from electronic health records across the US, UK, Germany, and Japan, overcoming significant regulatory and technical barriers.

  • The secure, cloud-based "trusted research environment" allows researchers worldwide to analyze harmonized cancer-specific datasets that are refreshed every 90 days, providing unprecedented insights into treatment effectiveness across different healthcare systems.

  • This groundbreaking initiative could transform global oncology research by enabling comparative studies of treatment pathways, providing diverse real-world control arms for regulators, and potentially allowing evidence "transportability" between countries.

Flatiron Health has achieved what many in the research community initially deemed impossible: creating a secure framework for sharing patient-level real-world data (RWD) across international borders. The company's groundbreaking initiative now enables oncology researchers to analyze harmonized electronic health record (EHR) data from the United States, United Kingdom, Germany, and Japan within a secure, cloud-based environment.
"There is so much to learn from the differences between countries with different clinical guidelines and treatment pathways and many of the questions are unanswered about which treatments and pathways can give patients the best outcomes," explains Blythe Adamson, Ph.D., Flatiron's international head of outcomes research and evidence generation who led the project.

Overcoming Regulatory and Technical Challenges

Creating this multinational dataset required navigating complex regulatory frameworks, particularly the EU's General Data Protection Regulation (GDPR) and Japan's Act on the Protection of Personal Information (APPI). Flatiron employed rigorous techniques including pseudonymization, obfuscation, redaction, and masking to protect patient privacy while maintaining data utility.
The technical challenges were equally daunting. Each country has unique documentation practices, clinical visit cadences, and data collection methods. Even within countries, data inconsistencies were common. In Germany, for example, cancer care is delivered through numerous small clinics, each storing data differently.
"We had to build custom technical integrations to harmonize this data from diverse EHR systems," Adamson notes. Flatiron's teams worked on-site with hospitals and clinics to integrate their EHR data, developing cancer-specific common data models with input from oncologists across all four countries.
The methodology ensuring data trustworthiness, reliability, and relevance was recently published in ESMO Real World Data and Digital Oncology, detailing alignment with the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) EHR-derived data SUITABILITY checklist.

A Unique Research Resource

The resulting dataset offers unprecedented research opportunities. Unlike traditional aggregated data sharing, this initiative provides access to patient-level information refreshed at least every 90 days, allowing researchers to follow patient experiences in near real-time.
The secure "trusted research environment" grants controlled access to deidentified or anonymized datasets. Properly permissioned researchers can analyze the information using R or Python programming languages or interact with dashboards to examine different patient subgroups.
"It's a dataset that's not found in any other real-world data sources," Adamson emphasizes. The information includes rich unstructured data from documents, including genomic testing reports and physician notes—content typically unavailable in claims data or clinical trial registries.

Global Infrastructure and Expansion

To support this initiative, Flatiron established subsidiaries in London, Berlin, and Tokyo three years ago. These locations house multidisciplinary teams including medical oncologists, software engineers, data scientists, and data insights engineers who collaborate to ensure data quality and relevance.
The company is now expanding its site network within the UK, Germany, and Japan to capture greater geographic variability within each country. The datasets are rapidly growing to include approximately four times more patients powered by at least ten new sites across all markets this year.

Leveraging AI for Enhanced Data Extraction

Flatiron is also advancing its use of artificial intelligence to improve data extraction. In a recent study published in AI in Precision Oncology, the company demonstrated that large language models (LLMs) could effectively extract PD-L1 biomarker data from EHRs.
Notably, LLMs "fine-tuned" with high-quality labeled data outperformed both zero-shot approaches and deep-learning models trained on much larger datasets. This suggests promising applications for enhancing data completeness and usability in the multinational datasets.

Implications for Global Oncology Research

The cross-border initiative offers several significant benefits for oncology research:
  1. Comparing treatment effectiveness across different healthcare systems and clinical guidelines
  2. Providing drug regulators with geographically diverse real-world control arms
  3. Exploring the "transportability" of evidence between countries
Through Flatiron FORUM (Fostering Oncology RWE Uses and Methods), the company is investigating when evidence created in one country can appropriately inform decision-making in another. This could be particularly valuable for nations lacking robust data infrastructure.
"If you can borrow information [from the U.S] and make adjustments to apply it to a different country's patient characteristics, then it really opens up the opportunity to use synthesized data from all around the world to make decisions about drug effectiveness and stewardship of medicines in that country," Adamson explains.
As researchers begin accessing these harmonized datasets, the oncology community anticipates a wave of new studies that could transform our understanding of cancer treatment effectiveness across diverse populations and healthcare systems.
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