Expanding the possibilities for point of care solutions in oncology
Point of care
We’re partnering with cancer centers to deliver a better patient experience, strengthen practice health, and close the gap between care and research.
Realizing the full potential of real-world evidence in oncology
Real-world evidence
With Flatiron’s integrated RWE solutions, our customers are harnessing new approaches to evidence generation through our engaged care network, oncology-specific expertise, and fit-for-purpose scientific methods and tools.
Bridging the gap between care and research
Clinical research
We’re transforming clinical research through technology that seamlessly integrates into everyday patient care.
Transforming global oncology care and research
International
We’re accelerating cancer research and improving the quality of care globally, using real-world evidence to improve patient outcomes, inform policy, and advance research.
Hear from our customers
With Flatiron Assist™ surfacing and prioritizing appropriate regimens for physicians, based on key clinical and prognostic factors, we are driving value with every treatment decision.

Flatiron was chosen because it’s fit-for-purpose. You have the right population and good sample size. Unstructured data covers the primary endpoint of the trial, which is overall response rate. A key learning is you need to choose a database that is fit-for-purpose with good quality.
Source: ResearchX
Working with partners like Flatiron and technology like [Flatiron] Clinical Pipe™ and Flatiron Vessel™ has been an excellent opportunity for us to really build processes and systems around the technology that is available today and get those technologies to work together.

Featured Content

Monday, May 8 | 10:15 AM - 11:15 AM ET
RWE and health disparities in HTAs: Is transferability the main barrier for equity data sharing across borders?
This discussion will include presentations from consultancy, industry, and epidemiological perspectives, followed by an interactive discussion.
Flatiron Participant:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning

Monday, May 8 | 10:15 AM - 11:15 AM ET
Podium Presentation - Measures of neighborhood structural racism and overall survival among patients with metastatic breast cancer
Harlan Pittell, PhD, Quantitative Scientist

Monday, May 8 | 11:30 AM - 12:15 AM ET
Data quality 2.0: The future of real-world evidence
It will also explore how a fresh perspective on data quality enhance the impact and applicability of real-world evidence, particularly in value assessments.
Flatiron Participants:
Javier Jimenez, MD, MPH, Chief Medical Officer
Emily Castellanos, MD, MPH, Senior Medical Director

Monday, May 8 | 3:15 PM - 6:45 PM ET
Poster Session 2 - 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
Flatiron Participant:
Wendy Wang, PhD, MPH, Senior Quantitative Scientist

Monday, May 8 | 3:15 PM - 4:15 PM ET
What is an open source model? Forking a path to definition
The panel will explore the meaning and scope of open-source in HEOR, the practical value of agreed definitions and the role of the OSM SIG in promoting their adoption. Finally, they will discuss how the HEOR community can learn from other contexts and review the different perspectives that ought to be considered. The OSM SIG will develop a manuscript on this topic informed by audience contributions.
Flatiron Participant:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning

Monday, May 8 | 4:45 PM - 5:45 PM ET
Podium Session - Machine Learning in Outcomes Research
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning

Tuesday, May 9 | 8:30 AM - 10:00 AM ET
AI wants to chat with you: Accept or Ignore?
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning

Tuesday, May 9 | 10:15 AM - 11:15 AM ET
Beyond burden of illness - Using RWE for advanced HEOR analytics
Flatiron Participant:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning

Tuesday, May 9 | 11:45 AM - 12:45 PM ET
Assessing real-world data quality from electronic health records for health technology assessments
The panel will also discuss limitations of EHR data, such as privacy and confidentiality issues, consent issues, data security concerns, and funding sources. Finally, the speakers will discuss future directions for the field.
Flatiron Participants:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning
Seamus Kent, PhD, MSc, Senior Adviser, HTA and Market Access

Tuesday, May 9 | 1:45 PM - 2:45 PM ET
Larger, deeper, and in real time: Applications of machine learning and natural language processing on electronic health records to learn from the patient journey at scale
While conventional data sources like claims data lack clinical depth, unstructured EHR data offers more information about biomarker results. Machine learning and natural language processing techniques have improved upon traditional manual curation methods, generating deep insights at scale and in real time.
Flatiron Participant:
Katherine Tan, PhD, Senior Quantitative Scientist, Machine Learning

Wednesday, May 10 | 8:00 AM - 9:00 AM ET
Routing RWE sources in HTA submissions when standard of care is not established: How to robustly validate your uncertainties
Additionally, two recent NICE submissions will be presented as case studies for audience participation and there will be a discussion on how current RWE frameworks could guide RWE decisions and HTA submissions.
Flatiron Participant:
Seamus Kent, PhD, MSc, Senior Adviser, HTA and Market Access
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