Reimagining the infrastructure of cancer care
Clinicians, researchers, and regulators rely on Flatiron technology and evidence to learn faster and power smarter care for every patient. Because transforming cancer care takes all of us.
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.
Olivia Barrett
Healthcare Business Analyst, Hematology Oncology Associates of Central New York
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.
H. Mark Lin, PhD
Senior Director, Global Evidence and Outcome Research in Oncology at Takeda
Source: ResearchX
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.
Dannelle M. Palmer, MBA, PMP
Chief Operating Officer, OncoBay Clinical
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 panel will debate the potential of RWE to address health disparities in HTAs, and whether RWE transferability is a major concern and potential barrier for HTA bodies when deciding whether treatments could undress underlying health disparities.
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
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
Flatiron Participant:
Harlan Pittell, PhD, Quantitative Scientist
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
This education symposia focuses on the importance of a data-quality framework in handling the increasing complexity and diversity of real-world data.
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
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
Poster Code: RWD51
Flatiron Participant:
Wendy Wang, PhD, MPH, Senior Quantitative Scientist
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
This forum session, led by ISPOR’s Open Source Models (OSM) Special Interest Group (SIG), will discuss challenges in defining ‘open-source’ in the context of Health Economics and Outcomes Research (HEOR).
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
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
Flatiron Participant:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning
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?
Flatiron Participant:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning
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
This workshop is focused on the potential of using real-world evidence (RWE) beyond descriptive statistics in drug reimbursement decisions. The discussants will share various advanced analytics that can be used with RWE, such as hybrid RWE-enhanced extrapolation and synthetic control arms, and potential analytical biases that can be mitigated. The manufacturer’s perspective on how RWE can support reimbursement submissions is also discussed, as well as the health technology assessment perspective on how RWE analytics can reduce uncertainty.
Flatiron Participant:
Blythe Adamson, PhD, MPH, Principal Scientist, Machine Learning
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
This research is focused on establishing emerging good practices for the use and interpretation of electronic health record (EHR)-derived real-world data (RWD) for health technology assessments. The task force will discuss data quality frameworks and data provider issues, as well as an overview of EHR data guidelines by NICE in the UK, ICER in the US, and CADTH in Canada.
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
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
This research is focused on the importance of real-world data (RWD) information systems in precision medicine to understand patient outcomes for smaller populations and keep up with the evolving standard of care. Larger-scale and detailed RWD is required for applications such as identifying at-risk populations, addressing inequities in care delivery, and understanding outcomes for patients with rare biomarkers.
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
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
This workshop will discuss the challenges of health technology assessments (HTA) when direct comparative clinical data is lacking and how real-world evidence (RWE) can be used to address this evidence gap.
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
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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Our mission is to improve and extend lives by learning from the experience of every person with cancer.
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Reimagine the infrastructure of cancer care with a community that values integrity, inspires growth, and is uniquely positioned to create a more modern, connected oncology ecosystem.