REAL DATA
REAL INSIGHT
Transforming Oncology with Real World Data.
Oncology is no different from any other disease.
You should be able to answer all oncology use cases with real-world data.
Example Use Cases
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What is the burden of cancer in terms of incidence, prevalence, and survival across different populations?
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Are new cancer therapies as effective in the real world as they are in clinical trials?
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How does cancer treatment effectiveness vary across subgroups (e.g., race, age, comorbidities)?
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What barriers do novel cancer therapies face, and how do these impact outcomes?
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Are precision oncology treatments (e.g., targeted therapies, immunotherapies) being used as expected in practice?
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Does genomic and molecular testing differ by healthcare system or geography, and what are the implications for treatment?
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How do multi-gene panels (e.g., liquid biopsies, comprehensive genomic profiling) affect real-world treatment decisions?
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What are the real-world patterns of adverse events for novel cancer therapies?
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Do comorbidities increase the risk of treatment-related toxicities?
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What is the optimal sequence of treatments for different cancer types?
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How do clinicians decide on therapy sequencing when there are no clear guidelines?
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What are the long-term outcomes and risks (e.g., secondary cancers, cardiovascular issues) for cancer survivors?
Our services
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The complexity of oncology data often
prevents us from answering these
critical questions
with traditional approaches.
If Real-World Evidence (RWE) in oncology is possible,
it’s only through the application of standard methodologies to standardized data.
OHDSI and OMOP have laid the foundation for this approach, making reliable, large-scale research a reality.
At nemesis, we build on this foundation with four essential elements that make oncology RWE achievable:
our solutions
OHDSI
A global, open-source, open-science community that drives collaboration and transparency, ensuring that healthcare research evolves through collective expertise.
OMOP CDM
The OMOP Common Data Model standardizes real-world data from diverse sources, enabling scalable and efficient research that can be reliably compared across systems and studies.
Cancer-Specific Methods & Tools
Our methodologies and tools are tailored specifically for oncology,
ensuring the generation of reliable, reproducible evidence that is rigorously evaluated across various
populations and healthcare settings.
A Network of
OMOP Databases
By utilizing a wide network of OMOP databases, we conduct research at a scale that ensures findings are generalizable across diverse populations and healthcare systems, making the evidence robust and applicable to real-world clinical practice.
Team Members:
Asieh Golozar
MD PhD MHS MPH
Christian Reich
MD PHD
What our customers are saying
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