Data Asset Series


The Big Picture

Tudor Health was founded by a group of scientists and industry experts to improve patient care in hyper-specialized markets. We provide multinational (not just USA) RWE data for highly specialized disease cohorts, starting in Oncology and Non-Alcoholic SteatoHepatitis (NASH). Our organization supports drug commercialization and development in highly specialized markets. Tudor Health uses a novel, patient-centric approach to generating genotype and phenotype data. Circumventing large, monolithic EMR systems, we can provide hyper-targeted disease cohorts and monitor them over time. 

Data Capture 

What kind of data do you have?

Longitudinal clinical histories that emulate EMR data 

What are your sources?  

Treating physicians

What is the granularity of the data? 


What is the frequency of data refreshes?


How much is the Data lag?

30 days

What is the overall capture rate? 

We are not a large-scale data provider like claims or EMR but focus on hard to find patient cohorts 


We capture International data by emulating EMR data without being tied to a specific technology. We use a novel model to engage HCPs so that we revisit the same HCPs to obtain longitudinal data on their patients. Using screening and over-sampling methods, we source data on previously difficult to find patients in unusual cohorts or rare diseases. Data capture also includes genotypes, biomarkers, and other factors relevant to the target patient population as well as detailed physician notes. Primary use in pharma has been related to the characterization and measurement of patient segments and patient journeys within niche patient cohorts. One case study enabled the client to identify segments of HCPs and segments of patients in NASH (which currently does not have a labeled therapy). This allowed for the creation of patient journeys covering diagnostics and treatment for multiple segments within the NASH population, including a critical genotype.


An example of a core market gap that we fill is in oncology where other sources are tied to specific treatment locations, we collect data in all treatment centers, including community-based. Another example is in our ability to collect comparable longitudinal patient data ex-USA. We are not an effective tool for HCP targeting activities, although analysis of Tudor Health data could be used to build models for application to larger sources (like claims) for targeting purposes. Our syndicated data business model also gives us the opportunity to market disease focused reports – certainly something to look out for. 

How to Start Exploring 

Starting with our website (, anyone interested in our data should contact us directly. We liaise with the client before purchase, including all elements of the expected data (only in our case this can also include targeted data elements as well as counts and samples). A typical engagement starts with the client needing to get more detailed patient-level data on a niche cohort (frequently a rare disease). Our business model is syndicated data that we collect and then license to multiple clients. In the event that a client comes to us with a need in an area that has already been captured, we will engage on existing data and updates (including new data elements). When it is a new disease area to us, we work with the first client as an early-adopter of data for that disease. By collecting data on relevant patients we avoid having to collect data on large populations from which the target data are retrieved. 

Where do you see healthcare data used by pharmaceutical manufacturers
evolving in the next 2 - 3 years? 

One of the primary reasons why we launched Tudor Health is because we see increased demand for patient-level data that can be used for commercial purposes internationally. Registry and similar data are available, but usually with restrictive data use agreements. Our model circumvents that problem. In addition, much of the patient data captured at the moment comes from specific technology (EMR from a particular vendor) which severely restricts the scope of the data. Our model circumvents that problem. We also see an increased use of AI/ML in commercial applications and this must drive increased use of patient-level and HCP-level data – across all countries and not just in the USA.

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