AdInfer allows the screening of large and representative samples from the general population that is the basis for prevalence and incidence studies.
In line with the work done by EVALRI, the IFORD approach in collecting real world data is a huge leap forward in improving the scope and quality of data. The traditional approach of collecting real world data (e.g., claims data, electronic medical records) tries to complete a very limited static set of data fields with hardly any control for accuracy and consistency
Our technology, AdInfer, captures data using advanced artificial intelligence. The principle is simple: like a good doctor, tailoring the questions to each individual, asking the right questions, but unlike a doctor, making use of un-constraint time to get a thorough insight into the health of the patient. The proof of concept has been the capability of our system to screen, recognize and evaluate diseases by directly interviewing subjects.
As our approach does not rely on healthcare professionals, we have used our AdInfer technology to create census-based representative data of general populations. We have these data for countries in Europe, Northern America and Asia. We even have representative longitudinal data from the USA.
AdInfer can contribute to the screening and recruitment of participants for clinical trials, potentially at earlier stages of the disease, and generate new insights for the development of treatments.