Real World Evidence for Your Study

Discover the potential of the Real World Data derived from the millions of electronic health records
Data from over 25 regions in Russia
Data from over 25 regions in Russia
60,000,000+ electronic health records
60,000,000+ electronic health records
Experience in working with biomedical data and biostatistics processing
Experience in working with biomedical data and biostatistics processing
Real world evidence (RWE) and real world data (RWD) have been gaining a lot of interest with their role in global healthcare becoming more and more apparent. Real world data is the data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. Real world evidence is the clinical evidence that determines the processing and potential values and challenges of a drug or a medical device collected through analysis of RWD.
The Data MATRIX RWE team will guide you through the intricacies of real world data sources and help you establish and launch an effective research method to collect the right evidence for your projects.
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Taxonomy of the Real World Evidence

Taxonomy of the Real World Evidence

Our Real World Evidence Solutions for your Study

Epidemics Studies

  • Clinical portrait of patients of a given disease
  • Patient’s journey within a medical facility
  • Drug treatment patterns

“Smart” patient registry

  • Retrospective data processing
  • Automatic update of registries based on prospective observational data
  • Biomedical statistical analysis based on registry data

Pharmacoeconomics studies

  • Modeling patient and healthcare system expenses on diagnostics and treatment
  • Determining RWD for modeling

Marketing analysis of prescriptions

  • Pool of data collected based on anonymised electronic health records
  • Reasons for switching or cancellations of therapies
  • Tracking of treating doctors’ appointments by specialization

Drug treatment efficacy and safety

  • Biomarkers analysis based on lab test data
  • Determination of SUSAR submitted by patients and identified by a doctor
  • Mathematical modeling of drug efficacy within different patient cohorts and treatment lines

Post-authorization and additional studies

  • Comparative efficacy studies
  • Use of drugs outside of indications
  • Pharmacovigilance (side effects)
  • Clinical trial design
  • Orphan disease patient search
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