Real World Evidence comes into play when clinical trials cannot really account for the entire patient population of a particular disease.

Our unique approach to handle multiple sources, structured & unstructured data, inconsistency, variability & complexity within an ever-changing regulatory environment

We go through each process in depth, so as to facilitate high quality real world data analysis

Data Due Diligence

  • Healthcare data is unique and difficult to measure
  • It resides at multiple places in multiple formats
  • Making it essential to conduct a rigorous data due diligence
  • As it will also lay the groundwork for a successful real world data analytics exercise

Data Abstraction

  • Clinical data abstraction involves extracting & mining critical clinical information & its components from paper media to electronic media
  • We use manual, NLP & simple query based abstraction tools with required quality assurance & data validation to generate abstracted data

Data Curation

  • Involves refining and enriching the data by identifying & correcting incomplete and incorrect data, as well as harmonizing, validating, standardizing &QAing it
  • Purpose is to maintain, preserve & add value to the abstracted data throughout the lifecycle of the data

Data Analysis

  • Analyzing the data comprehensively is of immense value as data is now the value generator for most healthcare companies
  • Our analyzed data is visualized in the form of Qlick view, tableau, Excel, SPSS, etc

Data Modelling

  • Transforming raw clinical data into high value clinical data model helps our clients to access data in a fashion that is easily understandable, meaningful & useable
  • Electronic capture data model
  • Review data model
  • Submission data model
  • Analytics data model