
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