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 rigorous data due diligence.
- As it will also lay the groundwork for a successful real-world data analytics exercise.
- 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.
- Involves refining and enriching the data by identifying & correcting incomplete and incorrect data, as well as harmonizing, validating, and standardizing it.
- The purpose is to maintain, preserve & add value to the abstracted data throughout the lifecycle of data.
- 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 Qlik view, Tableau, Excel, SPSS, etc.
- Transforming raw clinical data into high-value clinical data models 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