The challenges of working with real world data and key ways to overcome them

Real world data (RWD) is uniquely positioned to support commercial teams by providing a clear, impartial view of what’s happening in the market today. RWD that integrates medical and pharmacy claims, EHR, and specialty datasets provides a view of hundreds of millions of patients, pharmacies, payers, HCPs and sites of care.

However, deriving accurate and unbiased insights from RWD, particularly from claims data, can be challenging. Without careful consideration and a deep understanding of the common pitfalls associated with RWD, analysis results can be prone to bias and errors.

In this report, Clarivate expert delves into the challenges faced when working with RWD and highlights key strategies to overcome them, leveraging broad ecosystem of data assets, advanced analytics, and human expertise.

In this report, you will learn more about:

  • What is the most prevalent bias in RWD analysis?
  • How can claims coding nuances impact all claims-based analyses?
  • What is the “first line of defence” when you’re facing challenges with both open and closed claims data?
  • How can advanced analytics and machine learning help by filling gaps in data?
  • How to avoid the scenario of “over-promising and under-delivering” and “garbage-in, garbage-out” when working on the RWD analytics project?