Comprehensive, high-fidelity, patient-level data is critical for assessing disease burden and generating insights that drive evidence-based medicine. Incorporating clinical and patient-characteristic data (e.g., lab, genomics, race and ethnicity) ensures a holistic and accurate view of the patient.

HEOR Studies

>50% of teams report these 8 "significant to very significant challenges" impede research:

  • Linking claims data to specialty, proprietary, or other external datasets
  • Avoiding regional bias
  • The legal process for procuring data
  • Timely data delivery
  • Seeing the complete longitudinal view of the patient
  • Receiving support with analyzing/working with data
  • Seeing robust patient coverage, including genomics and biomarker data
  • Obtaining patient mortality data

What’s behind the challenge? 

  • Proprietary (vs. ubiquitous) tokenization makes data linking extremely complex and fraught with pitfalls
  • Lack of access to high-fidelity data that accurately reflects the patient population by geographic region
  • Data silos that require teams to identify and assess data sources, initiate multiple contracts, and manage multiple vendors
  • Using data that does not capture multiple years of the patient journey
  • Purchasing data cuts on the marketplace vs. working with a data partner that provides thought leadership support
  • Using data that doesn’t include both closed and open claims or integrated specialty data