• Obtaining granular insights across the patient journey is critical to resolving therapy adoption/adherence hurdles
  • AI/ML enables predictive analytics to engage HCPs and patients at the most appropriate time
  • Identifying underserved populations to advance care equity is the right thing to do and has the potential to increase market size, market share, and return on investment (ROI)4
Driving Therapy Adoption

50% of Commercial teams report they face a "significant to very significant challenge" obtaining accurate insights into:

  • The care setting where therapy is being prescribed
  • Identifying eligible patients at the appropriate time to enable timely HCP engagement
  • Patient “drop-offs” (therapy discontinuation)
  • Underserved populations 
  • Patient access barriers, such as denials by payer and payer type

What’s behind the challenge? 

  • Datasets that don’t include both open and closed claims data
  • Hurdles to integrating/harmonizing data sources
  • Not using technology solutions enhanced with AI/ML
  • A lack of high-fidelity (representative) demographic data
  • Limited or no access to specialty data sources
  • Using claims data alone to determine primary and secondary insurance coverage and identify access barriers (too many empty fields, inaccuracies, and “Payer Unknown” statuses)