In life science brands’ quest for highly targeted, clinically-relevant HCP communications, EHR “trigger” data is an increasingly popular choice. Its benefits include accuracy and control over message deployment, but many brands don’t realize that this precision comes at a cost – decreased share of voice with the physicians who matter.
The Problem with “Multi-Trigger” Programs
To be clear, EHR-based programs that use 1-2 data points (ICD10, NDC, etc.) to align brand information with a specific patient profile can be a cost-effective way to reach the right HCPs. However, when programs are built with 3+ combined data triggers to find complex patient profiles, the number of “triggered” situations declines. That means that brands are delivering fewer overall impressions, achieving a smaller share of voice and reaching a lower number of physicians with their key messages.
While many brands looking for a hard-to-find audience may think “quality over quantity”, it’s not just audience size that’s a barrier to program success. Other significant drawbacks include:
- Reactivity, Not Proactivity: Multi-trigger programs are inherently reactive in nature, so they are unable to engage and build mindshare with physicians in the days approaching an eligible patient visit
- Local Data Limitations: Data triggers can only be drawn from local EMR systems – these models can’t reflect longitudinal or sequential data from additional patient records
- Un-Triggerable Characteristics: Not all eligibility criteria can be reduced to “triggerable” data points, especially as patient profiles grow more complex
For brands that need more precision beyond 1-2 key NDC or IDC10 criteria, there’s a better way to build a data-driven targeting model: predictive patient finding.
Predictive Patient Finding with AI and RWD
Today’s AI-driven targeting models are capable of identifying complex patient profiles with even more accuracy than traditional triggers, and without sacrificing physician reach. They do this by shifting from a reactive “trigger” approach to a proactive, predictive patient flow model. Here’s how it works:
- Patient qualification criteria is applied to both local EHR systems and other RWD sources, and used to train an AI-model on the characteristics that that define eligibility
- This predictive patient model then runs a weekly, real-time check for patients approaching brand eligibility milestones and identifies their treating HCP(s)
- The predictive model, generated weekly, identifies lists of HCPs with eligible patient inflow, and concentrates brand messaging to these HCPs during their “relevance windows”
The result? Up to 50% increase in brand share-of-voice without sacrificing targeting precision, plus greater cost-efficiency from less wasted impressions and better cross-channel alignment.
Shift Your HCP Engagement Strategy from Reactive to Proactive
If you’re struggling to balance targeting precision and HCP reach for your life science brand, we’d welcome the chance to show you how a predictive patient approach maximizes program impact. Connect with us today!
