Improving targeting precision and field force direction through AIML-based patient finding

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Background

  • A global rare disease company was looking to improve targeting precision and support field team effectiveness
  • Traditional targeting was non-viable due to the small size of the patient populations, complex disease recognition and diagnosis, and restrictive therapy eligibility criteria
  • Attempts by a prior analytics partner to use rule-based alerts failed, and even after two years, no new patients had been identified
  • Given the small number of patients in each indication, every new start is high value, both for the lives of patients in need and for the commercial success of the therapies

Trinity’s Solution

  • Create an AIML model that identifies clinically on-label patients—rather than simply patients with the disease
  • ‘Learn’ from on-therapy patients to identify how patients present prior to therapy initiation and execute AIML models to flag patients based on the probability that they are candidates for treatment
  • Deploy bifurcated notifications to both inside and field-based reps through ‘push’ alerts within the client’s CRM and enable ‘pull’ solutions to support call planning
  • Success drivers include:
    • Data acumen to evaluate and select source data
    • Therapeutic area expertise
    • Deep collaboration with Medical to refine and calibrate models
    • AIML experience to optimize feature selection and approach

Project Outcomes

Designed and deployed AIML models for three indications in 8 weeks

Narrowed field team focus from >25,000 HCPs to ~1,000 highest priority HCPs

Identified 100 high probability targets within first 2 months of launch

Patients confirmed through Machine learning.

At least 5 patients confirmed to be clinically on-label for therapy within first 2 months

Generative AI

First new patient enrolled within 5 weeks of initiating alerts

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