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Case Studies

Rep-Centric Field Force Tool

Client Situation A midsize biopharma client wanted to create a rep-centric tool to help them understand their targets and map against current performance and gaps, identify changes in market scenario, manage schedules, prioritize targets and deliver contextual content Trinity’s Role Trinity developed a comprehensive field force tool to track target treatment paradigms, analyze market drivers and identify Next Best Actions (NBAs) ­Integrated tightly with rep calendars to provide triggers with insights and recommendations contextual to rep schedules ­Leveraged multiple data…

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Patient Finding with AIML: Increasing Targeting Precision and Field Force Pull Through

Available On Demand

There are four key pieces to Patient Finding: Identifying, collating and integrating the right data Defining the appropriate test population Building the right features in the model Planning and executing actions once you’ve found the patients All four are crucial to successfully find patients. Join Adrienne Lovink, Partner and Head of Real-World Evidence (RWE) at Trinity Life Sciences, as she hosts a lively discussion on the ins and outs of Patient Finding with Trinity panelists Steve Laux, Vice President of…

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AIML-Based Patient Finding—Threading the Needle in the Haystack

Published July 21, 2023

For life sciences companies focused on rare diseases, accurate patient finding is a worthy challenge—one deserving dedicated time and resource to tackling and solving. The benefit of enrolling even one new patient is large, both for the lives of patients in need and the commercial success of the therapies. Why is patient finding in rare disease such a challenge? The hallmarks of a rare disease work against traditional targeting methods: small patient population sizes, complex disease recognition, lengthy roads to…

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Case Studies

Improving Targeting Precision and Field Force Direction through AIML-based Patient Finding

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…

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