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Case Studies

NBA Insights Solution

Client Situation As part of an enterprise-wide Digital Transformation initiative, our client was looking to optimize effectiveness of marketing execution channels—and maximize clinical and commercial outcomes—by personalizing campaigns through engaging HCPs with the most relevant content (safety, efficacy, support, etc.) The client was first looking to scale an existing brand-specific NBA solution across all their inline brands. However, there were several challenges with the initial application and this approach: Basic, limited functionality with no prediction capability around sequential touchpoints High…

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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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Case Studies

Next Best Engagement Recommendation System

Client Situation A large biopharma customer wanted to optimize their digital channel strategy and outreach to most effectively engage with HCPs Trinity’s Role Trinity developed an AI solution that predicts the next best digital channel, content and cadence to engage with individual HCPs Although originally created for one brand, the technology and AI model was crafted so that it could be scaled and easily configured to add different criteria and datasets After success with the initial brand, Trinity expanded the…

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Blog

Insights & Analytics in Life Sciences: How to Embrace a Future with Generative AI

Published December 18, 2023

First, A Few Potentially Unsettling Truths Adoption and adaptation to Generative AI (GenAI) is a strategic imperative for pharma commercial insights departments if they want to maintain and enhance their relevance, impact, and strategic value within their organizations. Organizations are increasingly data-driven; market research teams that do not leverage AI will struggle to provide the timely, nuanced insights that modern pharma marketing organizations demand, diminishing their role in critical decision-making processes. Top talent gravitates towards innovation. Departments that fail to…

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Webinars

Cracking the Code on Generative AI: From Hype to Impact

Available On Demand

Watched our AIML 101 webinar and ready to learn more? Trinity Life Sciences is excited to continue the conversation with our next webinar, which will dive deeper into how pharma companies can move beyond the hype to unlock the value of Generative AI (GenAI). Join Trinity experts, Steve Laux, VP of Commercial Insights & Advanced Analytics, Nabha Subramanya, VP of Data Science and Patrick Waring, Director of New Products & Strategy, as they sift through all the noise surrounding GenAI and share…

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Webinars

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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Blog

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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Webinars

Artificial Intelligence & Machine Learning 101

Available On Demand

You have likely heard all the buzz around Artificial Intelligence and Machine Learning (AIML), but what does it really mean? It’s time to cut through the complexity during Trinity Life Sciences’ AIML 101 webinar. This webinar is designed for the AIML novice, who knows very little about the topic but doesn’t want to be left behind. AIML is here. Be ready with Trinity. Key Webinar Topics What is Artificial Intelligence? What is Machine Learning? Are they different? We’ll demystify how…

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Webinars

Driving Commercial Success with AI: Build, Calibrate and Operationalize

Available On Demand

Now Available On Demand: In today’s cutthroat life sciences marketplace, commercial effectiveness in all its parts has never been so important.  From field force effectiveness to optimization of promotional investment through to Next Best Action implementation, tailoring the customer experience through content and tactics is the focus. Cross-functional coordination across teams and operationalizing at scale are the goal. Come join Nabha Subramanya, VP of Data Science at Trinity Life Sciences and Anushank Anand, Director in Trinity’s Analytics practice, as they…

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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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