Trinity AI

Trinity AI delivers uniquely tailored decision intelligence to life sciences organizations:

Demonstrated Impact

• Extensive track record of success in delivering solutions that address the needs of various life sciences stakeholders (e.g. patient finding, promotional excellence for marketing, commercial operations/field force enablement, customer behavior and patient engagement)

Comprehensive Commercial Use Case Expertise

• Healthcare data acumen to evaluate and select the right data sources to meet the business outcome/objectives
• Understanding of the right modeling techniques and approaches to use
• Pre-built “accelerators” curated from delivering hundreds of projects (e.g. features library, baseline business rules, data extractors, master code sets, etc).

Focus on Speed

• “Data science” as-a-service with capabilities built for speed
• Solutions designed for organizations to scale their data science portfolio to manage increasing workload and demand quickly and easily
• AIML techniques to process and extract insights from large datasets

Seamless Integration across Trinity Offerings

• TGaS benchmarking contextualizes analytics and insights results with others across the industry and helps end-users prioritize how they allocate resources
• AI insights effortlessly flow into projects powered by Trinity’s broader portfolio (BMEx, Patient Centricity, etc.) and Analytics portfolio

Expertise in Machine Learning Operations (ML Ops)

• ML ecosystem creation in the client environment and execution support from advisory, implementation to ongoing maintenance

Flexible Range of Engagement Models

• Offerings addressing a range of needs for organizations just starting to use data science to large scale Data Science COEs
• Solutions ranging from fully-deployed on-premise analytics to fully-managed ecosystems
• Flexible, transparent support so clients can be as involved as they want as they evolve
• Long-term partner approach to provide continual measurement, optimization and guidance

2025 Featured Specialist

Operationalizing Generative AI in Life Sciences

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• Prescriber AI
  • Understand prescriber behavioral segments
  • Predict behavior with propensity and look-alike modeling (e.g., likelihood to switch brands, decline/grow or churn)
  • Identify early adopters and influence network with referral network graphs
  • Leverage Dynamic Segmentation & Targeting
• Provider Engagement (NBA)
  • Predict the right marketing channel (personal & non-personal), message and cadence to activate and drive provider brand engagement and utilization with CE Orchestrator
  • Leverage Dynamic Targeting/Target Lists and Rep Suggestions
• Marketing/Promotion Effectiveness
  • Measure and optimize promotional investments across different channels to maximize ROI and improve customer experience with Marketing Mix (MMX) Studio and Portfolio Spend allocation
  • Leverage multi-touch attribution models, scenario testing and control stratification testing to facilitate campaign experiments and effectiveness
  • Analyze point-in-time promotion impact (e.g. A/B testing of speaker program)
  • Optimize promotional tactics (e.g. dynamic call planning, distribution of TV sends across DMAs)
• Patient AI
  • Find patients in rare diseases
  • Predict patient drops from PSPs, therapy progression and regimen change
  • Design interventions to engage patients in PSPs to improve adherence and persistence
  • Engage patients with Next Best Action
  • Identify sites via referral networks
• Generative AI & Natural Language Processing (NLP)
  • Build and implement secure, client-specific Generative AI solutions for Market Research, Medical Affairs, Field Force Enablement, Competitive Intelligence, Insights Synthesis, Workflow Efficiencies and much more
  • Leverage Voice of Patient Analytics and identify Adverse Events with NLP
  • Apply Computer Vision and Optical Character Recognition (OCR) technology to solve complex problems
• Machine Learning Operations (ML Ops)
  • Operationalize and scale ML Models across model deployment, retraining, registry and versioning, AI governance, etc.
  • Monitor, maintain and govern ML Models in deployments.

Trinity AI is a comprehensive portfolio of data science applications, robust capabilities and industry-specific expertise that supports a range of use cases.

Clients are able to leverage:

• Deep expertise in end-to-end design and deployment of AIML solutions, starting from raw data and delivering advanced insights rapidly through robust, best-in-class applications
• Broad knowledge of advanced analytic and AIML methods, using structured and unstructured data, to support new and custom use cases
• Productized solutions and capabilities built specifically for life sciences use cases, such as Medical Affairs Insights AI, Field Insights AI, the CE Orchestrator offering and MMX Studio
• Efficiencies that accelerate projects with pre-built models, feature libraries and intuitive, self-service user interfaces
• Strong subject matter expertise across therapeutic areas in the analytics process
• Extensive experience across datasets that support varied analytical needs throughout the product lifecycle

Trinity AI projects deliver:

• Accelerated time to peak brand revenue through disruptive methodologies
• Enhanced understanding of patient journey by predicting patient treatment and behavior patterns
• Optimization of marketing investment and budget allocation through profit and revenue forecasting
• Commercial operation optimization, including streamlined customer engagement
• Improved brand performance and elevated customer experience

Trinity AI Brochure

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Case Studies

• Hyper-Personalized HCP Recommendations

  Client Situation

  The objective was to increase market share of target prescribers through an AI-powered Next Best Action model

  Trinity’s Role

  We were able to deploy this solution in 3 weeks. The project included:

  • Discussions with the client team to finalize standardized business rules for brand-agnostic data and KPI definitions
  • Master dataset development through applying business rules and automated monthly PySpark code refreshes
  • State-of-the-art machine learning algorithms like reinforcement learning, deep neural networks with dynamic recalibration and automated hyperparameter tuning for enriching HCP recommendations
  • Automated data engineering pipelines to append the recent data to master data set at regular intervals
  • Sequential Content – channel combination is recommended for each HCP at timely intervals without any manual intervention

  Project Outcomes

  This project resulted in a 9% increase in engagement rate. Deliverables included:

  • Hyper-personalized recommendations for HCPs with relevant content, channel and sequence
  • A parameterized, brand-agnostic model with incremental training enabled, which adapts to changing prescriber behavior and provides optimal recommendations
    • Seamless data flow to and from channel partners
    • Re-usable and modularized codes to help in quick enhancements, easy refreshes and reduction of additional effort
    • Self-learning enabled in final recommendations – results are updated based on recent engagements

  “We are happy with this ‘Re-configurable White Box Solution.’ Several man-hours that usually go in other projects are avoided here due to parameterization and config creations. Most of all, being able to change the rules without help from the development team at any point is very helpful with quick changes in requirements and Ad hoc analysis.”

   

• HCP-Level Site Alert Predictions

  Client Situation

  The objective was to develop an advanced machine learning algorithm to predict potential site alerts for HCPs in the target universe

  Trinity’s Role

  Besides sales rep deployment, the client used these alerts to direct their non-personal promotion like email and digital advertising.

  The project was done in 2 phases.

  • Phase 1:
    • Data coverage of claims data, EMR data and other sources for incoming site alerts to expand the existing HCP list
    • Business rule creation to identify incoming site alerts data from additional HCP lists to ensure better coverage and make the existing list more comprehensive
    • Key driver identification for prescribing behavior of HCPs
    • Regression model development to estimate the future number of alerts from HCPs
  • Phase 2 (leveraged Phase 1 output):
    • Creation of a channel and content recommendation algorithm for the updated HCP universe
    • Data sources such as Multichannel Marketing (MCM) activity, sales data and call activity were sent across various channels for effective targeting

  Project Outcomes

  This project resulted in a ~20% improvement in site alerts address rate. Deliverables included:

  • An efficient recommendation model to help plan the HCP engagement journey, featuring alerts forecasted out 4 weeks to support informed decisions, HCP targeting and tactical brand share management
    • High-value HCP identification, prioritization and collation
    • Enrichment of HCP universe and site alerts predictions to improve coverage of total alerts
  • Increased utilization of sales reps & channel communications
    • Proactive engagement with high-value HCPs was promoted by sending site alerts communications through the right channels
    • Call activity planning was realigned to help sales reps optimize their efforts

  “This innovative approach to predict site alerts has helped our teams to intervene proactively to help make treatment decisions. It puts us in an advantageous position by enabling our sales and marketing teams to reach out to HCPs more effectively with well-timed and tailored messaging.”

   

• Augmented Patient Recruitment

  Client Situation

  A large biopharma company wanted to accelerate clinical trial recruitment in the U.S. by leveraging claims to identify patient pools and building a referral network

  Trinity’s Role

  • Identified the patient cohort and physicians treating based on inclusion/exclusion criteria
  • Prioritized referring physicians based on patient volume, level of treatment activity and connection to sites/Principal Investigators (PIs)
  • Developed and activated a referral network between referring physicians and sites/PIs to increase patient recruitment
  • Assessed the potential of HCPs with a higher number of connections to serve as trial PIs
  • Implemented a Machine Learning (ML)-based scoring algorithm to predict sites with high likelihood and optimized results for high patient onboarding
  • Determined the sites that would be suitable for conducting the clinical trial

  Project Outcomes

  • Increased the total patient count from 79 to 1823 unique patients from top HCPs within 50 miles of the PIs—a 20-fold increase!
  • Generated a list of sites for conducting the clinical trial
  • Identified HCPs who could be potential trial PIs
  • Accelerated patient recruitment for the clinical trial
• Spend Optimization via Geo Clustering

  Client Situation

  The objective was to achieve maximum response from prescribers and patients for a TV campaign by identifying geographic clusters of high-potential prescribers

  Trinity’s Role

  • Trinity ran a deep-dive analysis on various data sources (sales, claims, marketing and others) and amalgamated them to create features at the zipcode level
  • After several iterations, well-defined clusters were created using unsupervised learning techniques
  • These clusters were profiled to provide insights on prescribers, access controls, patients and brand potential
  • Based on the cluster profiling, high-potential geographic targets were identified across the US

  Project Outcomes

  To maximize return on different marketing campaigns, U.S. geography was divided into three clusters and prioritized:

  • Cluster A—High potential in terms of population, branded drug sales and highest prescriber marketing activity (Priority 1)
  • Cluster B—Highest treatment rate with branded drug sales concentrated within a few zipcodes (Priority 2)
  • Cluster C—Lowest treatment rate with lowest target population per zip (do not target)

Automated, Scalable Data Foundation
Case Study

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GenAI-Powered Process Automation
Case Study

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Patient Finding Case Study

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HCP Call Planning Case Study

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Trinity AI’s GenAI-powered process automation solution to improve data operations efficiency won QC Data Management Solution of the Year in the 2024 BioTech Breakthrough Awards.

Case Study

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