2025 Annual Conference - Agenda

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Enhance your impact with greater creativity. Learn about why creativity is critical, what kills creativity in the workplace today and how you can enable creativity for you and your team.

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Translating natural language queries into SQL is essential for accessing data efficiently, yet traditional Large Language Models (LLMs) often struggle with complex real-world data due to intricate schemas and inadequate tokenization of domain-specific terms. This challenge is particularly pronounced in the healthcare sector where access to data can be restricted based on users' capability to understand database systems.

To overcome these challenges, this presentation introduces an innovative approach using Knowledge Graphs (KG) and gist tokens. Key learnings will include:

• The principal ways that KGs can be utilized in complex data environments are to enhance accuracy and reduce typical hallucinations in query generation.
• The concept of gist tokens and their role in improving performance and reducing cost while maintaining the context of the complex real-world systems.
• How integrating KGs can significantly improve the precision and comprehensiveness of SQL query generation by LLMs over non–KG approaches.
• Why this is a scalable solution for complex data environments that can broaden accessibility and usability across different systems.

This mixed-methods approach transforms data utilization to enable a wider range of users, from analysts to business leaders, to interact seamlessly with data, ultimately helping to enhance data-driven decision-making while democratizing technology.

Focus Areas:

• Generative AI

Over 50% of patients discontinue chronic therapies within the first year, significantly affecting their health outcomes. While some early therapy drop-offs may be inevitable, personalized touchpoints delivered through coordinated patient services can be crucial for a seamless patient experience, helping patients remain on therapy. ZS and Jazz have collaborated to develop an intelligent patient insight engine that utilizes diverse data sources, including proprietary pharmacy data, and employs an explainable AI model to predict weekly patient discontinuation risk. This engine also provides insights into the reasons behind therapy drop-offs, enabling personalized n=1 recommendations for interventions to be deployed to healthcare providers and patients via digital and field channels, including case managers, nurse educators, and sales representatives.

Join us to discover how an integrated patient data ecosystem, AI, and orchestration can revolutionize the patient support experience.

Focus Areas:

• Predictive Analytics
• Real-World Evidence (RWE)

In the rapidly evolving landscape of AI-driven analytics, organizations must adopt robust data strategies that not only address current challenges but also pave the way for future innovation. This session will delve into how generative AI is revolutionizing analytics-ready data (ARD) creation—accelerating, automating, and enhancing critical processes like descriptive statistics, data transformation, and modelling. By leveraging advanced AI-configured data models and cutting-edge libraries, participants will discover how to streamline data workflows to deliver actionable insights across diverse use cases.

The discussion will emphasize the importance of ensuring ARD is efficiently processed and ready for consumption by analytical systems, showcasing real-world examples of scalable, automated solutions. Attendees will gain practical insights into integrating generative AI into their analytics frameworks, enabling smarter, faster, and more impactful decision-making. Whether you're a data scientist, strategist, or technology leader, this session will equip you with the knowledge needed to harness the power of AI and drive next-generation analytics outcomes.

Focus Areas:

• Data Strategy
• Generative AI

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Join us to learn how to adopt an enterprise mindset that prioritizes customer needs and enhances team dynamics for better outcomes and insights. We will explore what design thinking is, its benefits, and how you can start implementing it in your life today whether you are a cross functional analytical team or planning your next Disney vacation!

Focus Areas:

• Storytelling

Everyone wants you to “think outside the box.” But to truly give yourself a competitive advantage and lead in the business world, you must approach problem-solving to invent an entirely new box. Welcome David Corsaro. For the past 25 years, David has been the "go-to" guy in the business world regarding creativity and problem-solving. Additionally, David is an accomplished magician and mentalist, performing in 4 different off-Broadway magic shows in NYC as well as the world-famous Magic Castle in Los Angeles and he even appeared on the hit TV show “Penn & Teller: Fool Us”....and yes, he fooled them. In David’s seminar, “Problem-Solving like a Magician”, you will not only learn how a piece of magic works but more importantly you will learn WHY it works and how you can take the principles of psychology and neuroscience and apply them to your business challenges.

This session will showcase how AI/ML can enhance Non-Personal Promotion (NPP) strategies for healthcare providers (HCPs). NPP strategies in the pharmaceutical industry have historically been calendar-based, relying on media vendors to deliver promotions on fixed schedules. While field strategies have evolved and are currently embracing advanced AI and analytics to empower sales reps to engage HCPs with personalized and near real-time insights, the NPP space has largely remained unchanged. Promotions are still sent at predetermined frequencies from pre-planned campaigns, often overwhelming HCPs and failing to deliver on expected value. There is a need to leverage AI-driven strategies in the digital omnichannel space.

We will present a comprehensive strategic framework for NPP orchestration that extends AI-driven engagement strategies into the digital environment. The proposed solution leverages machine learning models, enabling dynamic and personalized outreach to HCPs. This AI-powered framework automates decision-making by predicting the best channels and content based on historical interactions and real-time data, thus improving promotional targeting, frequency, and content relevance. Incorporating business rules further refines the AI models, ensuring agility and adaptability to evolving data signals. This approach demonstrates how advanced AI/ML techniques can drive automation, predictive accuracy, and more impactful, data-driven decisions in pharmaceutical marketing.

Focus Areas:

• Machine Learning
• Predictive Analytics
• Storytelling

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In the rapidly evolving field of healthcare analytics, deriving meaningful insights from patient data is crucial. Existing AI/ML solutions enable users to profile patients, map their journeys, and drill down into sub-cohorts, but they require significant time for data exploration and often produce too many findings, some of which may be trivial. To address these issues, Cohort Comparison Synthesis (CCS) was developed. CCS uses statistical methods to identify significant differences between patient cohorts and leverages Generative AI to provide comprehensive insights in natural language. The presentation will demonstrate CCS's capabilities in generating detailed cohort comparisons and highlight the integration of Generative AI techniques in healthcare analytics. CCS benefits pharma and biotech professionals by filtering out trivial insights and providing executive summaries of top insights, enabling data-driven decisions with greater accuracy and efficiency. The approach leverages reusable components that generalize to various use-cases, benefiting the identification of actionable healthcare insights. Key components include data sources and processing (IQVIA longitudinal Prescription Data and Office-Based Practitioners Medical Claims Data), statistical techniques (significance testing and effect size calculations), payload definition (using an extensible ontology), and Generative AI techniques (state-of-the-art prompt engineering with GPT-4 LLM).

Focus Areas:

• Generative AI

In today’s competitive pharmaceutical landscape, engaging healthcare professionals (HCPs) with precision and relevance is no longer a luxury—it’s a necessity. Traditional email outreach methods often fall short, relying on static rules that fail to adapt to the evolving behaviors of HCPs. But what if we could move beyond rigid segmentation and instead continuously learn and adapt, delivering exactly the content HCPs need, when they need it?

The talk will focus on a novel Reinforcement Learning paradigm to automate content recommendation for HCP emails. Through a combination of NLP, advanced machine learning, scenario planning, and integer optimization, the pipeline utilizes a broad array of HCP characteristics and Omni-Channel interaction history to dynamically recommend email content that is most likely to engage, while still adhering to core business rules. The pipeline also incorporates an epsilon-greedy mechanism to balance exploration and exploitation of the problem space.

The current talk will uncover through replay analysis that an RL-based recommender system for home office emails could result in a significant uplift in customer engagement. This scalable, intelligent solution can be extended across various customer touchpoints within the pharma industry.

Focus Areas:

• Machine Learning
• Predictive Analytics

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This talk explores the transformative potential and challenges of Artificial Intelligence (AI). AI can analyze large datasets and predict consumer behavior, revolutionizing personalized marketing with precise targeting. The discussion covers examples of improved advertising outcomes, in both efficiency and effectiveness, due to AI. However, issues like data privacy, algorithmic bias, and loss of human interaction pose significant challenges. The talk provides a balanced view of AI's opportunities and challenges, emphasizing the need for human intelligence to maximize AI's benefits through ethics, creativity, and empathy.

In the talk we will discuss how AI/LLMs often produce wrong results because data is not representative and misleading. We propose HI approach to fix the data first before it goes into the AI/LLM systems.

Focus Areas:

• Generative AI
• Machine Learning
• Predictive Analytics
• Data Strategy

Imagine a comprehensive data and insights repository on all things CML… a digital super Disease Area Expert!

We compiled all market research content over the last two years and used genAI to synthesize the data and build a patient journey. Once tested and validated, this formed the backbone of our digital Disease Area Expert (DAE). We can now use the digital DAE to get instant answers to a range of questions that we constantly grapple with – testing of multiple TPPs, impact of new Takeda and competitor data readouts, drivers and barriers, identifying untapped opportunity areas etc. – eventually we should also be able to run sensitivity analysis on sales and marketing tactics!

We started with market research for our pilot and over time we will layer in additional primary and secondary data such as publications, market access and pricing research, conference abstracts, competitive intelligence, etc., and eventually data from legitimate external CML websites.

The Virtual super DAE is a “live” application that will continue to learn and evolve as we layer in external publications and additional work, ensuring insights are always up to date with the latest data.

Focus Areas:

• Generative AI
• Machine Learning
• Storytelling

More information coming soon.

Pharma companies need to plan for and manage large-scale events such as national meetings, conferences, launches, etc. These types of events present unique challenges such as involvement of multiple stakeholders, complex handoffs, moving targets and figuring out critical paths. Utilizing bleeding-edge Machine Learning techniques, we have built a toolkit that solves for all of the above challenges. In doing so, it improves adherence to plans, optimizes around impending and ongoing issues, and presents options to meet initial objectives. It also has a set of unique and highly interactive visualizations and reports that facilitate effective presentation/communication by following a storytelling approach. In the presentation, we plan on sharing all of these insights, as well as talk about our experience as we've built this unique toolkit.

Focus Areas:

• Machine Learning
• Storytelling

Agentic AI is transforming healthcare analytics, offering unprecedented opportunities to enhance decision-making, optimize workflows, and ultimately reduce the burden of disease. This session will explore how agentic AI systems, powered by advanced natural language processing (NLP), is enhancing decision-making and innovation across key areas in the pharmaceutical industry.

This presentation will break down the core concept of agentic AI—its capabilities, challenges, and strategic considerations for implementation. Through a real-world case study on MapAI, Komodo Health’s generative AI analytics assistant, discover how agentic AI can be seamlessly integrated with business logic, existing services, and industry-specific datasets—such as structured claims from one of the largest U.S. healthcare databases—to drive intelligent, precise, and scalable analytics solutions.

Attendees will gain actionable insights into the transformative power of agentic workflows, including best practices for ensuring AI accuracy, mitigating risks, and fostering collaboration between AI agents and human experts. We will also provide a forward-looking perspective on how agentic AI is shaping the next generation of healthcare analytics.

Join us to discover how agentic AI is reshaping healthcare analytics, accelerating innovation, and setting the foundation for a more intelligent, data-driven healthcare ecosystem.

Focus Areas:

• Generative AI
• Real-World Evidence (RWE)

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The landscape of cardiometabolic disease management is shifting with the introduction of GLP-1 receptor agonists, SGLT2 inhibitors, and innovative weight-loss medications. However, the interconnectedness of conditions such as hypertension, diabetes, and obesity requires a holistic patient management approach. As healthcare research pivots toward personalized (n=1) strategies, traditional disease-focused methods may overlook the broader relationships between these conditions.

The primary objective of our initiative was to develop a data-driven framework using Claims and Social Determinants of Health (SDoH) data to identify and quantify patient journey archetypes among approximately 100 million patients with diabetes, obesity, and ASCVD, which reflects variations in clinical characteristics, treatment adherence, and SDoH factors.

This initiative has fostered strategic alignment among stakeholders, revealing intervention opportunities to enhance patient support, address care gaps, and improve health equity. By aligning patient and healthcare professional archetypes, we created a Market map for personalized marketing strategies, demonstrating Novo Nordisk's commitment to advancing patient-centered care in the cardiometabolic field.

Focus Areas:

• Machine Learning
• Storytelling

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GenAI agents are good at performing specific tasks, however, are limited when it comes to understanding organizational context and user needs. To overcome this challenge, we have developed a "brain" agent that will infer user needs, apply the right organizational context, and provide the right user experience through a GenAI agentic ecosystem. This has helped in the development and deployment of very persona-specific solutions like Sales Rep and Marketing AI assistants.

This presentation introduces a step-by-step framework for extracting meaningful, unbiased insights from large-scale, healthcare claims data integrated with consumer demographics and machine learning-modeled propensity data. Attendees will discover how to leverage these insights to inform brand media strategies and tactical decisions—including messaging, creative development, and channel mix—even in the absence of historical campaign data.

Brands seeking to engage this or any other population can build a foundation of campaign analytics to better understand consumer behavior, media preferences, and barriers to care. Attendees will leave with actionable insights and tools to navigate the unique challenges of healthcare advertising for their brand or therapeutic area, driving impactful engagement and better outcomes for patients.

Focus Areas:

• Predictive Analytics
• Storytelling

Deloitte has partnered with clients and key alliance partners to develop a vision for the future of Agentic AI in Commercial Life Sciences. By re-imagining the flow of work across sales, marketing, market access, patient services, medical, and decision support, we have identified the critical roles non-human agents play in supporting commercial objectives. Our investment in AI-driven agents provides data-driven intelligence for omnichannel marketing and sales, enabling continual sense and respond capabilities in dynamic therapeutic areas.

Attendees will learn how these AI agents independently perform functions such as data extraction, monitoring and alerting, decision making, content generation, channel execution, and dialogue and explanation. Discover how the interaction between these agents, through data sharing and query exchanges, enables efficient commercial analytics goals, including engagement enhancement, closed-loop measurement, and mid-campaign promotion optimization. This session will showcase how our innovative approach can transform commercial operations in life sciences, driving efficiency and effectiveness in achieving commercial objectives.

Focus Areas:

• Generative AI

• Leverage AI/ML capabilities to identify harder-to-diagnose patient sub-sets within current indications.
• Identify new patient populations and sub-populations that are difficult to map and identify for upcoming indications and brands.
• Analyze patterns in tests, co-morbidities, and misdiagnoses to develop models that surface these patients.
• Model patients at higher risk of severe events to enable timely intervention.
• Predict patient presentation to allow for intervention, education, and action.

Focus Areas:

• Machine Learning
• Predictive Analytics

GenAI Driving Business Value For MSLs In VoKOL & Strategic Portfolio Management: Leading pharmaceutical companies increasingly rely on Key Opinion Leader (KOL) feedback to shape strategies for engaging healthcare professionals (HCPs). There is a growing need for a robust solution to analyze sentiments from Voice of KOL (VoKOL) data, enabling organizations to track key performance indicators (KPIs) and derive actionable insights through an automated reporting process. In this context, Generative AI (GenAI) plays a pivotal role. Its advanced capabilities to process, understand, and interpret unstructured data—such as feedback from KOLs—empower pharmaceutical companies to uncover hidden trends, measure sentiment accurately, and inform strategic decisions with speed and precision. Also, these insights are helping companies in spearheading research and development on specific molecules/drugs etc.

Focus Areas:

• Generative AI
• Predictive Analytics

The PLD data sources we use contain an alarmingly high rate of patient duplicates. In other words, 2 or more patient ids refer to the same patient. We conducted measurements on several PLD data sources for different therapeutic areas. The result is the same: The duplication rate is much higher than what the industry says it is. The extent of patient duplication is not a one-off anomaly but a systematic feature of PLD data sources.

Focus Areas:

• Data Strategy

The rapid advancement of data analytics has transformed how industries engage with target audiences, including the healthcare sector. Physicians and healthcare providers are increasingly the focus of personalized outreach by pharmaceutical companies, medical device manufacturers, and health-tech organizations. By leveraging data such as demographics, prescribing patterns, and behavioral insights, these entities can deliver more tailored communications and recommendations. This study explores an innovative approach to segmentation and targeting using unsupervised learning and generative AI (GenAI). Central to this effort is ensuring the right treatment reaches the right patient through the right provider, a critical factor in improving outcomes and building trust in healthcare.

Focus Areas:

• Generative AI
• Machine Learning

The successful organizational adoption of artificial intelligence (AI) in the pharmaceutical sector hinges on overcoming key challenges: siloed operations, lack of synergies, and absence of a clear roadmap. Departments often operate independently, leading to redundant efforts and missed opportunities for broader organizational benefits. A unified strategy that fosters cross-departmental collaboration is essential to unlocking AI's full potential.

A defined roadmap, aligned with business goals, provides structure for AI implementation. A proposed three-step framework emphasizes: (1) Building a strong foundation—ensuring robust data governance, data quality, and adopting mature technological frameworks for scalability; (2) Prioritizing impactful use cases—focusing on high-impact, low-complexity initiatives to build momentum and ensure resource efficiency; and (3) Enterprise-wide adoption—managing expectations through leadership support, change management, and structured planning.

By adopting this comprehensive approach, pharma companies can align their AI investments with broader goals, enabling scalable, impactful, and sustainable innovation.

Focus Areas:

• Data Strategy

Application of GenAI capabilities in supporting coaching activities for the field force.

Focus Areas:

• Generative AI

More information coming soon!