2026 Annual Conference - Agenda

What if every insight you needed could be surfaced instantly, governed, and trusted —freeing you to focus on the work that truly moves the needle?

In this two-part workshop, Axtria will demonstrate what becomes possible when AI becomes your most powerful ally, transforming you from a report generator into a strategic intelligence leader.

In Part One, Axtria's AI and Data Team, joined by a customer speaker from Alcon, will share how AskADI, powered by Axtria InsightsMAx.ai, transformed customer experience and clinical performance across its online store and insight center. By deploying intelligent agents capable of resolving FAQs, tracking orders, and surfacing KPI-driven GenAI insights through natural language, Alcon achieved 80% faster query resolution and 24/7 autonomous service delivery, giving you the space to do more of the work that matters.

In Part Two, you'll hear a compelling real-world success story before going hands-on with Axtria Headquarters Intelligence (HIQ). HIQ doesn't just answer questions — it reasons through them, validates every response with built-in confidence scoring, and connects directly to your existing data warehouse. No ETLs. No guesswork. Just you, equipped with a tireless intelligent copilot that handles the data legwork while you drive the strategy.

Together, these sessions chart a bold new path: from reactive reporting to proactive, governed, autonomous commercial intelligence, with you at the helm. This is what the future of analytics looks like. Come experience it firsthand.

AI agents have graduated beyond POCs to reshape how Pharma commercial teams plan, act, and adapt. But isolated agents only go so far. The real breakthrough happens when agents collaborate: a targeting agent feeds an omnichannel orchestration agent, which informs a field coaching agent, all aligned to a shared commercial objective.

In this hands-on workshop, participants will work through a realistic end-to-end commercial scenario from opportunity identification through pull-through execution, mapping where agentic collaboration unlocks the most value and where it breaks down. Participants will leave with a practical framework for designing multi-agent workflows, identifying the right human-in-the-loop checkpoints, and building the organizational buy-in needed to deploy at scale.

Session is designed for Business leaders, Analytics leaders, commercial insights teams, and AI practitioners.

Despite the promise of AI, commercial teams experience responses that feel like a used car salesman – confident, persuasive, and subtly wrong. How do you generate answers you can trust to drive key business decisions? This hands on workshop explores how contextual data built for life sciences and multi-agent orchestration can work together to reduce hallucinations, improve precision, and be transparent on uncertainty. Through commercial use cases such as understanding brand performance, stress-testing scenarios, and customer insights, we’ll explore how to get trustworthy answers, make the limits of AI transparent, and keep humans in control.

Eden Wells, Chief Insights and Decision Science Officer, Novartis U.S., is the keynote speaker at this year’s PMSA annual conference. Eden is widely recognized for her visionary leadership in advancing AI and technology and systemizing data-driven decision-making within the pharmaceutical industry. Eden’s keynote will address topics such as leveraging Talent, Technology, and Process Systemization to elevate decision-making across the enterprise, breaking down silos and unlock innovation-powered business excellence.

As pharma organizations scale patient-level AI across drug development, the need for governed, interoperable data platforms is critical. From clinical trials to real-world data, enabling AI requires integrating diverse data sources while ensuring compliance, traceability, and regulatory alignment.

This session outlines how to design modern data platforms that balance innovation with control. We will address challenges such as fragmented data, evolving protocols, and multi-modal integration (clinical, genomic, imaging, and RWD), while enabling scalable AI.

At the core is the ANCHOR framework:

• A – Accessible & Interoperable: Unified, standards-driven data access (e.g., CDISC, FHIR)
• N – Normalized & Contextualized: Analysis-ready, patient-linked datasets
• C – Compliant & Controlled: Built-in governance, lineage, and regulatory adherence
• H – Harmonized Integration: Cohesive multi-modal data ecosystem
• O – Operationalized AI: Scalable, reproducible AI/ML deployment
• R – Responsive Architecture: Modular, adaptable to protocol and data changes

Attendees will gain a practical blueprint to build AI-ready platforms where governance is embedded by design—enabling faster, compliant, and patient-centric drug development.

More information coming soon

Medical Affairs teams generate a wealth of insights from unstructured data but turning these into timely, consistent, and actionable intelligence remains a challenge. Manual processes are slow, resource-intensive, and often limit the ability to act when it matters most.

This session explores a pragmatic shift: an SME–AI collaboration model that blends AI-driven synthesis with expert validation. By applying LLM-based workflows to unify and analyze diverse insight sources, teams can rapidly identify emerging themes and signals while maintaining scientific rigor through expert-in-the-loop review.

Rather than replacing human expertise, this approach redefines it, freeing SMEs from manual summarization and enabling them to focus on interpretation, context, and decision-making. Early adoption shows meaningful improvements in speed, consistency, and transparency of insight generation. Know more about how to operationalize Human–AI collaboration to unlock faster, more reliable medical insights.

Pharmaceutical sales representatives generate thousands of field execution signals daily - yet most incentive compensation (IC) plans continue to reward lagging sales outcomes rather than the leading behaviors that drive them. High call volumes don't guarantee strong performance, and traditional IC metrics rarely capture execution quality.

This session introduces a conceptual framework that uses AI to bridge that gap. By identifying which rep activities, sequencing patterns, and engagement approaches correlate with downstream commercial outcomes, the framework embeds those insights directly into IC plan design.

Three integrated layers form the foundation: a behavior-performance attribution model that distinguishes high-impact activities from low-value signals; an AI-generated impact scoring methodology that quantifies the commercial contribution of specific behaviors; and a governance structure that ensures transparency, explainability, and auditability - critical for sales leadership trust and compliance.

The result is a structured approach to shifting compensation toward the behaviors that demonstrably matter: the right customer, at the right time, with the right message. Better alignment between controllable behaviors and incentive design means improved rep focus, reduced wasted activity, and more consistent commercial execution.

As pharma organizations push for sharper, faster, and more localized decisions, forecasting can no longer remain a top-down planning exercise. This session will explore how advanced machine learning and deep learning are reshaping subnational forecasting—moving it from static target-setting to a dynamic decision intelligence capability. We will discuss how next-generation forecasting approaches can detect local demand shifts earlier, identify which geographies, accounts, or HCPs should be prioritized, flag where sales are outperforming or falling short of expectations, and determine what execution changes are required to deliver on plan.

Beyond forecast accuracy, the session will focus on the broader value of cutting-edge ML/DL enabled forecasting in improving agility, reducing planning bias, and creating actionable alignment across field, analytics, and marketing teams. It will also examine how these approaches can better handle sparse and cold-start markets while preserving consistency with brand-level strategy.

Finally, the discussion will look ahead to the next frontier: moving from brand-by-brand model building toward reusable, adaptive forecasting systems that can learn from prior implementations and rapidly generalize to new brands, refresh cycles, and business contexts with far less retraining effort.

Territory scorecards are ubiquitous in pharma field analytics. Every commercial team ranks territories by goal attainment, colors them red/yellow/green, and distributes them weekly. The problem: ranking tells leaders WHO is underperforming but not WHY - and without a diagnosis, the coaching conversation defaults to "work harder." For a field organization managing 320+ territories across multiple indications, this gap between measurement and action was consuming analyst capacity without changing field behavior.

This session presents the performance diagnosis framework Bayer's Commercial Analytics team developed to move from territory ranking to territory classification. The core insight came from analyzing the relationship between effort metrics and outcome metrics across 320 CR1 territories: 25% of underperforming territories had above-median call activity. They weren't underperforming because of insufficient effort - yet the scorecards couldn't distinguish them from territories that were.

The framework classifies underperforming territories into five diagnostic categories based on metric patterns, not subjective judgment:

• Effort gap. Call activity and reach metrics are below peer benchmarks. The territory needs more activity. Roughly 30% of underperforming territories.
• Targeting gap. Call volume is adequate but directed at low-priority HCPs. My Plan targets are uncovered while non-targets receive repeated visits. Approximately 25% of underperforming territories and the most common misdiagnosis, since these territories look "busy" on standard scorecards.
• Conversion gap. Effort and targeting are both adequate, but calls aren't generating prescriptions. Closing rates are below peer benchmarks despite comparable reach. Roughly 20% of underperforming territories.
• Writer retention gap. New writers are being acquired but lapsed writers are leaving faster. Net writer health is negative despite positive acquisition numbers. Approximately 15% of underperforming territories.
• Access gap. Claim rejection rates significantly exceed regional benchmarks, creating a headwind no amount of field activity can overcome. Roughly 10% of underperforming territories.

Each classification triggers a different coaching action. An effort gap requires activity management. A targeting gap requires call plan review. A conversion gap requires message or skill development. Collapsing all five into "you're behind on NBRx" wastes the coaching conversation.

The framework operationalizes through automated weekly delivery to 48 area managers - each receiving a personalized territory scorecard with classifications and specific recommended actions, replacing a manual spreadsheet process that previously covered only a handful of territories per week.

This session explores how enterprises can move from fragmented AI experiments to scalable, production-grade AI through an “AI Factory” approach. Using a real-world case study, we demonstrate how a structured, reusable ecosystem of components - spanning knowledge layers, agent orchestration, and AgentOps, enables rapid development of agentic AI solutions from idea to MVP in weeks.

Attendees will learn how unstructured and structured data can be transformed into AI-ready knowledge using ontologies, knowledge graphs, and vector databases, forming the backbone for intelligent agents. The session also highlights how reusable assets, standardized workflows, and governance frameworks drive speed, consistency, and cost efficiency while ensuring trust, safety, and compliance.

Through practical examples across market research, medical, and commercial use cases, we will showcase how organizations can industrialize AI, improve productivity, and accelerate business decision-making.

The session is designed for leaders and practitioners looking to operationalize GenAI and agentic systems at scale, turning AI from isolated pilots into a sustainable enterprise capability.

Launch windows are short. Analytical backlogs are not.This session explores how a conversational AI layer built on structured healthcare claims data is changing the pace of launch analytics. Traditional approaches — custom SQL, manual cohort construction, static dashboards — create 4-6 week turnaround cycles at the exact moment speed matters most. We'll walk through how generative AI translates plain-language business questions into validated analytical workflows: automated cohort construction, treatment line classification using longitudinal lookback, multi-dimensional prescriber and patient stratification, and iterative hypothesis refinement — all with built-in statistical validation against published prevalence benchmarks.

Attendees will leave with a clear picture of where AI fits in the launch analytics stack, what it takes to implement responsibly with healthcare data, and how teams are using it to answer the questions that matter — while the launch is still happening.

This moderated leadership roundtable explores how Data, Talent, and AI truly converge to deliver measurable commercial impact in pharma. We will share practical lessons learned from three real world applications: an AI-powered Bot for vaccine market insights, a Return on Data Dashboard that quantifies the true business value of third-party data assets, and a three-tier Intelligent Care-Gap Signal Service that surfaces actionable opportunities from unstructured data.

We’ll share the common solution frameworks and methodologies that connect these use cases, then dive into our AI-driven data engineering approach that dramatically accelerates data completeness and velocity while reducing cost and improving quality.

The session is highly interactive, with open Q&A and discussion prompts designed to let attendees’ helicopter between strategic business outcomes and technical execution details based on their specific interests.

Why did Brand X lose 3 points of share in the Southeast, and how much of it is a formulary access problem versus a field execution problem?" That question today gets split across two teams, four data sources, and a three-week turnaround. AI can now answer it before the plates are cleared — delivering a finished board deck, a territory coaching summary, or a payer escalation brief, not a chart your team has to rebuild. And while you're eating, it keeps monitoring your business for the next shift. We'll prove it with a live demo over lunch: a real multi-source commercial question, answered end-to-end, with finished work products as the output. Then we'll open the table for discussion: what would change about how your team operates if questions like that took minutes instead of weeks?

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 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 spark creativity in you and your team members.

Enterprise-wide AI adoption requires more than technical feasibility. It requires understanding people, their attitudes and behaviors, and how they work. Analytics teams are building increasingly sophisticated solutions, but adoption remains the bottleneck. When stakeholders across sales, marketing, finance, and operations respond to the same AI initiative with vastly different levels of enthusiasm, skepticism, or indifference, a one-size-fits-all rollout strategy won't cut it. Understanding these differences in needs, expectations, mindsets, concerns, and readiness across functions, roles, and fluency levels creates opportunities for targeted communication and change management strategies.

Through mixed-methods research including in-depth qualitative interviews and structured surveys with commercial leaders across multiple biopharma organizations, we developed a behavioral segmentation framework that categorizes stakeholders by their orientation toward AI, capturing dimensions like enthusiasm, data fluency, risk tolerance, and decision-making criteria. We've identified five distinct segments, each with unique barriers and enablers that inform how to tailor engagement strategies for different audiences within the commercial organization.

In this interactive session, we'll walk through the framework, share what's worked and what hasn't in practice, and provide analytics teams with a practical playbook for building organizational buy-in. Live polling will let you benchmark your organization's adoption landscape against peers in the room.

This session shares a practical blueprint for building Enterprise AI in pharma and scaling beyond pilots. We introduce a three-layer model: (1) AI for intelligent engagement (patient/HCP and field-facing assistants), (2) AI for operational excellence (automating analytics workflows for productivity), and (3) a scalable foundation—the platform layer that enables reuse and trust through context engineering, orchestration, integration, and governance.

We cover different “where to start” paths—engagement-first, productivity-first, or foundation-first—based on an organization’s maturity and constraints, and discuss the trade-offs using common pharma scenarios. Attendees leave with clear sequencing guidance and a roadmap from MVP to scale.

GEO represents a critical new discipline for pharma, merging scientific rigor, metadata strategy, content engineering, and AI governance. Companies that adopt GEO early will shape how their science is understood in a generativeAI-first world, enhance AI-driven medical insights, and build competitive advantage across commercial and clinical domain. The session will focus on real world applications of this in the healthcare ecosystems and key ways in which brands can leverage data and insights to boost their presence within the constantly evolving ecosystem.

This session explores the transition from "Flashy Experimentation" to "Disciplined Engineering" through the implementation of Agentic AI within the theme of Advanced Machine Learning & Deep Learning. Drawing on parallels from large-scale deployments such as agentic voice ordering systems that must handle high-concurrency, real-time reasoning and the safety guardrails developed for frontier models like Gemini, we will discuss how to architect a "Cognitive Engine" for pharma.

AI in pharma is at a turning point, where pilots are no longer enough and real impact is the expectation. This panel dives into what works when scaling AI, from proving value fast to building talent that bridges science, data, and business. Panelists will share how leading organizations are creating a culture where AI empowers people and drives smarter decisions. The conversation will also tackle where true competitive advantage lies and how to strike the right balance between AI autonomy and human judgment.

Abhishek N. Singh will share how leading pharma organizations are rethinking analytics and decision‑making at the convergence of data, talent and AI. Drawing on Merck’s journey, he will explore how strong analytical talent, modern data foundations and applied AI come together to drive meaningful business impact—while keeping human judgment, accountability and outcomes at the center.

More information coming soon

Healthcare media planning still treats HCP and consumer audiences as separate worlds—despite the reality that clinical decisions and patient behavior are deeply interconnected. This session introduces a data science framework that bridges that gap by identifying and activating overlapping HCP–consumer audiences using privacy-safe modeling and human insight.

Instead of relying on fragile individual-level linkage, we focus on cohort-level alignment—where provider intent and patient behavior converge across therapeutic areas, geography, and time. By combining AI and deterministic modeling, we enable planners to prioritize high-impact overlaps and move from channel optimization to true cross-audience orchestration. We will share a framework which is dynamic and privacy-compliant, re-framing audience overlap from a reporting metric into a strategic planning lever.

We will also discuss how data science and clinical expertise jointly shape model design, ensuring outputs are not just accurate—but meaningful and actionable. Real-world results will also be shared, demonstrating how campaigns targeting high-overlap cohorts outperform traditional segmented approaches, driving stronger engagement and better outcomes.

Attendees will leave with a practical roadmap for implementation with a new lens on how to build adaptive, healthcare media systems that reflect how decisions actually happen.

This session presents a sophisticated Agentic AI architecture designed to unify structured databases and unstructured document repositories within a single, enterprise-ready intelligence framework. It demonstrates how a modular dual-agent design enables organizations to process natural-language questions through parallel pathways, delivering both real-time quantitative insights and rich qualitative or clinical context.

At the core of the approach is a semantic layer, supported by a scalable knowledge graph, which serves as the orchestration engine for interpreting queries, routing them to the appropriate data source, and generating accurate, relevant responses. The session also emphasizes the importance of enterprise-grade governance, including traceability, source attribution, security, and controlled access, to ensure that AI-generated insights remain trustworthy, verifiable, and aligned with organizational standards.

By combining semantic intelligence, modular AI agents, and governed data access, this presentation illustrates how human expertise and artificial intelligence can work together to accelerate decision-making, reduce manual effort, improve insight quality, and lower operational complexity. Attendees will gain a practical understanding of how this architecture can modernize enterprise knowledge discovery and decision support at scale.

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As the pharmaceutical landscape becomes more complex, organizations need faster and more reliable ways to understand GTN risk. This session will highlight how AI/ML enabled forecasting can surface early signals of emerging risks and revenue shifts, helping teams respond more proactively to market changes. By combining advanced analytics with business understanding, the approach strengthens rebate strategy and supports more informed decision making across finance, commercial, and market access functions.

How can life science teams turn thousands of congress outputs into timely, actionable insight? This presentation introduces an AI-enabled, human-guided framework designed to accelerate scientific intelligence while preserving analytical rigor. At the 2025 American College of Cardiology (ACC) Annual Scientific Session, more than 4,000 abstracts and a surge of professional social media created an overwhelming volume of information. In this session, we’ll showcase how this framework cuts through the noise to rapidly identify what matters most.

You’ll learn how combining automated text analysis, large language models, and expert oversight from Medical Affairs, Commercial, and data science teams enables faster, more confident decision making. Rather than replacing expertise, AI enhances it by accelerating pattern detection and insight generation at scale.

The approach delivered insights five times faster than traditional methods, with around 90% confidence in thematic and sentiment analysis. We’ll highlight key trends from ACC, including rising focus on lipoprotein(a), expanding cardiovascular relevance of GLP-1 therapies, and increased attention on ATTR-CM and pulmonary hypertension innovation. Join this session to discover a practical, scalable approach to modernizing congress intelligence while keeping experts firmly in control.

Join us for an inspiring Analytics Luncheon, designed by members of the PMSA Women in Analytics program team, where leaders of all genders explore how data, talent, and AI intersect to shape the future of business. This year’s theme, “Leading with Impact in the Age of AI: Visibility, Voice, and Influence,” celebrates the contributions of female leaders while engaging everyone in the conversation.

The session begins with a brief presentation by Tamara Burzinski (Novartis) to set the stage, followed by facilitated table discussions, where participants will:

• Hear success stories from leaders elevating women colleagues
• Explore strategies to cultivate influence and secure a seat at the table
• Learn how leaders are staying ahead in technology and AI
• Discover opportunities for career growth and talent development

This luncheon celebrates leadership in analytics and encourages all attendees to amplify their visibility, voice, and impact in the age of AI.

This panel will discuss an evolution from analytics to Agentic and Gen AI, areas where analytics might be enhanced or even replaced by Agentic/Gen AI, and areas where AI is still has limitations.

More information coming soon

Commercialization in COPD is becoming increasingly complex, with fragmented patient journeys, evolving treatment pathways, and shifting access dynamics. Traditional approaches often struggle to fully capture this variability. This session explores the use of Digital Twin technology to model the COPD ecosystem by creating dynamic representations of patients, physicians, payers, and market interactions. By integrating real-world data with simulation techniques, teams can examine how different factors such as access changes, treatment patterns, or competitive activity may influence outcomes.

We will discuss how this approach supports more informed planning across areas like targeting, launch sequencing, and market access, while enabling a more forward-looking view compared to static analytics. Join us to know more about how Digital Twins can support more adaptive and data-driven decision-making in COPD.

In this session we will discuss the power of integrated healthcare behavioral data with consumer elements aligned to HCPs to provide an expanded insight into how consumer characteristics (of the HCP) predict and drive prescribing behavior. This session will examine how these techniques can be applied to better understand behavior and better align targeting and segmentation efforts.

Synthetic data has an image problem. It is often dismissed as “fake data” because it fails to capture the nuanced realities of medicine. For synthetic data to be viable, it must be clinically sound. This means capturing complex dynamics, such as the fourfold prevalence of migraines in females compared with males, or the increased odds linking breast cancer and ovarian cancer when a patient is ER‑negative as opposed to ER‑positive. Without this clinical integrity, the “garbage in, garbage out” principle renders the data useless for any medical or commercial inquiry.

One more thing matters: novelty. Synthetic data must bring something new; it cannot merely be a de‑identified echo of existing real‑world data with minor perturbations. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are powerful for generating individual records but struggle when it comes to generating longitudinal time series. It is no secret that TimeGANs and sequential VAEs fall short. There is good news, though. Variations on operator‑based approaches, as exemplified by Genetic Algorithms, prove to be well suited to this task.

In this talk, we will motivate our foray into synthetic data and discuss our findings and lessons learned. In particular, a purely rule‑based approach is a nonstarter, while the availability of real‑world data for the drug of interest—or for a suitable surrogate—is extremely helpful. We will also describe use cases that lend themselves to synthetic data. Like anything else, synthetic data is not a panacea—it cannot do alerts. So, do not throw the baby out with the bathwater.

More information coming soon

Pharma commercial teams rely on BI dashboards for brand performance, targeting, and field execution decisions, yet extracting actionable insights is often slow and error‑prone. Users must navigate multiple views, interpret complex visuals, and stitch answers across pages, increasing cognitive load and delaying decisions—especially when questions extend beyond pre‑built dashboards. These inefficiencies can lead to missed opportunities, slower responses to market changes, and inconsistent interpretation across teams.

This session introduces a GenAI‑powered conversational BI platform that transforms static dashboards into interactive insight engines. Users can ask questions in natural language and receive immediate, governed answers with KPIs, trend narratives, visual summaries, and transparent assumptions. Unlike typical GenAI approaches, governance and auditability are embedded by design, ensuring every insight is traceable to executed queries and aligned with approved business logic. This delivers both speed and trust—critical requirements for pharma analytics.

A key innovation is semantic onboarding: an AI‑assisted workflow that builds a shared business vocabulary across dashboards and data sources, validated by analysts for accuracy and compliance. This enables faster dashboard onboarding and consistent interpretation of user intent. An admin panel monitors usage, reliability, and feedback for continuous improvement.

In evaluation, the assistant achieved over 90% accuracy and reduced query resolution time by ~30%, enabling faster scenario exploration and more time spent acting on insights. Attendees will learn how to operationalize governed conversational BI to accelerate insight‑to‑action cycles across commercial teams.

Despite significant investment in AI, many pharmaceutical organizations have yet to realize its full potential in forecasting — missing opportunities for dramatic reductions in effort and cost. The barrier is often a perceived tradeoff: that adopting AI means sacrificing the granular, epidemiology-based insights that commercial teams depend on. This session challenges that assumption, showing how agentic AI and workflow automation can be embedded within proven epi-based methodologies to deliver forecasts that are faster, more reliable, and analytically richer.

Production-grade, AI-enabled forecasting solutions are transforming how organizations approach model development, scenario planning, insight generation, and stakeholder alignment — combining intuitive self-service workflows with advanced analytics into a seamless end-to-end process. The session will highlight how AI can surface and quantify the impact of external market signals, generate actionable recommendations, and maintain the transparency and explainability that established forecasting frameworks support.

Critically, we will also examine the organizational and behavioral shifts necessary for successful adoption, including change enablement strategies and the evolution of the forecaster's role in an AI-augmented environment.

Forecasting leads, commercial analytics professionals, and their business partners will gain practical insights into how pharmaceutical organizations can:

• Modernize forecasting to significantly reduce effort while improving accuracy and business impact
• Elevate the role of forecasting in strategic decision-making

Transformer-based AI is redefining how pharmaceutical teams identify and engage patients earlier in their treatment journey. While much of the industry is focused on traditional machine learning or LLM-based approaches, this session highlights a different path: purpose-built transformer models trained directly on longitudinal real-world data to predict near-term disease progression and drive actionable commercial strategy.

Using a real-world case study in uveal melanoma, we demonstrate how Prospection partnered with Immunocore to predict which patients were likely to metastasize in the next 3–6 months—enabling earlier, more precise engagement with treating physicians. Unlike traditional models that rely on predefined data features, these transformer architectures learn directly from sequences of clinical events, automatically capturing timing, context, and complex patterns across patient journeys—delivering stronger predictive performance.

The result is stronger alignment between commercial activity and patient need—demonstrating greater physician engagement, improved access to the right patients at the right time, and ultimately the potential for increased market share.

Beyond the case study, we explore how these models can be rapidly retrained across therapy areas and data sources, unlocking scalable, high-impact predictive capabilities for commercial teams.

In the sesssion we will learn how Pharma is using AI to streamline patient starts. In case example we will talk about how a rare disease product leveraged AI to identify patients needing more support to get started on therapy

Multi-Touch Attribution is increasingly seen as a critical capability for understanding omnichannel effectiveness in complex healthcare ecosystems. However, many organizations struggle to move beyond pilot-stage measurement into scalable, real-world application. This session will present a practical, end-to-end approach to designing, implementing, and operationalizing MTA using integrated data sources such as claims, digital engagement, and field interactions. Attendees will gain insights into key modeling considerations, including handling data limitations, selecting appropriate techniques, and aligning MTA outputs with broader measurement frameworks.

Beyond modeling, the session will focus on how to embed MTA into commercial workflows while enabling brand, omnichannel, and field teams to translate insights into actionable decisions. It will also highlight the importance of governance, cross-functional collaboration, and continuous feedback loops to drive sustained adoption. By combining AI-driven analytics with human expertise, this session provides a practical playbook for moving from attribution insights to measurable business impact.

More information coming soon

Weighted Statistical Methods for Novel Feature Selection in Claims-Based Predictive Models: Feature selection is the cornerstone of effective predictive modeling in Real-World Evidence studies, yet conventional approaches—mutual information and p-values—struggle at the scale and sparsity of administrative claims data. Mutual information surfaces rare, high-signal features that lack prevalence in the target population, while p-values lose discriminatory power in the massive sample sizes these datasets demand.

This presentation introduces a prevalence-aware weighting framework comprising four composite metrics—two uni-directional and two bi-directional—that embed class prevalence ratios directly into statistical evaluation. Uni-directional metrics isolate features uniquely predictive of the target class; bi-directional metrics identify features that maximally separate both classes. Together, they deliver a more statistically robust feature set than either MI or p-values alone.

Attendees will see the framework validated through a real-world case study identifying undiagnosed MASH patients, where the model achieved 89% recall and surfaced non-obvious clinical predictors—neurological comorbidities, cardiac disease, antidepressant use—that would traditionally require extensive Key Opinion Leader review.

Key Takeaways:

• Why standard MI and p-values underperform at claims-data scale
• How prevalence-weighted metrics correct these shortcomings
• Practical formulas ready for immediate implementation
• Demonstrated reduction in manual KOL dependency
• A validated, reproducible approach to accelerating feature selection in binary classification for RWE studies

This session presents an Incentive Compensation Digital Twin built as a governed analytical replica of plan rules, performance drivers, and key constraints. It is designed for self-serve use by Home Office teams to compare plan options and incentive add-ons before deployment, then calibrate plan structure against benchmark archetypes. Agentic artificial intelligence powers the framework end-to-end: it retrieves approved plan logic and industry benchmark references, runs standardized scenario tests, and drafts a concise narrative that highlights trade-offs and assumptions in plain language. The goal is consistency in how plan decisions are evaluated and explained across stakeholders, including sales operations, finance, and commercial leadership, so decisions are not influenced by individual analyst style or institutional memory.

For pharmaceutical organizations, the practical value lies in speed and consistency across design cycles and launch windows, along with reduced manual effort spent on scenario comparison. Using benchmarks from comparable industry peers, teams typically target 30% faster plan design and approval cycles. They also target a 40% reduction in manual analyst effort. By surfacing fairness, stability, and budget risks earlier, teams can reduce post-rollout rework cycles and disputes by 25%. This agentic AI–powered twin accelerates the entire insight-to-decision value chain, enabling more motivating and impactful incentive strategies, strengthening field trust, and improving responsiveness across go-to-market execution, including stronger support for launch success.

In healthcare analytics, a key limitation is not model performance but incomplete data. Relying on incomplete claims is like navigating a city with a map missing critical streets. Fragmentation between adjudicated closed claims and near real-time open claims creates blind spots in longitudinal patient journeys. These gaps can obscure important clinical events and introduce bias into downstream analytics, particularly in oncology.

This session introduces neural linkage, a practical approach for reconstructing the invisible patient journey when key clinical events are missing. Neural linkage learns patterns from high‑fidelity closed claims and applies those patterns to fragmented open claims. Patient journeys are modeled as ordered sequences of clinical events, allowing sequence-based models to infer missing events based on surrounding clinical context and cohort-level patterns.

The presentation will describe how masked sequence models are trained to simulate real-world data fragmentation and how inferred events are evaluated against held-out closed claims data. A working prototype will demonstrate reconstructed patient timelines along with confidence signals associated with each inferred event. Attendees will gain a clear understanding of how neural linkage methods can be applied to improve the completeness, interpretability, and reliability of patient-level analytics when claims data is incomplete.

Closing Novo Nordisk's Monday Morning Gap: How We Reduced Field Analytics Latency from 10 to 2 Days.

Forecasting teams today support an expanding breadth of assets—often with multiple indications—and increasingly rapid planning cycles. Yet forecast model design remains largely manual, inconsistent, and time intensive. Reliance on legacy templates and individual judgment leads to long build times, variability across assets, and limited scalability, creating a material bottleneck as organizations move from brand level forecasts toward portfolio and enterprise wide decision support.

This session introduces a smartly automated and AI driven forecasting model builder that automates the creation of fit for purpose models tailored to a product’s scientific profile, therapeutic context, market dynamics, and lifecycle stage—while preserving the familiarity and flexibility of spreadsheet. The approach replaces one size fits all templates with intelligent, context aware model architecture and enables secure reuse of common patient and market structures across similar indications.

By combining automated model generation, pre filled assumptions from curated sources, and human in the loop expert refinement, the solution transforms model design from a manual craft into a scalable, consistent capability. In practice, build times shrink from weeks to days, governance is strengthened, and teams can focus on what matters most: assumptions, scenarios, and the insights that drive strategic decisions.

This panel brings together medical leaders to explore the role of Medical Affairs within a modern engagement ecosystem. The discussion examines how omnichannel engagement is being used to close clinical care gaps, accelerate evidence dissemination, and enable high‑value scientific exchange, while shaping the pre‑launch scientific landscape across field medical and digital channels. Panelists will highlight where misalignment between medical and commercial engagement surfaces for healthcare professionals, and what it takes to design experiences that work seamlessly across both contexts. The session also traces the evolution of medical engagement and personalization, from early models to today’s state of practice, and looks ahead to how emerging agentic capabilities are poised to redefine the future of Medical Affairs.