The fireside chat, "Applications and Impact of Generative AI in Global Pharma and Life Sciences," will delve into how generative AI is transforming pharmaceutical industry on a global scale. Aligned with the Summit's theme of fostering cross-border collaboration, this session will explore real-world applications, innovative practices, and the profound impact of generative AI, offering attendees invaluable insights into the future of global pharmaceutical data analytics.

In this presentation, we will cover the following topics:

• Understand current business challenges and why a “data products strategy” is crucial to accelerating Gen AI solutions
• Outline our Analytics Ready Data (ARD) products solution implemented in an accelerated timeframe
• Showcase two powerful Gen AI use cases enabled and their business impact
• Incentive Compensation Health Check
• Precision Content Personalization

Sharing insights from the development of ML models to predict launch timelines using clinical trials data and regulatory approvals, with learnings from implementation of diverse approaches to improve data preparation and model outcome. The presentation will also highlight how the output can be used in launch or strategic forecasting to improve scenario design.

At Pfizer, we offer a wide range of medications and vaccines across more than 200 countries. We generate extensive in-house content through various channels, including promotions, surveys, and feedback, presenting valuable opportunities for transfer learning and strategic improvements across international markets. By developing a unified framework to standardize this data into a searchable format, we empower marketers to quickly find relevant content and extract insights. This enhances tactical planning for new content creation addressing common challenges across geographical regions and brands. To support this, we have introduced an AI-powered auto-tagging solution that improves content searchability, uncovers key insights about customer preferences, and enhances content relevance. Our goal is to leverage GenAI to build a smart, scalable, and cost-effective solution for all content types while addressing challenges in handling international datasets and ensuring trust and quality through appropriate guardrails in a complex global data landscape.

The use of Generative AI, specifically LLMs, can help pharmaceutical teams extract insights from large, structured datasets to support decision-making. Generative AI enables non-technical users to access data-driven insights with ease, improving market analysis, patient outcomes, and regulatory reporting. However, implementing LLMs for structured pharmaceutical data poses challenges, including complex data schemas, high query complexity, context limitations, latency requirements, and domain knowledge gaps. To address these challenges, schema-aware prompting techniques, iterative database exploration with agents, intermediate representations, and robust validation systems are employed. By sharing practical insights, this presentation aims to help pharmaceutical data professionals implement and scale Generative AI applications to enable agile, analytics-driven strategies, leading to faster, globally informed decision-making.

In this presentation, we will explore the innovative use of transformer models and image data to enhance patient event prediction in healthcare, with a focus on rare diseases. Traditional data sources like claims often fall short in identifying specific patient populations for personalized treatment strategies. Our study addresses these challenges by leveraging GenAI and transformer-based algorithms to process sequential data, offering new insights and improving productivity, revenue, and process transformation in healthcare.

We will discuss the development and fine-tuning of RareBERT, a rare disease-specific transformer model, using six years of data. By integrating image-extracted features from scanned EOB documents, RareBERT significantly improves the accuracy of predicting patient events, achieving a hit rate three times better than random predictions. Attendees will gain insights into the methodology, results, and the potential of transformer models in advancing healthcare research and patient care.

ETL is possibly the most time intensive part of analytics. Documenting the process, exceptions, business rules is never enjoyable, yet necessary. As data becomes increasingly dynamic, Extract, Transform, Load (ETL) processes require efficient solutions that maintain data quality, accessibility, and compliance. Generative AI (GenAI) is emerging as a transformative tool in ETL, enabling intelligent automation, standardized data handling, and streamlined documentation.

GenAI has the capability to apply uniform processes to non-standardized data sets- this is especially valuable. Organizations often work with data from diverse sources. For the sake of this workshop, we will use marketing data as a backdrop. Each vendor has a different data structure. Each department within the organization – finance, sales, CRM has a different data structure. Each time a new source is added- new processes must be created to integrate the data. So far, this is done by a team of engineers. GenAI can intelligently interpret and transform these disparate data sets, ensuring seamless integration across varied formats and minimizing manual adjustments.

Beyond this, GenAI automates the generation of metadata and can also automatically assign critical attributes—such as therapy area, brand lifecycle, and customer segment, marketing channel—based on the content and context of the data. This automated tagging further enhances usability across teams, and broadens the scope of analytics possible. Integrating GenAI into ETL processes creates a more agile and transparent data environment, where non-standard data is standardized, and critical attributes are automatically assigned. This abstract explores how GenAI can transform ETL workflows by simplifying data integration, enhancing traceability, and supporting compliance, ultimately fostering a more data-driven and efficient organization.

Learning Objectives

• Discover GenAI's role in automating ETL for non-standard data
• What kind of prompt engineering would you apply here?
• What are the limitations of using GenAI in ETL. It is not a magic wand.

This presentation explores how Generative AI is revolutionizing Real-World Evidence (RWE) by accelerating insights, optimizing clinical trials, and enabling personalized medicine. It highlights the role of cutting-edge technologies such as Agentic AI for autonomous hypothesis testing, DistilBERT for scalable NLP-driven text mining, and Multimodal AI for integrating diverse datasets like genomics, wearables, and clinical notes. The presentation also delves into Digital Twins, Federated Learning, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs), showcasing their impact on synthetic data generation, privacy compliance, and predictive analytics. The session outlines challenges like privacy and regulatory compliance, offering solutions through Explainable AI (XAI) and federated learning. Attendees will leave with strategic recommendations for adopting AI-driven RWE platforms, upskilling teams, and driving healthcare transformation to deliver faster, more reliable evidence generation.

Join us for an engaging presentation that delves into the transformative power of Artificial Intelligence in the pharmaceutical industry. Discover how new age GenAI Chatbots enable organizations to tap into the vast wealth of knowledge contained in their extensive data sources, facilitating data-driven decision-making, fostering personalized interactions, and driving better outcomes for both sales teams and healthcare providers. The session will cover:

• Introduction to GenAI in Pharma: A brief overview of how GenAI technology is being integrated into pharmaceutical operations.
• Enhanced Decision-Making: Streamlining Pre-Call Planning by providing quick, tailored insights to reduce preparation time.
• Contextual Recommendations: How AI tools provide real-time insights and predictive analytics to better guide field representatives.
• Efficiency and Productivity: Ways in which AI automation streamlines routine tasks, allowing the field force to focus on high-value interactions.
• Enhanced Collaboration: Facilitates integration across teams like MSLs, Sales Reps, and TLLs.
• Scalability and Security: Scalable insights across therapeutic areas that ensure compliance with privacy and regulatory standards.

More information coming soon!

In today’s data-driven landscape, ensuring data quality (DQ) is essential for accurate decision-making and operational efficiency. However, traditional DQ methods are often reactive, addressing issues only after they arise, leading to delays and resource strain. To address this, we have developed a proactive Data Quality Framework that integrates structured interventions at every stage of data management to prevent issues before they impact operations. By leveraging Gen AI capabilities, this framework automates DQ checks and predictive analytics, creating a robust solution that strengthens data integrity and reliability across complex, multi-source processes.

This panel will explore common commercial applications of GenAI in pharma. Our panelists will explore potential use cases where GenAI can have significant impact while transforming existing processes and ways of working.

This study incorporated the use of LLM to extract relevant information from the clinical notes. We explored a variety of prompts to generate a structured output that accurately captured the dates and corresponding medications for a patient’s treatment in oncology. The goal was to identify a prompt that would minimize hallucination and produce concise, structured responses. The optimal prompt that emerged from this exploration instructed the model to act as an assistant providing a therapy summary focused solely on relevant treatments. The prompt specifically guided the model to list the therapies by date or year in a numbered format, ensuring that only the information directly related to treatments of interest was extracted, without any additional or extraneous details. This approach effectively reduced hallucination and ensured that the output was more precise and relevant.

A Generative AI–enabled PMR solution that automates multi-format data processing (text, audio, video), uses Graph-based RAG with domain ontologies for high-accuracy insights, and accelerates decision-making across pharmaceutical therapeutic areas.

Segmentation and targeting processes are the real-world manifestations of pharma sales force strategy. Using reps’ time wisely to target the most valuable customers with precise messaging is critical to commercial success. Pharmaceutical companies use multiple data sources and advanced analytical techniques to ensure their reps get a competitive advantage when selling products.

Our presentation will address how sales reps can deeply improve their understanding of their customers and make them more productive in the field. It will also show how a harmonized framework for Generative AI-driven segmentation can ensure standards and address local market nuances.

More information coming soon!

This abstract presents a GenAI-powered automation framework that streamlines workflows in the pharmaceutical industry by automating tasks across platforms like Jira, Confluence, Git and Azure. Leveraging advanced AI techniques, it accelerates project timelines, enhances code quality, and improves decision-making, driving global productivity and efficiency.

In today's data-driven pharmaceutical industry, the ability to derive actionable insights from vast datasets is critical. Traditional dashboards often provide a static view, limiting the potential for dynamic and in-depth analysis. This limitation means that sales representatives, marketers, and leadership teams often lack the specific, real-time insights needed to make informed decisions.

Our presentation introduces the GenAI Copilot for Insights and Analytics, a sophisticated Generative AI tool designed to enhance how healthcare data—including EHR, prescriptions, clinical trials, and marketing activity data—is utilized. The GenAI Copilot leverages Large Language Models (LLMs) to process and synthesize data from multiple sources, providing real-time insights that are readily accessible. This tool empowers sales representatives and marketers by offering a better understanding of market trends and HCP activities, enabling more informed decision-making. The GenAI Copilot provides tailored insights that cater to the specific requirements of each role in Commercial Analytics teams, thus limiting the need for multiple static dashboards with a single, adaptive solution.

We will explore the current landscape in pharma, the limitations of existing tools, and how the GenAI Copilot addresses these challenges. Participants will gain an understanding of how this technology can drive improvements in commercial insights and analytics, fostering more effective strategies and outcomes.

Field Managers (FMs) in the pharmaceutical industry generate thousands of Field Coaching Reports (FCRs) each quarter, yet the traditional process of analyzing these reports has been inefficient, time-consuming, and lacking standardization. The reliance on external vendors for manual sampling and analysis led to inconsistent insights and limited personalization, requiring over 1000 hours per analysis cycle.

This innovative project leverages OpenAI’s GPT-3.5 APIs to transform FCR analysis into an automated, enterprise-wide solution. Capable of processing over 5000 reports across diverse brands and franchises, the tool provides Field Managers, Regional Directors (RDs), and Vice Presidents (VPs) with personalized, AI-driven insights. These insights enable FMs to deliver more impactful feedback and adapt their coaching strategies, fostering better field performance.

Smart Assist is a conversational GEN AI assistant that enables your field teams to pull real-time insights on payouts, contests, and eligibility, streamlining performance tracking. It improves operational efficiency by making essential information accessible on the go, saving time and effort for field reps, and allowing them to focus on their sales goals. ZAIDYN™ Augmented Analytics is a Gen-AI driven tool to derive reporting insights, create custom KPIs, build reports, perform data analyses, generate data visualizations by using simple textual prompts. It also helps derive valuable patient journey related insights, highlight custom KPIs, build visualizations to aid decision making by using simple textual prompts enhancing patient-focused outcomes.

More information coming soon!

We will share the experience of scaling teams. The presentation will cover team structure, talent management, communication, cultural differences, and productivity. We will explore the right mix of business and technology skills.

The panel will discuss the most recent technologies around data analysis expansions and their impact on pharma in medical and commercial areas. Gen AI and how some big pharma and pharma consulting has responded in the last year will be shared by our panelists. Our panelists will also discuss perspectives on the near future evolution of data analytics and digital journey of pharma and what trends they are currently observing.

The presentation outlines a strategic approach to reinforce the foundational journey for Next Gen Analytics. Next Gen Analytics Excellence denotes a company's ability to adeptly leverage advanced analytics technologies and techniques for informed decision-making, insights generation, and gaining a competitive advantage. This proficiency necessitates the integration of cutting-edge analytical tools and methods with robust data management processes, facilitating the extraction of actionable insights from vast and intricate datasets. However, organizations encounter challenges in their preparedness to adopt and execute Next Gen Analytics initiatives, including a lack of foundational knowledge and limited data capabilities.

It is imperative for organizations to focus on the five-stage process to construct foundational analytical processes, ensuring preparedness for Next Gen Analytics. It encompasses defining of the business problem, prioritizing key business questions, identifying the right data, and continuous data enrichment to maintain perpetual readiness for the demands of Next Gen Analytics. Additionally, the continuous cycle emphasizes the significance of asking precise questions, optimizing Gen AI capabilities for maximum impact, and refining the process continually. This approach will ensure a resilient foundation aligned with the evolving nature of business and technological landscapes. Overall, a comprehensive foundational guide for Next Gen Analytics will position organizations to seamlessly integrate advanced analytics and AI, ensuring sustained value generation amidst the challenges of a dynamic environment.

More information coming soon!

Novartis, being a data-driven organization, is dedicated to reducing the time required to act on new recommendations and insights that benefit both patients and caregivers. The Commercial Data Sciences team has developed an approach that utilizes advanced ML/Ops solutions to rapidly and effectively build data science products using the AI ecosystem suite. The proposed architecture facilitates the seamless integration of statistical models and the timely orchestration of model recommendations. Moreover, it allows Data Scientists to monitor real-time model performance through a continuous integration and deployment philosophy, intervening as necessary to correct data or model deviations. This user-friendly architecture includes data standardization layers, feature engineering practices, and customizable model design layers. Lastly, we would like to highlight the limitations in building ML products and address the unique data challenges faced in the pharmaceutical industry.

This presentation emphasizes the importance of aligning marketing strategies with dynamic supply chain demands. We will discuss integrating market insights and supply chain intelligence, enabling organizations to anticipate and respond effectively to changes.

Additionally, we will explore Market Mix Modelling (MMM) with mathematical optimization, a powerful analytical technique for understanding the effectiveness of marketing activities and improving resource allocation. Delving into MMM methodologies and practical applications, we will showcase how it guides resource allocation and improves ROI, while dealing with challenges of limited data internationally.

Moreover, we will also address challenges in supply chain management, particularly related to demand forecasting. Traditional methods often fall short in capturing uncertainties, leading to suboptimal decisions and increased risks. The adoption of probabilistic forecasting models, such as DeepAR, offers a promising solution to these challenges, enhancing forecasting accuracy and mitigating risks in supply chain management.

This presentation will explore the use of AI to harness data sources for a profound understanding of the underlying 'WHYs' behind physician behaviors and enable marketers to create customized content and messaging based on these critical 'WHYs' to drive brand growth. This presentation will address four key aspects:

• The Limitations of Traditional Data in Uncovering the Deep 'WHYs' of Physician Behavior
  While traditional data sources offer a glimpse into the behaviors of individual physicians, they fall short in revealing the beliefs, motivations, and deeper WHYs underlying these actions. Although market research can provide additional context, it often represents a generalized view based on a limited sample of physicians, resulting in an incomplete picture of their genuine needs and identities.
• Extracting Deeper 'WHYs' at the N=1 Level Using Diverse Data Sets
  We will examine strategies for going beyond conventional data, employing first, second-, and third-party datasets with AI to unearth the comprehensive factors driving individual physician behaviors. This approach aims to achieve a nuanced understanding of each physician's unique motivations.
• Reimagining marketing planning to enable N=1 Level Insights
  Despite the capability to extract insights at the N=1 level, practical application calls for a more strategic approach. This segment will outline how to develop customized content and strategies aimed directly to address 'WHYs' and create personalized marketing plans for individual physicians based on the specific 'WHYs' influencing them.
• Implementation by Pharma Companies
  The concluding section will illustrate how pharmaceutical companies are successfully implementing this personalized strategy, aligning it with precise N=1 level targeting to achieve more effective and impactful marketing outcomes.

More information coming soon!

Customer Experience, or CX, has become a pivotal aspect of pharmaceutical sales and marketing. A holistic, 360-degree Customer Experience is integral to both Omnichannel and Customer Experience transformations. In this session, we will delve into the critical factors for successful Customer Experience transformations, discuss the challenges encountered by our panelists, and share their success stories.

More information coming soon!

More information coming soon!

The debut of ChatGPT has been a watershed moment in integrating of Generative AI for enterprises. Teams across industry verticals and functions have been experimenting with Gen AI POCs to understand its optimal application. While the potential is unquestionable, we're still in the nascent stages of fully harnessing the value of Gen AI applications.

In this session, we aim to share our insights and experiences working on over 20 use cases across Pharma and other industries. Our presentation explains how Gen AI can yield additional value compared to traditional AI/ML methodologies. We share the diverse ways teams across industries leverage Gen AI and the most prevalent use cases emerging within Pharma. We will also discuss frameworks based on value/complexity and user journeys to aid in planning and prioritizing use cases for maximum impact. Additionally, we address common pitfalls encountered during the development of Gen AI solutions for Pharma Commercial teams and recommend ways to avoid those.

More information coming soon!

Dive into the heart of our presentation where we introduce a revolutionary Generative AI solution designed to elevate the effectiveness of the Email channel in the Life Sciences domain. The two meticulously crafted modules are poised to redefine the landscape of email marketing, providing tangible solutions to industry challenges.

Optimization of Email Subject Lines with GenAI and ML:
Embark on a journey with our Reinforcement Learning-based Omni-channel Generative AI. This module, driven by exploration and exploitation techniques, leverages historical engagement data to dynamically craft subject lines and preview texts in real time. The primary goal is to substantially boost physician engagement by increasing open rates, setting a new standard for impactful email communication.

Dynamic Generation of Personalized Rep Triggered Emails using OpenAI:
Witness the empowerment of Sales Reps through this innovative module. By incorporating user inputs into email templates, the model seamlessly generates personalized emails with improved open rates. The evaluation against predefined criteria ensures a strategic and engaging approach, empowering Sales Reps to enhance their communication effectiveness.

Join us for a live demo during the session to witness firsthand how these groundbreaking AI modules can transform your email marketing strategy and elevate your engagement metrics.

The vision for this presentation is to take selected practices from Customer Experience (“Cx”), combined with storyboarding and effective data storytelling, to design and execute exceptional “client” meetings. The presentation wraps-up with an aspirational message about why all of us work so hard supporting our “clients” in the life sciences industry.

Key topics, each with a “pro-tip” takeaway, include:

• “Who?” is the most important question – how to assess your audience?
• How to design a customer experience (i.e., “the meeting”) to satisfy both the PhD and the marketeer?
• Storyboarding for success – how to elicit the BEST work from your (global) team?
• Data storytelling to elicit insights and decisions – how to communicate for human impact?

More information coming soon!

RWE is gaining momentum as a valuable tool for understanding real-world drug effectiveness, informing healthcare decisions, and ultimately improving patient outcomes. Evolution in today’s computational space, especially AI changes the dynamics of the way we treat and utilize RWE data.

We discuss the following key areas:

• Key challenges in implementing RWE and how RWD integration can be achieved cost effectively today.
• Utilizing RWE beyond the traditional use cases.
• XAI – Examples of how explainability can be modeled into your enterprise analytics.
• How can your organization unlock the potential of RWE today, with meaningful approaches?

In an era defined by rapid technological advancements and increasing cost pressures, Global Capability Centers have emerged as strategic hubs driving innovation, efficiency, and global competitiveness. This panel discussion aims to delve into the key trends, challenges, and success stories surrounding GCCs, providing attendees with valuable insights to navigate their own journey toward operational excellence.

Key Themes to be covered in the panel:

• Exploration of critical aspects involved in establishing Global Capability Centers.
• Discussion on the pivotal role played by advanced technologies and platforms in enhancing the success of GCCs.
• Discussion on emerging trends and innovations shaping the landscape of GCC operations.
• Sharing of valuable lessons learned from practical experiences in operating Global Capability Centers.
• Discussion on the significance of talent development in building high-performing GCC teams.

More information coming soon!

AI and NLP are universally applicable and malleable technologies that can be applied to many domains in the pharma industry, improving both patient outcomes and business performance. These tools offer invaluable insights from unstructured data, particularly in reporting and analytics for late stage and launched products. However, transitioning from a proof of concept (POC) to a productive environment presents challenges, especially in demonstrating the value of these AI/NLP solutions and establishing the role of the product owner an organization function with no prior experience.

We will address transitioning from a POC to a productive environment including strategies for communication and cross-functional collaboration as well as strategies for quantifying and communicating the value of AI projects in the medical domain in the pharma industry.

Finally, we will discuss the creation of a product owner role in an organization that has little experience of this role. We will outline the specific competencies, roles, and responsibilities, which may need to be acquired or developed within the organization. Additionally, we will discuss strategies for fostering a culture of innovation and collaboration is crucial to successfully implementing AI/NLP solutions.

This presentation will focus on a case study showing how EMR-equivalent data provides high quality, longitudinal patient data to support product launch in an uncommon disease in four European countries.

Uncommon disease background:

• Rare disease <1,500 patients in each major European country. Treatment is loaded towards a subset of the institutions and life science companies have a list of target institutions.
• Diagnosis via complex combination of genetics, diagnostic tests, plus S&S.
• Progressive condition with acute relapses. Acute episodes are treated differently from progression – a key data target.
• Until recently no labelled treatments.
• Client need and data objectives
• In each of four major European countries (Germany, France, Italy and Spain)

Detailed patient journey:

• Diagnostics and interaction with comorbidities
• Progression and treatment
• Relapse dynamics

Data objectives:

• Market structure
• Dynamic market evolution
• Validation of existing data sources
• Registries

The client need defined as follows:

• Longitudinal patient clinical records tracking from pre-diagnosis (EMR data)
• Data from target institutions
• Forward-looking tracking to monitor shifts in treatment behaviors and new treatments

Data capture and analysis. Proprietary data capture tools used to achieve:

• Diagnostic journey and adherence with guidelines
• High response from target tiers
• Representative data coverage
• Full EMR records on each patient, allowing for complex multi-dimensional analytics

Conclusions: This presentation will show how this method can be applied and shows how the method is being expanded by specialty and geography.

The session will be focused on answering the following questions:

• Why is it important for LS companies to strategically plan the promotion budgets in Italy?
• How Market Mix Modelling is a valuable solution to make data-backed informed decisions?
• How to effectively build an MMM solution despite limitations like lack of HCP level sales and other associated constraints given the market landscape?
• What are the expected outcomes of this MMM solution?
• How to scale this up for other EU countries?

Precision medicine powered by novel targeted therapies is revolutionizing oncology care for patients based on their protein expression levels and genomic phenotype. As the pharmaceutical landscape continues to evolve with these treatment advances, there has been a shift in strategy to focus more on improving patient health outcomes and equity. This shift in focus ensures that more patients receive best-in-class and optimal treatment. This requires health care providers to be more aware and empowered to administer comprehensive care based on the latest approved guidelines. Improving the ability to identify these unmet needs, both for patients as well as physicians, enables pharmaceutical organizations to become strategic partners in ensuring delivery of optimal care to patients.

To adapt to these changes in practice, its important to understand the needs of both patients and physicians. Understanding deviations from optimal treatment and quantifying gaps in care can define priority focus areas for the pharma industry. These insights can impact clinical trial planning and enhance the specificity of commercial and medical initiatives including targeted educational programs. These insights will help define medical strategy and guide proactive and integrated evidence generation planning. Finally, the pharma industry can use these analyses to better plan their resource allocation and drive enhanced physician and patient engagement experiences.

AI and NLP are universally applicable and malleable technologies that can be applied to many domains in the pharma industry, improving both patient outcomes and business performance. These tools offer invaluable insights from unstructured data, particularly in reporting and analytics for late stage and launched products. However, transitioning from a proof of concept (POC) to a productive environment presents challenges, especially in demonstrating the value of these AI/NLP solutions and establishing the role of the product owner an organization function with no prior experience.

We will address transitioning from a POC to a productive environment including strategies for communication and cross-functional collaboration as well as strategies for quantifying and communicating the value of AI projects in the medical domain in the pharma industry.

Finally, we will discuss the creation of a product owner role in an organization that has little experience of this role. We will outline the specific competencies, roles, and responsibilities, which may need to be acquired or developed within the organization. Additionally, we will discuss strategies for fostering a culture of innovation and collaboration is crucial to successfully implementing AI/NLP solutions.

This presentation will focus on a case study showing how EMR-equivalent data provides high quality, longitudinal patient data to support product launch in an uncommon disease in four European countries.

Uncommon disease background:

• Rare disease <1,500 patients in each major European country. Treatment is loaded towards a subset of the institutions and life science companies have a list of target institutions.
• Diagnosis via complex combination of genetics, diagnostic tests, plus S&S.
• Progressive condition with acute relapses. Acute episodes are treated differently from progression – a key data target.
• Until recently no labelled treatments.
• Client need and data objectives
• In each of four major European countries (Germany, France, Italy and Spain)

Detailed patient journey:

• Diagnostics and interaction with comorbidities
• Progression and treatment
• Relapse dynamics

Data objectives:

• Market structure
• Dynamic market evolution
• Validation of existing data sources
• Registries

The client need defined as follows:

• Longitudinal patient clinical records tracking from pre-diagnosis (EMR data)
• Data from target institutions
• Forward-looking tracking to monitor shifts in treatment behaviors and new treatments

Data capture and analysis. Proprietary data capture tools used to achieve:

• Diagnostic journey and adherence with guidelines
• High response from target tiers
• Representative data coverage
• Full EMR records on each patient, allowing for complex multi-dimensional analytics

Conclusions: This presentation will show how this method can be applied and shows how the method is being expanded by specialty and geography.

The session will be focused on answering the following questions:

• Why is it important for LS companies to strategically plan the promotion budgets in Italy?
• How Market Mix Modelling is a valuable solution to make data-backed informed decisions?
• How to effectively build an MMM solution despite limitations like lack of HCP level sales and other associated constraints given the market landscape?
• What are the expected outcomes of this MMM solution?
• How to scale this up for other EU countries?

Precision medicine powered by novel targeted therapies is revolutionizing oncology care for patients based on their protein expression levels and genomic phenotype. As the pharmaceutical landscape continues to evolve with these treatment advances, there has been a shift in strategy to focus more on improving patient health outcomes and equity. This shift in focus ensures that more patients receive best-in-class and optimal treatment. This requires health care providers to be more aware and empowered to administer comprehensive care based on the latest approved guidelines. Improving the ability to identify these unmet needs, both for patients as well as physicians, enables pharmaceutical organizations to become strategic partners in ensuring delivery of optimal care to patients.

To adapt to these changes in practice, its important to understand the needs of both patients and physicians. Understanding deviations from optimal treatment and quantifying gaps in care can define priority focus areas for the pharma industry. These insights can impact clinical trial planning and enhance the specificity of commercial and medical initiatives including targeted educational programs. These insights will help define medical strategy and guide proactive and integrated evidence generation planning. Finally, the pharma industry can use these analyses to better plan their resource allocation and drive enhanced physician and patient engagement experiences.

Learn how real-time profile information helps drive launch and commercialization strategies. Hear best practices for identifying, researching, and informing interactions with scientific experts.

The explosion of numerous promotional and communication channels raises many questions regarding optimizing channel investment. More importantly, it has become critical to identify which channels to use across various HCPs (e.g. office-based GP and specialists, hospital-based specialists) and across product portfolios (inline/mature vs. newly launched brands). An evidence-based approach is needed to make both strategic and tactical decisions with confidence. While there are multiple ways to evaluate multi-channel promotional effectiveness, a focus on the aggregated impact of channels on sales is the key to measuring ROI. Going a level deeper, linking HCP level promotional activities with the most granular HCP/segment sales data available in EU in accordance with data privacy laws can help uncover how different promotional activities impact various segments over time.

Using a customized advanced analytics approach to quantify impact in a robust and accurate manner can yield previously unrealized HCP/segment-level insights, recommendations and optimization. Once in place, the model new data is periodically evaluated providing sustainable channel optimization and fine-tuning.

Given the increasing importance of patient centricity, it has become paramount for brand managers to incorporate patient-centric approaches as part of brand strategy. To better understand drivers of brand performance and market potential, approaches that combine social media channels, anonymized longitudinal patient level data and AI/machine learning techniques can uncover brand strengths and growth barriers while quantifying the value of each patient segment.

This abstract investigates patient journey, initiation drivers, socio-demographic variables, co-medication and side effects as determinants of brand success. We also examine similarities across patient profiles at various stages in the patient journey, the impact of various treatments and how to cluster patients. Finally, a deeper look at patient lifetime value assessments can indicate brand priority areas and patient upside potential.

The challenge of useful application of AI in a large variety of fields has to do with understanding of context. Richness and openness of context distinguishes areas of as-of-yet limited success of AI in comparison with applications like image recognition and game-playing. In the world of commercial pharma the problem is multiplied by manifold. For a CRM system there are TWO customers – the sales representative and the physician, or THREE if one counts the patient. That means that three contexts have to ‘merge’ to make for successful interactions, and each of these is complex by itself.

We exemplify AI’s context challenge and highlight the key success factors for dealing with it using a two case-studies involving making useful & personalized suggestions for communications between sales representatives and HCPs. In one case the emphasis is on the communications channel, in the second the focus is on contents. In each case we show the key elements of context and explain how they are tracked and used. The use cases provide a useful illustration of what AI really means – what a well-rounded AI applications addressing a complex business problem should consist in, and it illustrates the need to go well-beyond Machine- or Deep-Learning alone.