2023 Global Summit

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.

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.

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?

Generative AI has emerged as a disruptive trend in many industries, and the Pharma stands to benefit significantly from its applications. According to an estimate by Mckinsey, Gen AI has the potential for 15-25% value potential as a % of the operating profit in the Pharma industry. This is a story of staggering potential.

One of the applications is use of Gen AI in virtual reps, where Pharma companies can create personalized customer experiences that are tailored to the customer's needs to increase overall engagement. According to a survey conducted by IQVIA in 2020, nearly 80% of pharmaceutical companies reported using virtual reps as part of their sales and marketing strategies. This represents a significant increase from just a few years ago, when virtual reps were still a relatively new concept.

In this talk we will present an overview of the benefits and applications of Generative AI for virtual reps in the Pharma. We will also discuss challenges associated with implementing Gen AI technology into existing systems.

Finally, we will provide examples of how successful implementations of Gen AI for virtual reps could look like. Through this presentation, attendees will be able to gain an understanding of how Generative AI can be used to enhance customer experiences and drive business growth in Pharma

In this talk, we will delve into the implications and applications of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical forecasting. The pharmaceutical industry is already witnessing the critical role played by AI and ML in various areas such as drug discovery, drug manufacturing, diagnostic assistance, and optimizing medical treatment processes. However, understanding the impact of these technologies on forecasting requires exploration of both their potential application in the forecasting process and the broader implications of an AI revolution on the industry's structure and the evolution of diseases.

Join leading experts from J+D Forecasting as they shed light on the transformative potential of AI and ML in pharmaceutical forecasting, enabling more precise predictions, improved decision-making, and strategic guidance for the industry.

The presentation will explore the challenges that will be faced and the hurdles that will need to be overcome if pharmaceutical management science is to deliver to its true potential in Europe. The presentation is underpinned by a 2-cycle delphi exercise based on broad expert opinion (n=41) which was used to determine the key evidence pricing and access challenges facing healthcare and the pharmaceutical industry in Europe over the period 2023-30.

In Europe, orchestrating multiple channels and tactics to drive maximum customer impact can involve significant investments and operational efforts at the country level. At the same time, the unavailability of HCP-level sales or prescription data makes measuring HCP-focused tactics challenging.

As pharma companies adopt digital for their European markets, they are moving toward sophisticated, interlaced, multi-channel tactics or NBA programs. This session will describe methodologies to measure the relative importance and impact of different tactics and recommendations within the NBA program.

Attendees will leave with an understanding of how a test- and control-based approach to measuring the impact of HCP-focused NBA programs can be implemented in their company with tactic level recommendations.

Cancer remains a significant global health challenge, necessitating the development of effective treatments. Targeted therapies have emerged as a groundbreaking approach, offering hope for patients across various tumor types. However, these therapies face hurdles in their adoption due to limited awareness and knowledge among healthcare professionals (HCPs). This session focuses on the vital role of pharmaceutical companies' Medical Affairs departments and Medical Science Liaisons (MSLs) in improving education and awareness of oncology therapies among HCPs.

Drawing upon an extensive analysis of over 3 million HCP-industry interactions in oncology, collected from Veeva Pulse, and scientific awareness data on more than 30 oncology treatments in Germany, we explore the correlation between scientific awareness and medical activities. Additionally, we employ graph machine learning analysis to assess the variance in scientific awareness among clusters of HCPs interested in comparable research topics.

Our study presents a methodology to measure and optimize the performance of medical activities conducted by MSLs, with a specific focus on targeted treatments. The findings hold significant implications for pharmaceutical companies in educating and training HCPs on the utilization of new cancer treatments. By effectively measuring performance and implementing strategic MSL approaches, medical education departments can greatly contribute to ensuring prompt and efficient patient access to novel targeted oncology therapies.

Join us for this session to gain valuable insights into enhancing medical education and driving awareness of targeted therapies, ultimately improving patient outcomes in the field of oncology.

Executing consumer omnichannel strategies in the pharmaceutical domain presents distinct difficulties due to the sensitive nature of patient information and the necessity to adhere to data protection regulations. This presentation will delve into the common obstacles encountered in pharma consumer omnichannel initiatives and illustrate how combining data tokenization and clean room techniques can effectively resolve these issues while enabling precision marketing and sophisticated analytics.

Increasing availability of data points across multiple channels of communication, along with the desire from Pharmaceutical companies to meet the engagement expectations of Health Care Providers (HCPs) while ensuring they are made aware of the latest product developments, is creating a challenging omnichannel optimization problem.

At MSD, embedding analytics and AI/ML into the core of our omnichannel strategy has aided our approaches to solving the omnichannel optimization problem. While the principles that drive our business and brands decisions have not changed that much and remained very much predictable - how we can optimize my resources to create the biggest impact in our HCPs so that we can improve patients’ lives - our next best engagement strategy has evolved to accommodate a dynamic channel landscape that is increasingly becoming personalized.

In this session, Roberto Baguer provides his perspective on 4 winning analytical strategies to help evolve next best engagement. While the focus is United States, it is applicable for the rest of our markets worldwide.

Disease outbreaks cause market-shifting impacts, and the world looks to pharmaceutical companies to be ahead of the game. Recent innovations in large language models offer a compelling opportunity to detect outbreaks earlier and easier than ever before... however, it comes with numerous challenges. The credibility of these models is critical in order to be adopted and trusted by decision makers. In this talk, we will share the challenges, lessons learned, and pitfalls encountered as we adapted to novel LLM technologies. Attendees can expect a candid and insightful, “behind the scenes” look of the challenges involved in building models that inspire trust in clients.