2017 European Summit

Why bother looking at what's going on in the US if we are not doing business in the US? Everything is different: targets, drug basket, referral dynamics, reimbursement system, and the list goes on. While this observation is legitimate, it misses a larger truth. There are invariants about the disease state and the therapy that cut across geographic borders and socio-economic markets. They transcend the make-up of the drug basket, pricing and reimbursement, and cultural norms and expectations. These invariants are right there, embedded in the data and eager to reveal themselves to those who bother to look and ask the right questions. There is yet another reason to look at data from the US. It is that the data is extremely rich both in granularity and breadth and, as a result, odds are good that analysis of the US data will uncover valuable insights.

In this talk, we'll provide an overview of all the major data sources that are available in the US. They include EMR, Open and Closed Patient Claims, EMR-Claims combinations, Lab Orders and Results, Charge Data Master, Remit, GPO, Sell-in, Co-pay and Discount Cards, Foundation Assistance, Shipment and Distribution, Chargeback, Utilization, Digital, and the list goes on. To provide structure to what would otherwise be a laundry list, we'll describe journeys patients undergo to get the drug, point out data collection points along the way, and discuss the data sources that emerge as a result. We'll take a close look at several patient journeys as each journey gives rise to a different set of data sources. They are:

• Patient gets a drug in a brick-and-mortar pharmacy
• Patient gets administered a buy-and-bill drug in the Physician office,
• Patient gets a specialty drug from an SP after going through a Hub, and
• Patient gets the drug while being at the hospital

Since a database is only as good as the answers it provides, we'll discuss business questions databases help illuminate, how they are deployed, the types of insights they provide, and their limitations and caveats. We'll also describe emerging trends of the industry that are redefining how pharma data is being produced and consumed. The most prominent trend is undoubtedly that of alerts, triggers, and pop-ups which is blossoming as the industry keeps its steady focus on Specialty drugs, Rare Diseases, and Oncology.

Learning Objectives: After attending this session, attendees will

• Be better equipped to develop a winning global data strategy.
• Have a broader perspective on data and analytics as we compare the offerings from Europe with the offerings from the US.
• Understand the rationale behind the ongoing shift in the marketplace and the upcoming changes we need to gear up for.

With the growing presence of social platforms and with around 40% of the world online, the Pharmaceutical industry’s stakeholders' online presence is also growing. The Pharma has traditionally looked to historical marketing engagement activity and market research to understand customers. This growth of online presence and dialogue offers Pharmaceutical companies an opportunity to learn real-time about customer behavior, preferences, reactions, influencers, and markets. Insights from these online conversations is only amplified when this data is merged with structured data.

However, it is a challenge to find Pharma-relevant conversations among the millions of conversations present online and collecting data via social listening tools is only the beginning. Conversations need to be put in context to help extract insights.

In this presentation we will share how modern technologies, such as big data and natural language processing (NLP) can be used to help put raw, online conversations in context. We will also share what insights can be gleaned from contextualized conversations and how these conversations can be joined with traditional, structured data sets for more advanced analytical applications. For example, social data can be used to validate forecast assumptions and to better forecast clinical trial enrollments. Join us to learn about how to analyze social data and the opportunities made available from doing so.

Most data-driven marketing decision often assumes that given the right information, physicians will act on it. If that hypothesis is true, then:

• Why don't physicians always prescribe the most logical and efficacious drug to the relevant patient?
• Why, in the age of data and technology proliferation, are most marketing departments under significant pressure to show ROI?
• Why are we still unable to decode the "Why" behind behaviors that often cannot be explained by data?
• Why are we still unable to answer with certainty which half of marketing is wasted?

The session will address how to close the knowledge gap between what we think vs. what is the truth. It will provide a new way to look at the data and physician decision hierarchy. Using the art of decoding and the science of decision making, we will show a revolutionary way for marketers to optimize their multi-channel efforts. Through a case study, we will shed light on key differences in effectiveness of “small” vs. “big” data for multi-channel marketing, and shed light on:

• Blind spots of data-driven analytics marketing optimization
• The science of how physicians use pre-programmed, mental shortcuts to make over 95% of their decisions. How do we leverage such learnings in multi-channel marketing?
• What is "tiny data" in the world of physicians? How can such data help marketers to influence the lateral thinking that physicians use when prescribing a brand?
• How to integrate tiny data with big data to close the knowledge loop – not only what, how, when, but also “why.”
• How a pharmaceutical company leveraged big and tiny data to generate double digit growth in NBRX
• How to finally close the loop of multi-channel and multi-device marketing with artificial intelligence

The evolving treatment paradigm in Oncology has changed understanding brand and regimen performance relative to mass market therapies. Momentum is shifting to genotype and biomarker-based identification. Treatment decisions can be monotherapy or combination, using infusion or oral administration. Understanding sequencing and its rationale is also unique and challenging. And while Oncology data has progressed, it is increasingly challenging to build robust metrics as therapies are built for smaller targeted patient populations.

In this session, we aim to move from the thinking oncology data in in its common silos of “Research Strategies” and “Data Purchasing Strategies” to brand-centric Information Strategies that enable a much richer view of performance. Across silos of knowledge and information that exists today is an opportunity to better see and integrate across the full range of data available, and serve it back to the organization to enable better decisions for competitive advantage.

This session will offer:

• Perspective on the development of information strategies for better decision-making
• Discussion around formulating KPIs that offer impactful performance measurement
• A framework for considering the pros and cons of various data sources
• Examples of how to bring it all together and the change management required to achieve it

Learning Objectives: After attending this session, attendees will learn:

Effective information strategies to better support a brand or portfolioHow KPIs can offer impactful performance measurement by integrating underlying data sourcesA framework for considering the pros and cons of various data sources, both traditional and emerging

While patient-level healthcare databases continue to proliferate globally, the challenge to any healthcare researcher is how to find the optimal database to solve the research question at hand. Electronic Health Records (EHR), disease registries, claims, and patient-reported surveys each have their strengths and weaknesses. Thus, oftentimes the ideal solution is a combination of fields from one or more databases. While integration of these datasets is becoming more and more commonplace in the United States, it faces tremendous challenges in the EU, where privacy is much more stringent on comprehensive coverage and goes beyond HIPAA’s covered entities and business assoicates.

This presentation will focus on three topics:

• An overview of the current state of real-world evidence (RWE) datasets and the typical way in which these datasets are used in research, including the strengths and limitations of each dataset.
• The approaches being used to link these datasets together to allow for deeper understanding of the patient and therefore lead to more valuable and comprehensive insights for the study. These approaches will include patient-level deterministic matching, probabilistic matching, and propensity-score matching / imputation. The pros and cons of each of these approaches will be discussed.
• The privacy implications of each of these models and how to address them within the complex EU privacy environment.

Learning Objectives: After attending this session, attendees will learn:

• About the different types of real-world data available and some of the strengths and weaknesses of each.
• How critical data integration is as patient-centered research becomes more prevalent
• The potential ways healthcare data can be integrated within the privacy legislation boundary
• The overall privacy challenges when working with real-world data, especially when integrated
• The world-wide privacy landscape

Over the years, the Life Sciences industry has evolved to benefit from a wide range of data sources, spanning across various stakeholders and transaction points (sales, claims, EMR, payer, CMS, etc.). However, the abundance of data does not always translate into impactful insights, either due to data limitations or lack of access to all required sources. In these circumstances, how can we ensure to best leverage available data while effectively informing business critical decisions?

Pharmaceutical companies spend a significant portion of their sales revenue on promoting brands through various marketing channels even in emerging markets. While a major part of the promotional budget is spent on Field Force visits, other promotional activities are equally resource intensive. In the world of competitive spending, companies are likely to overspend on some brands and channels, and underspend on the rest if not guided by insights based on data. However, an optimization study conducted at a high level may not yield the optimal benefit for the business if such a study is not in sync with the local realities of the market space.

Learning Objectives: After attending this session, attendees will learn:

• How to uncovering hidden opportunities within and across portfolios of brands for boosting sales growth & reach forecast goals
• Using econometric modelling approach, the impact (incremental sales) of channels are quantified at the most granular level (territory, brick etc)
• How to employ non-linear optimization techniques for Bottoms Up sales and profit maximization for optimizing resources at most granular level (territory, brick etc)

The presentation will focus on the journey of setting up a digital analytics and reporting capability so that the focus remains on helping the business rather than worrying about data-related issues. Areas of focus will be on setting up and executing the digital analytics capability.

It has become well publicized that many international regulatory agencies have pursued a multitude of methods with the intent of limiting the price that a pharmaceutical manufacturer can charge during a global launch. One of the most common practices for negotiation between companies and governments for reimbursable prices of pharmaceutical products is the application of International Reference Pricing (IRP) rules. IRP is perhaps one of the most significant challenges that the pharmaceutical industry currently faces and it clearly indicates that the industry operates in a globally interconnected environment. In its simplest form, IRP is a government attempt to compare the price of a pharmaceutical agent to that of comparable countries, resulting in a benchmark price that is not substantially different from those compared. For example, an individual country may require that its price for a drug be derived from a rule applied to a specific set of countries, often termed as the “reference basket”. A simple rule could be used where the launch price of the product must take the average of the existing prices in that country’s reference basket. As the launch sequence progresses, these reference relationships evolve into larger and more complex networks that become extremely difficult to navigate, resulting in a challenge that closely resembles the butterfly effect where a small change can have a significant impact later on.

The driving motivation of this practice is to impose the effect of price erosion as companies launch their products in more countries over time. The adoption of IRP is widespread: in 2010 only four of twenty seven European Union Member States did not impose IRP rules. Since then, there has been a significant increase in the number of countries that apply IRP rules, making it a truly global phenomenon.

Much of the research to date has focused on industry behavior from the perspective of economic policy. Studies have, for example, taken a statistical approach to explain market behavior as it reacts to IRP. However, very little has been done to address the optimal launch policy from the company’s vantage despite the significant financial, political, and reputational implications of pricing. Although some companies have taken steps to formalize the process, the level of modeling investment and difficulty related to computational complexity have deterred strict analytical progress in this field. Our experience shows that the industry profoundly needs more rigorous capabilities to compute, address, and implement solutions to this problem.

The clinical complexity of oncology treatment and the dynamic nature of the market presents major challenges for the pharmaceutical industry to accurately forecast the commercial opportunity for emerging therapies. These difficulties must be overcome in order for critical business decisions to be made on the viability of pipeline assets. However, the challenges facing pharmaceutical forecasters are only set to grow because the oncology market will continue to expand and fragment; meanwhile, competition for patient share will become fiercer.

In this presentation we use select case studies to highlight various methodologies used to create detailed bottom-up revenue forecasts for individual emerging therapies. We discuss how forecasting challenges can be overcome through a thorough understanding of the epidemiology of the indication, analysis of how current treatment may leave pockets of clinical unmet need, and how disparate sources and types of data (from primary research, secondary research, and real-world data) can be modelled and triangulated to create robust revenue forecasts in complex settings.

Sales of many high-cost biologics in the EU, comprising some 35% of the current EU biologics market in terms of revenues, will lose patent protection over the next five years, triggering the arrival of biosimilar medicines. Biosimilar competition will result in new market dynamics emerging, major costs savings and significantly reduced revenues which companies will seek to mitigate by introducing new innovation. Success will be driven by competing pricing strategies.

Whilst list prices may appear to be stable, all players will be regularly and directly adjusting their effective net prices in order to win, whilst indirectly attempting to slow down the shift to price commoditisation where all parties lose.

A critical success factor for any company playing in this dynamic market will be the ability to determine the pricing decisions that maximize opportunity and minimize risk.

The presentation will outline an approach to dynamic pricing, based on the principle of “dynamic difference” (an adaptation of established approaches developed for marketing resource allocation) and will show how these approaches can be married with delphi panels and decision trees to guide pricing decision-making.

Learning Objectives: After attending this session, attendees will learn:

• The complex market dynamics that come into play when two worlds collide - new innovation meets old innovation (bio-originals/bio-similars)
• The key factors that must be considered when determining the pricing decisions that maximize opportunity and minimize risk
• How methodologies such as “dynamic difference” can be married with delphi panels and decision trees to guide pricing decision-making