Pharmaceutical Management Science Association

Today, we are experiencing an epidemic of "infobesity" -- we are exposed to too much information, which impacts our ability to think critically, process facts and make effective decisions -- also known as "cognitive load."

Our audiences also experience cognitive load when we present them with too much information, unnecessary detail, content that is not tailored for them, or we "deck them" with endless slides covered with copious amounts of text. The definition of genius is making the complicated simple. In BRODY Professional Development’s “Scientifically Speaking” workshop, we will show you how to create clear, compelling presentations, even with the most technically dense and detailed information.

Amy Glass, Director of Training at BRODY, can help you create messages that resonate, tell stories which are memorable, make your visuals more visual (data visualization), and present complex information in a confident, engaging way. This program will enable you to successfully sell your ideas, projects, or products with ease -- while minimizing unnecessary details and tech speak. Come to the Scientifically Speaking workshop and learn the ART and SCIENCE of scientific speaking!

Learning Outcomes After attending this program, participants will be able to:

• Assess the technical acumen of an audience to develop optimally effective presentation strategies
• Present technical information in understandable terms with clarity and impact
• Educate and engage their audience through visual, vocal, and verbal signals
• Leverage powerful visuals to assist communication of complex ideas
• Achieve increased comprehension and retention

Non Personal Promotion Planning
Pharmaceutical companies need to fundamentally re-think how they interact with prescribers as the “Reach and Frequency” used for personal promotion is becoming increasingly limited as prescribers spend less and less time with medical reps. Some of the market trends reveal that reps are making fewer calls, reps are not getting detail time, or they are leaving without detailing. This is why non-personal promotion becomes more and more important.

By understanding the segment of physicians specially, those who are not open to personal promotion, and discovering their characteristics, effective non-personal promotion such as email campaigns or paid searches can be customized.

This presentation with discuss one approach that a team of consultants from Deloitte Consulting took to utilize internal and external sources of data to uncover physician characteristics and inform non-personal promotion planning.

Life Style Based Analytics
Life Style Based Analytics (LBA) models use non-traditonal data (3rd party marketing) to predict the health risks of a population where claims data is otherwise unknown or unavailable. This method is used by insurance companies to explore total claims as well as specific diseases and conditions.

In this tutorial, the Deloitte team will present the types of external information that can be used to profile the population based on their life style based data. Finally they will recommend approaches that this method can be used in Pharmaceutical and Medical Device marketing projects for designing more effective patient management programs as well as predicting the patient behavior which leads to better outcomes.

Accurate Prediction of High Cost Insurance Claims
In this part of the presentation the Deloitte team will discuss a case study from the Auto Insurance industry. The intent is to show that by using more data sources outside of what was used in conventional models, higher prediction lift and cost management can be achieved. There will be a follow-up discussion on how to apply such techniques to Pharmaceutical and Medical Device marketing projects.

Industry research shows that a small number of claims drive a high percentage of insurance claim costs. For example, in Healthcare, 5% of the population spends 50% of the total amount and in Auto Insurance, 10% of the claims account for more than 50% of claim payment. Early intervention on high risk claims can reduce the cost of high risk claims by 50%.

Advances in data mining technology allow models to handle more predictors, even when the predictors are strongly correlated. New techniques and large data samples guarantee higher performance of complex models without over-fitting.

Sarah Nguyen is a Manager in the Advanced Analytics and Modeling Practice where she focuses on marketing and sales analytics, payer analytics, and network analysis in Life Sciences. Sarah has over 15 years of experience in advanced analytics spanning a variety of industries, including hi-tech/electronics, wireless communications, and pharmaceuticals.

Sarah’s areas of expertise include advanced analytics and forecasting in commercial operations. While in industry, Sarah drove the implementation of evidence-based and data-driven methodologies to guide the decision making process throughout the commercial organization. She successfully leveraged advanced analytics to influence brand planning and strategy development, new business initiatives, and managed markets contracting strategy.

Sarah has Bachelor’s Degrees in Economics and Mathematics from New Mexico State University, and a Master’s Degree in Economics from New Mexico State University.

Amin Torabkhani is a senior consultant in Deloitte life sciences practice. He has over 7 years of experience in life sciences strategy consulting supporting brand teams in decision making regarding complex dynamics of patients, physicians and payers. He has deep expertise in patient journey analysis, life sciences data mining and predictive modeling and analysis in more than 10 therapy areas. He is experienced in brand planning, forecasting and scenario analysis for both inline and new products. Amin has a masters degree in Industrial Engineering from Northeastern University in Boston and he studied system dynamic and simulation at the Sloan School of Management at MIT. Currently, he is managing the implementation of a global commercial analytics platform, Athena, for the renal franchise.

The pharmaceutical business has always offered a highly dynamic and challenging environment within which to work and build a rewarding career. Although this aspect of our industry will not change, the stakes have never been higher. In today’s world, developing and delivering innovative therapies to patients in need requires companies to devote up to 15 years of concentrated effort and well over $1 billion dollars in capital investment. With the advent of healthcare reform, pipeline challenges and tighter margins, the need for robust, reliable and timely analytic support has never been greater.

Every senior leadership team in every pharmaceutical company across the country is looking to their analytics professionals to help them turn “information” into “knowledge.” Data – big or small – is not the answer. “Crunching numbers” or deploying complex CRM systems to gather more information is not the answer. The “answers” come from the individuals and teams who possess the skills and the drive to truly understand what the data means, and to turn that understanding into knowledge that informs decisions that benefit the business. In today’s talk, Greg Barrett will focus on what leadership is looking for when they ask their analytics teams to help turn information into knowledge.

Brand teams make sizeable investments in acquiring data assets and commissioning resources to perform analytics. However, when a business question comes up, typically substantial time is spent in collecting, processing and integrating data. In most cases this leads to analysis not being ready in time or lacking the level of comprehension and analytic rigor needed to solve the problem.

Wouldn’t it be great if the analysis was ready in a proactive way to support decision making at the time the question is asked? By taking a proactive approach to analytics, the focus can be on deriving key insights and supporting better decision making, instead of spending time setting up and conducting the analyses.

This presentation will highlight how brand teams can strengthen their overall analytic readiness while achieving greater efficiency and insightfulness. Case examples will demonstrate how the framework has helped the brand team answer several key questions spanning broad topics, level of complexity and urgency.

We will conclude the presentation by offering some practical tips for management science professionals to facilitate the creation of agile systems to strengthen the brand teams they support.

Disruptive Innovation will be happening in healthcare in the next decade impacting access, quality and cost of healthcare. The innovation in products and services, care delivery models, consumer engagement programs will be driven by insights from big data. The future financial performance of the pharmaceutical companies depends on how pharmaceutical companies harness and leverage data in the patient outcome driven accountable care model. In this presentation the factors that are driving the growth of data in healthcare will be explored. Why this growth has created more the 200 companies in the last three years some with disruptive business models and what white spaces have created these companies? What does this mean to the future of pharma in the complex, uncertain and transformative healthcare system? Using design thinking approach and ongoing interviews with senior leadership the future of pharma, readiness of pharma and the challenges in creating and managing big data will be presented. A futuristic discussion on how the data can transform the current drug discovery to commercialization will be presented.

EMR adoption has increased dramatically across the US in recent years, in part fueled by the government’s Medicare and Medicaid EHR incentive programs to achieve meaningful use. EMR data contains incredibly rich patient information with key advantages over other secondary data sources and has the potential to provide pharmaceutical & biotechnology companies with a powerful data source from which they can glean key insights to understand their markets and businesses. However, manufacturers currently face several key challenges in utilizing EMR data sets, challenges that stem from hospitals and community practices implementing EMR systems from a wide variety of EMR vendors, resulting in capture limitations, inconsistent data fields, and imperfect data standardization.

This session will provide an overview of an ongoing collaborative initiative between ZS Associates and the PMSA that aims to assess the current EMR landscape, identify EMR data needs of manufacturers, evaluate current EMR data quality and usefulness (from a manufacturer standpoint), and ultimately provide recommendations to both manufacturers and EMR vendors regarding how each side can help shape the future of EMR data. Questions this research will aim to address include: To what extent have manufacturers utilized EMR data to-date, and what specific challenges have they faced along the way? What would manufacturers ideally like to see from EMR data moving forward? Who are the leading EMR vendors today? What best practices exist in collecting and providing EMR data to manufacturers, and where are those best practices being implemented? Where can improvements be made to EMR vendor systems and processes in order to increase the value of their data sets for manufacturers?

Key findings and insights from manufacturer and EMR vendor interviews to-date will be shared, with the hopes that some of you in the audience will be interested in participating in the rest of this exciting research moving forward!

Unstructured data – data found in Electronic Medical Records (EMRs) in the form of clinician notes – has traditionally been difficult to access much less quantify. Time-consuming chart reviews have been somewhat informational but labor intensive and based on small numbers. Advances in Natural Language Processing (NLP) – and the technology enabling it – have changed the rules of the game, offering deep insights into millions of patient records at faster speeds and adding never-before-seen color to the world of pharmaceutical marketing.

To succeed in a healthcare world increasingly driven by information, life sciences firms must use the entire palette of available information, including the deep color provided by physician notes. EMRs continue to evolve and become more sophisticated, but many critical components of patient care are still not found in structured data (i.e. pre-defined input fields of the EMR). The physician assessment itself – the very basis of medical care and prescribing decisions – is monochromatic without the notes. In the old black and white world, we see a diagnosis but not the chief patient complaint and complicating factors that prompted the office visit in the first place. In the old black and white world, we see a medication switch but not the physician rationale driving the medication change. In the old black and white world, it is far too easy to draw inaccurate conclusions without the light of notes.

This presentation will explore how NLP data obtained during clinical encounters and recorded in unstructured data fields can lead to insights that drive the thinking, messaging and targeting of brand strategy – in a more efficient manner than chart reviews. We will highlight key content areas from one patient note to show how, when aggregating across an entire patient population, notes can provide the color to empower better decisions.

This study provides an overview of the publicly & commercially available anonymous patient-level data sources that could be used in forecasting. It discusses their ranges of applicability, projectability, and their applications in different forecasting situation.

Depending on the patient-level data source, different statistical and analytical techniques could be applied to analyze this data for the forecasting. Selection of the analytical techniques is also strongly dependent on the intrinsic properties of the data. Some analytical techniques could be used to analyze particular data source for forecasting, other techniques could be invalid and even dangerous. This review also discusses publicly available international databases, and compares & contrasts them with the US bases patient-level data sources.

E. Loren Buhle, Jr. PhD, is a Life Sciences Executive with over 25 years in Life Sciences in roles ranging from scientific & clinical research, business management, and information systems. He spent 10 years as a medical researcher and practicing oncologist at the University of Pennsylvania School of Medicine (and ASCO member), followed by several years as a Smithkline Beecham executive in research and development. In the late 1990s, he moved into consulting at (PwC)IBM, working with global pharma, biopharma and medical device clients. His expertise ranges from infrastructure through corporate strategy, with two approved drugs and one approved medical device. He is closely associated with the Watson work on oncology with the Memorial Sloan Kettering Cancer Institute. He is a graduate of The Johns Hopkins School of Medicine.

1994 was a simpler time, a time when a young graduate with some operations research training could dream of deploying the Simplex Method to solve the problems of the day, a time when only futurists imagined digital marketing analytics, a time when physician-level prescribing data was still novel, and a time when people were just beginning to automate spreadsheets with Excel 5.0’s VBA. It was also the year I joined the industry.

The last twenty years have ushered in evolutionary & revolutionary changes to our industry, both changes that are almost imperceptible as they occur and ‘step-function’ changes that restructure core pieces of our industry, the work we do and the way we work. Through too many projects to count and too many air miles to want to remember, I have, I believe, learned some hard-won lessons, as well as ideas on where we can all still improve. This talk includes twenty lessons roughly split into answering these five questions:

• How do you build a great team?
• What makes a project tick, click and rock?
• How can we speak analytics to non-analysts?
• What can each of us do to grow our careers?
• Where do we go from here?

Although the healthcare industry has changed dramatically in recent years, the primary methodologies utilized to target physicians have largely remained stagnant. One recent marketplace dynamic that in particular has implications for targeting prescribers is the Affordable Care Act’s Health Insurance Exchanges, which have begun and will continue to impact growth in patient populations state to state, patient makeup of physician practices, drug coverage state to state, and much more. However, traditional targeting methods have significant limitations in addressing these implications, limitations that demonstrate the importance of utilizing an innovative targeting methodology that does not simply take into account these marketplace dynamics, but helps pharmaceutical and biotech companies leverage and consequently benefit from them.

To that end, The Marketing Advantage, Inc. refined our Dynamic Targeting™ methodology, a powerful system that profiles prescribers using over 130 rich behavioral metrics that shed light on the prescriber’s loyalty to competitive products, our product’s formulary status in the Managed Care plan under which a competitive product was prescribed, and much more. These behavioral metrics are a superior indicator of a physician’s performance than are standard metrics and, accordingly, result in more focused and productive sales efforts.

The pharmaceutical industry lags behind other industry sectors in the sophistication of certain of its commercial environments. This is especially true in the critical function and process of Forecasting which, in life sciences, is often disjointed, inconsistent and ineffective, leading to the suboptimal generation and distribution of business-critical knowledge. This presentation will address the issue of best-practices for business analytics as it relates to forecasting in general and how this impacts overall strategic planning in the industry.

In an industry with exponentially more data available, it’s easy to fall into the pattern of using as many measures as possible to evaluate performance. This big data trend is easy to follow in the pharma industry as the electronic capture of more and more data sources provide more refined and reliable data at the territory and provider level, now including even patient data. In developing incentive compensation plans, a key goal is to develop an effective measurement of sales force performance while motivating the field through incentives.

To effectively assess and reward performance, sales force leadership is adding more variables within performance measurement models, but are these complex models effective in measuring performance? Does the inclusion of more measurements provide a more accurate and fair assessment of performance? Do certain incentive compensation plan designs lend themselves to more data? Is there a challenge in field force confidence in emerging metrics, such as patient data-based metrics which cause the inclusion of these metrics to be additive rather than a replacement of previously-used metrics? If so, how can this be managed? What do representatives in the field think about these more complicated models? Does the increased complexity provide comfort or confusion?

Big Data Analytics is the hottest new trend in the pharmaceutical industry. The presentation addresses how the advent of Big Data will affect analysis approaches and generation of insights. The emergence of EMR data and expansion of Claims data presents a unique set of opportunities to generate actionable business intelligence. In particular, the historical focus in data analytics has been on addressing the “whats” whereas the new data sources open doors to gain a richer understanding of the “whys”. This is a subtle but critical paradigm shift with game changing long term implications for what data we use, how we use it, and the role of data analytics teams.

This presentation will cover innovative Big Data analytics, powerful visualizations, and scope for addressing business questions that were previously relegated to the realm of primary market research. The presentation will also address the caveats associated with these nascent data assets and the need for a thorough understanding of the data infrastructure.

The linkage between Incentives and Call Planning continues to be critical in the US Pharmaceutical industry. Overall, as an industry, many companies continue to strive on aggressive Call Plan Compliance (“CPC”) attainments, and CPC metrics continue to form a key part of field force effectiveness discussions. These companies are either mandated by global headquarter offices to strive and achieve almost ~ 80% CPC, or are influenced by history that pegs 80% CPC as an industry benchmark. However, field behavior is more driven by IC attainments, and in many cases there are disconnects observed between Call Plan Compliance and IC attainments. Reps would like to discover that higher CPC would lead to higher attainments, but this is not always the case. A rep may have high CPC but may only achieve 94% attainment while another rep might have a lower CPC with 110% attainment. Inherent disconnects between call plan models and IC models is often the root cause of this issue, and leads to distrust in call plans provided by the Home Office.

In this presentation, we discuss:

• Fractures in the process that causes this break in relationship to emerge
• What can we do in IC designs to account for a better linkage to call planning process. This would also include discussion on modeling approaches need to be aligned to achieve better linkage
• Finally, we will close with a discussion on cycle times and IC administration impact that our suggested improvements may cause

Launching a successful new pharmaceutical product is more challenging than ever. With declining approvals, payer pressure, fragmentation and competition, new product uptake and sales are down. It’s critical to focus on high potential assets, and maximize those opportunities. Through trend analysis of the Nielsen BASES new product database of 2000+ product profile evaluations spanning 75 therapeutic categories, and close examination of more than 50 product launches or approval submissions, Nielsen examines the impact of key drivers such as physician uptake, and market development on success. We then show how to use market research results to identify strong launch candidates and optimize commercialization.

Does a strong physician value proposition predict in-market success? What role does differentiation play? How do trends vary by category and disease severity? What is the role of a fragmented category vs. open territory? What about 1st to market vs. successors? Analyzed physician metrics include prescribing interest, adoption urgency, prescribing frequency, product differentiation, benefits vs. need, fulfilment of unmet needs and side effect burden.

Nielsen’s analysis will culminate with a category-specific Success Index framework that helps manufacturers set expectations, manage pipeline development, and focus efforts on assets that are most likely to pay out.

Over time, the pharmaceutical industry has considered various measures outside of territory level sales for motivating field force performance. In competitive environments, motivating collaboration and teamwork may prove challenging. To that end, a component of team performance has been included in many incentive compensation plans. Do these team components work? Do they effectively motivate collaboration? Most importantly, do they have an impact on an organization’s bottom line?

Using both subjective and objective data, including sales data and field surveys, we will provide the pros and cons of using plans that contain team components. We will examine various types of team components and discuss the benefits and hazards of each. Using plan results, we will compare the growth in plans with and without team measures for similar sales forces. We will utilize benchmark data from several sales forces for the analysis and illustrate key takeaways by focusing specifically on a case study of one organization’s utilization of team performance metrics.

Current patient assistance programs are designed to attract the maximum amount of patients and enable the maximum amount of utilization by those patients. The vast majority of those program designs, however, neglect to observe true consumer behavior over the course of that program’s implementation and rarely revisit program design over time, leading to overspending and a decline in potential profitability, even though revenue may remain the same.

By examining patient behavior over time, especially price sensitivity to out of pocket prescription costs and persistence of therapy, it is possible to design an assistance program with maximum efficiency which attracts and retains profitable patients while simultaneously being as cost conscious as possible, thus generating the maximum ROI.

CRM is not just another buzz word in the established arsenal of pharmaceutical communication tactics. It is a broad-based strategy grounded in proven impact across industries, and the next critical stage in the evolution of HCP and Patient engagement.

Prescription drugs and OTC medication are two traditional channels that align with the evolution of CRM. On the surface, a natural extension of this approach should be vaccines. While on the surface this appears intuitive, there are significant hurdles associated with CRM and vaccine communication. Despite this, if you rise the challenge, think about the opportunity non-traditionally and address the data gaps, the rewards will be worth the effort.

Join us as we take a look at a combination of common - sense and non-conventional approaches to building out vaccine targeting, segmentation and CRM strategies that work.

Every pharma organization aspires to identify and reach influential customers. However, the concept of “influence” has been difficult to articulate and challenging to quantify, since treatment decisions involve complex interactions between patients, physicians, and institutional stakeholders such as payers and provider networks. Using techniques derived from social network analysis, which deals with the complexities of mapping and measuring stakeholder influence, we have created a novel framework for combining multiple pharma-specific data sources and applying ranking algorithms to measure different types of influence at the customer level.

Specialist-driven markets such as oncology and rare diseases are especially appropriate for this type of analysis since treatment decisions in these markets involve a significant amount of peer-to-peer interactions, local expertise, and patient referrals. The insights gained from this analysis can be used to reshape traditional approaches to developing product strategy and engaging with customers.

This session will cover:

• Three notions of customer influence that are relevant for pharmaceutical sales and marketing: authority, credibility, and the prescribing environment
• Techniques for mapping, measuring, and visualizing influence within specialty markets
• Recent project examples that illustrate the social network approach and its applications to specialty markets.

Call Planning has been well integrated into the planning and execution of the pharmaceutical sales forces in the last several years. Call Planning is a great tactical step to tradeoff brand, sales and operational goals and help implement the best strategy for the organization as a whole. Several companies have integrated measuring call plan adherence and tasking their reps to follow the call plan. There also have been studies that try to correlate call plan adherence to rep performance. On the flip side, we know that the data is not perfect on several dimensions at a micro level - Data vendor captures a percentage of the actual data (& rest is projected), doctors treating & the doctor (pad) writing the Rx could be different, reps report what is asked of them (self-fulfilling prophecy). Given both sides of the coin, some fundamental questions still remain. How valuable are call plans to the field reps? How would the reps perform if we did not provide any call plans (compared to those who have a call plan)? Do we facilitate the reps to build their own call plans? What type of products and reps may need call plans? What if we did not measure any adherence (would we get more accurate call activity data)? We believe that Pharma companies, based on their internal cultures, could set up controlled experiments to answer these questions. Based on a real experiment, this talk will explore the approaches to answering these questions:

• We will present the operational challenges in setting up such experiments?
• We will present the results of the experiment at a couple of levels?
• How did the reps go about their activity?
• How did their activity measure up against the optimal?
• What were the results?

Physician valuation measures employed by pharmaceutical companies focus on past and current prescription volumes, and the subsequent promotional strategies are aimed at maximizing returns in the short term rather than building long term relationships. As the landscape of the industry undergoes constant changes, the focus is shifting towards creating targeting strategies with a future outlook.

In this discussion, we focus on customer life time value, a concept common to industries like banking and telecommunications. We will illustrate how the use of statistical models can be utilized to predict the net present value of physicians’ future prescribing. Physician life time value could create deep customer insight into the strategies and personalized interactions.

More specifically:

1. Understand the interconnected factors that influence customer lifetime value.
2. Improve the profitability of every customer interaction.
3. Boost profits and reduce costs by targeting customers with the highest lifetime value potential.