2018 Poster Presentations

2018 Best Poster Presentation Award Recipient: Measuring Health System Patient Flow for Effective Targeting

Shubham Lahoti, Associate Director, Data Science, Axtria, Inc

The pharmaceutical marketing landscape has changed significantly by the emergence of several types of “health systems” (e.g., ACOs, IDNs, IHNs, etc.), and there are several physicians, clinics, and hospitals affiliated to these organizations. Most companies have well established methodologies to calculate the potential of individual accounts, but since the potential overlaps within different accounts, it is difficult to measure potential at a health system level.

With decisionmakers changing, it is becoming increasingly important to assign optimal targeting effort not only at the account level, but also at the health system level. Hence, for effective planning purposes a pharmaceutical company needs to understand patient potential at a system level.

Axtria recently developed a methodology that estimates the potential at the health system level by de-duplicating potential at accounts that are affiliated to the health system. This poster presentation will discuss in detail the methodology that was used to de-duplicate and convert account level potential to health system level potential (assuming account level potential is available).

Achieve Superior Segmentation and Targeting through Integrated Analytics of Real-World Behavioral Data and Market Research Attitudinal Data

Anjani Tripathi, Director, Advanced Analytics, IQVIA

A winning promotion strategy calls for effective segmentation and targeting in order to deliver the right message to the right audience at the right time. This underscores the importance of understanding customers from both attitudinal and behavioral aspects.

However, leveraging the available attitudinal and behavioral data to achieve an optimal segmentation solution is still a significant challenge. A common practice in the industry is to drive segmentation entirely on one data dimension, or at best, primarily on one dimension with partial guidance from the second, resulting in segments with suboptimal differentiation and actionability on the second data dimension.

As pharma brands strive for holistic promotion strategy, there is a great need to develop better methods to analyze both data in a truly integrated manner, to reliably identify the best segmentation drivers and produce optimal solution.

Here we provide an innovative and cost effective approach in which we integrate and analyze attitudinal data from available PMR/Tracking studies (Such as ATU, Demand etc.) and behavioral (including environmental) data comprehensively to obtain superior segmentation solutions. It leverages IQVIA’s vast real-world evidence data, including longitudinal patient, HCP, IDN and payer data, as well as attitudinal data from various primary market research studies. Machine learning methods and predictive modeling are applied to perform segmentation and target identification. The final segments show great differentiation on both data dimensions and uncover insightful behavior patterns and their attitudinal drivers. The results are highly actionable for segment membership assignment for the entire customer universe, target prioritization, resource allocation and message development, laying solid foundation for a successful promotion strategy.

Activating the Synergy and Impact of Cross-Channel DTC Campaign Promotions

Adam Dubrow, Advanced Analytics Services Director, Crossix

In this applied research project, Crossix has leveraged extensive connected data sources to measure the reach overlap, Rx attribution, and ROI of a multi-channel DTC campaign combining television, digital display, and print advertising. Crossix directly calculated the overlap in reach between these channels, and measured the new patient starts from each channel alone and via the various combinations of channels. This ultimately leads to an ROI by channel, both independently and jointly.

Case Study: Incremental Modeling Benefit for Predicting Consumer Diabetes Conditions

Duncan Clegg, PhD, Product Operations Manager, Crossix

This poster describes the methodology by which Crossix builds propensity models using the combination of health data and consumer data to predict the likelihood that a population will exhibit target health criteria, thereby presenting a solution to the problem of reaching healthcare audiences while remaining HIPAA-compliant and privacy safe. Its particular focus will be on the advantages found in using multiple transactional datasets to focus in on audiences who treat for a condition which has both prescription and over-the-counter therapeutic options.

Dynamic Targeting

Mani Sethi,Principal, ZS Associates

In a world where Google customizes ads based on our web activity in real-time and Amazon makes purchase suggestions in real-time, Pharma’s targeting approach seems archaic. Dynamic Targeting aims to create weekly plans that not only tell reps who to target each week but also why and how, by leveraging a wide variety of data sources, predictive analytics, and machine learning. The solution is tightly integrated with Veeva and provides HQ tight controls to ensure that the field execution is in line with brand strategies.

Key Benefits:
  • Go Beyond traditional inputs for targeting / call planning
  • Maximize opportunity via agility and timely insights
  • Simplify processes and communications for the field
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Emailing Your Customers: When Is It Too Much?

Olympia Mantsios, Associate Director - Health Analytics, Merkle and Catherine Mackin, Director - Strategy, Merkle

How do we know if we are serving our customers the right number of emails, and delivering the right message in the right order? Join us as we dive into some of the challenges marketers face in our rapidly evolving direct marketing ecosystem. Emails are getting lost in the clutter and customers are oversaturated with content, becoming annoyed, opting-out, and disengaging. We see that, while email programs are showing positive ROI, open rates are decreasing, inbox placement is spotty, and clicks are virtually non-existent. Merkle’s PMSA poster presentation will introduce an innovative new approach for your portfolio email strategy, which will ensure optimal content engagement, minimal opt-out, and positive lift.

Evolving Industry Best Practice in Demand Research

Carter L. Smith, Ph.D., Principal, BioVid and Tom Stoeckle, BioVid

In the Life Science industry, demand studies are regarded as one of the most risky types of market research to conduct. Prototypically customers’ estimates of future utilization are too high to be believable. In turn, the results of such studies are difficult to socialize and may even be dismissed outright. Despite the consistent problem of demand study invalidity, we find little evidence that the industry has systematically attempted to examine the ways in which varying the design of studies influences the quality of the demand estimates that are returned. This interactive poster provides a critical, meta-analytic and theoretical look at four aspects of demand design framed with four basic questions.
  • Do Demand Study Participants Think the We Expect Them To? Most demand study designs are predicated on the assumption that customers make relatively rational or optimal choices and evaluations of the products we test. This assumption warrants critical examination, and has implications for various aspects of study design.
  • Are Accurate Demand Estimates Reliably Attainable? As an industry, we have paid minimal attention to the demand exercises we use to capture our most important variables. Typically, we rely on simplistic dependent variables that are suboptimal because they consistently result in predictably overstated numbers that overstate peak utilization. We explore how alternate approaches influence results and predictive validity.
  • When it Comes to Conjoints, How Complex is Too Complex? We see little systematic examination of the assumptions that underlie widely-used experimental exercises used in demand studies (e.g., conjoint, DCM). The decision science literature strongly suggests that that people do not make choice in ways that are consistent with these design assumptions.
  • Do the Contents of the Product Profile Matter? Finally, we know from research in cognitive science that how you present information can be just as important as the information itself. We examine how different styles of TPP (Target Product Profile) presentation influence demand study results.
This poster explores all of these issues from the standpoint of modern decision theory and with extensive meta-analytic evidence of hundreds of prior demand studies. We present both summary rules for how different design decisions will systematically influence demand estimate quality, and (where possible) offer specific recommendations for improving demand study designs. Pros and cons of design tradeoffs are also examined so that researchers can make sensible decisions about specific features of their studies. Visitors to this poster will have the ability to watch deep-dive video summaries of all four topical areas, and should feel free to explore the entire content overview of to focus on topics most relevant to their concerns.

From Good to Great: Accelerating Sales Growth through "Surround Sound" Understanding of Customers

Yasasvi Popuri, Associate Principal, ZS

Pharmaceutical companies are often faced with the tricky problem of identifying avenues to accelerate sales growth for mature products. In our experience, efforts to address this problem are somewhat narrowly focused on one of two areas: first, understanding and addressing the provider, patient or payer barriers; or second, diving deeper into field force interaction effectiveness to improve customer experience. To unlock the true potential of mature products, pharmaceutical companies will need to bring in a “surround sound” perspective, drawing learnings from different customer stakeholders, the field force, and the headquarters. We also believe that an introspective approach where pharmaceutical companies first identify their good and great customers, and subsequently crystallize the reasons for differences in product adoption across customers, is a stepping stone for accelerated sales performance. Such an approach is invariably data-driven and requires harnessing the collective power of sales and claims analytics, voice of customer from primary research, and field force profiling of good and great customers. In this session, we will present our framework for the good-to-great approach to accelerate sales growth, and share a case study where we applied this framework to a mature product for the treatment of a respiratory disease.

The Future of Outcomes-Based Contracting with Commercial Healthcare Payers in the US

Dmitry Goldenberg, JD, Principal, DRG Consulting

Our goal was to provide an overview of current published perceptions of the future of outcomes-based contracts (OBCs) between payers and providers in healthcare management, which offer a method of cost-containment and differentiation in the market to payers, while allowing them to provide optimal care to patients/customers. Structured searches identified 11 publications/white papers/presentations on the topic of OBCs. The included sources describe key hurdles of OBC implementation (e.g. data collection) and discuss possible solutions (e.g. test cases). Unless the hurdles which impair the effective implementation of OBCs are addressed, they will not deliver on their promise and will remain a niche solution in healthcare.

Health System Affiliation Influence on Prescribing Behavior

Fred Trotter, CareSet and Ashish Patel, CareSet

Medicare Part A and B claims for 50+ million beneficiaries were used to map health systems at the market level by extracting HCP affiliations to both Health Systems and Medical Groups. CareSet then assessed the amount of control that health systems have on prescriber behavior according to 8 factors, including the rate of new treatment adoption, use of EHRs, employment, etc.

Once data tables and weighted-directed graph data is mapped into an RDB (relational database) and graph database, attributes are visualized to depict geographic partnerships and variables tied to local healthcare systems dynamics. To validate key features, ANOVA and other methods are used to test where group affiliation matters, the impact on physician behaviors and patient outcomes.

A key finding is a correlation measure associating group affiliation to the rate of new treatment adoption by the group’s members. The study provides several examples based on prescriptions for specialized drugs billed as medical benefits. In conclusion, these volumes of open data sets can shorten knowledge capture rate necessary to benefit sales, marketing, training, and HEOR teams. Furthermore, this method can identify where protocol-driven testing, such as RCTs, or outcomes-based contracts have better chances for efficient execution.

Measuring the Financial and Non-Financial Impact of Sales Contests Investments

Sameer Sardana, Sr. Director, Axtria, Inc.

Life Sciences companies spend millions of dollars on incentive compensation. Sales contests are a key part of incentive compensation and are used to motivate sales representatives during key market events, brand launches, expanding breadth, and depth of prescribing, etc. If managed properly, the sales contests can generate significant gains in terms of incremental sales revenue. Unlike non-personal channel investments, most companies don’t have a systematic mechanism of measuring the effectiveness of sales contests. A comprehensive assessment framework incorporates financial ROI, change in rep activity/behavior as well as measurement of field force motivation and sentiment. Key considerations account for strategic timing, long term vs short term impacts and controlling for other channels/market events. The ultimate objective of the assessment is to help brand and sales leadership make informed decisions about designing and rolling out future sales contests.

Optimized Sales Force Investment Through Targeting Innovation

Jorge Blum, Sr. Director Global Analytics, Pharming Pharmaceuticals; Paul R. Johnson, VP, and Principal, KMK Consulting, Inc.; Jing Yu, Manager, KMK Consulting, Inc.

Targeting refers to segmentation of HCPs/accounts in the context of the opportunity they represent for a given brand. This can range from a simple to a complex process and rely on secondary data as well as primary market research. In many cases, current targeting approaches used in pharma may miss significant opportunities to distinguish between ‘Prospects’ from ‘Suspects.’ As once expressed by Lester Wunderman (considered the “Father of Direct Marketing”), ‘Prospects’ are ready, willing, and able to buy whereas ‘Suspects’ are merely eligible to do so. In this presentation, we will focus on 3 main areas for optimal targeting and highlight 2 to 3 key elements of each. This will help us differentiate ‘Prospects’ from ‘Suspects,’ and lead to a significantly improved ROI in one of the largest portions of M&S spend.

R&D Embracing Data Science: Leveraging Global Secondary Data Sources to Enhance Early Clinical Development and Accelerate Trial Recruitment

Michel F Denarié, Sr. Principal, Data Scientist, Strategic Drug Development, IQVIA and Lucas Glass, Ph.D., Global Analytics Lead, IQVIA

R&D departments of pharmaceutical manufacturers and clinical research organizations have historically been lagging behind in the use of real word patient data to support clinical development. This is changing rapidly and multi-center trials now often rely on globally available patient, prescription, and hospital discharge data to select appropriate countries, identify sites, and accelerate trial recruitment. Moreover, these data sets are increasingly leveraged for early clinical development to refine target product profiles, clinical development plans, and study protocols.

Use of Diagnostic Data and Patient Data for Commercial Operations

Aneesh Gupta, Principal (Health Data Services), Symphony Health and Marsie Genetti, Principal (Commercial Effectiveness), Symphony Health

The development of a universal Patient identifier and integration of clinically rich data with traditional claims data now enables new insight generation and utility within the Life Sciences industry. The potential of this powerful, patient-centric data creates limitless opportunities to analyze markets and strategically deploy resources:
  1. Optimize ongoing clinical research and clinical trial
  2. Right-Size target patient populations
  3. Enhance patient journey and buying process development
  4. Drive disease/diagnostic education
  5. Optimize commercial strategy and execution
Problem Statement (Case Study): This case study focused on the 5th pillar: commercial execution of real-time triggers. When early treatment is paramount to treatment success, waiting for a confirmatory diagnosis claim to appear in longitudinal patient medical claims may be too late for intervention. In such cases, reaching a treating HCP while he/she is exploring possible diagnoses is critical. This is particularly impactful within rare diseases where these early signals (lab results) are still relatively infrequent (thus resulting in a very manageable / highly actionable volume of information). Two approaches were used:
  1. Utilizing a universal patient ID to integrate clinically rich lab results with traditional claims data to identify treating HCP.
  2. Coupling longitudinal claims data (possibly with confirmatory labs as well) with machine learning enables development of algorithms to identify “high likelihood” undiagnosed patients.