Account AffiliationsTarun Kumar, Associate Director, Business Intelligence and Analytics, Bayer; Mohammed Zubair, Engagement Manager, Mu Sigma; Ananda Subramaniam, Decision Scientist, Mu Sigma
Vast changes in the global healthcare landscape have transformed the way drugs are prescribed. The power of making decisions around treatment is moving away from individual physicians towards key decision makers within hospitals, group practices and integrated delivery networks (IDNs). These decision makers decide what drugs are prescribed and also have some influence on the drugs being placed on formularies. Purchasing decisions are seeing a shift from multiple local sites to singular centralized headquarter locations. Additionally, as high prescribing physicians relocate, it has become crucial to track their movement within or outside the network. Hence, pharmaceutical companies have to increase their sales and marketing focus on creating and managing quality relationships with these key decision makers (Key account management).
Identifying Clinical Trial Sites Using "New Age" Data SourcesKishan Kumar, Associate Director, Axtria
Clinical trials are the engine of pharmaceutical innovation, leading to the development of new therapies as well as extending the uses for existing ones. They are essential to a healthy pipeline ensuring the continued growth of the company. In oncology and other aggressive diseases, they also offer treatment to patients where no other option exists. Clinical trials are complicated and expensive to set up and run.
Typically, the design and management of a clinical trial falls to three broad groups; the medical teams, who design and lead trials; clinical operations teams who plan, execute and manage trials; and the medical science liaisons (MSLs) who maintain relationships with leading KOLs and principal investigators at major academic institutions and clinics.
Clinical trials involve the recruitment of patients at participating sites. A site is a hospital or clinic (academic or community), that agrees to participate in the clinical trial and commits to recruiting a certain number of patients. At a given site, the trial will be entrusted to the principal investigator, a physician who is responsible and accountable for conducting the clinical trial. The PI assumes full responsibility for the treatment and evaluation of human subjects, and for the integrity of the research data and results, although s/he may be supported by a team of sub-investigators.
At a high level, the process of setting up and completing a clinical trial is as below. Once the trial design and protocols are finalized, the MSLs and the clinical operations team will propose potential sites for the trial. This is called site identification. Following this, the clinical trial leads will shortlist the sites and backup sites to be included in the trial.
The cost of setting up each trial site is very high, and hence appropriate site selection is crucial. This presentation will focus on the “site identification” step that results in a set of high-priority sites for the trial leadership to choose from.
Messages Across Time: Consistent & Effective Personalized Messages Over TimeYalcin Baltali, Senior Manager, Commercial Decision Analytics, Pfizer
The Millennium century we live in now has a direct impact on the way pharmaceutical companies evolve their promotional strategies and build innovative touch points to reach consumers. Companies are taking advantage of new capabilities and adopting new promotional channels to expand their reach both physically and digitally. However, companies should also examine their traditional mindset to generate value through all channels by providing the right message at the right time. Moreover, they should evaluate and customize the context and timing of the messages. A holistic messaging approach not only offers robust efficient execution, but also more effectively satisfies customers’ needs.
Monte Carlo Simulation: Betting on the UnpredictableDan Stewart, Manager of Professional Services, Optymyze
Pharmaceutical companies spend billions of dollars on sales compensation plans, but plan modeling techniques continue to lag other industries in their sophistication and rigor. Across organizations of all sizes, the most common technique used is “back-testing”, a simplistic approach of running last year’s results through the new plan. Enhanced modeling uses backward looking data to generate assumptions, which can then be adjusted based on future expectations. This allows for scenario based modeling that “stress-tests” the plan under a range of possible scenarios, not just what happened last year. The primary methodology used was monte carlo simulation, which leverages iterations of simulated data in order to generate a robust set of findings.
This session will outline:
- Why prospective modeling will enhance plan predictability and calibration
- How to leverage monte carlo techniques as part of plan modeling
- Details on different distributions, other types of simulation approaches
- Advice on communication techniques to help communicate the expected outcomes and risks to stakeholders
Pharmaceutical Launch Sequence Optimization:Navigating International Reference Pricing to Reduce Global Price ErosionJeremiah Riddle, Analytical Industry Consultant, SAS Solutions on Demand (SSOD)
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 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. As the launch sequence progresses, referencing relationships between countries 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. Furthermore, once global price is lost it is lost for good, forever limiting a brand’s revenue potential.
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 optimal launch policies despite the significant financial, political, and reputational implications of pricing. Our experience shows that the industry profoundly needs more rigorous capabilities to compute, address, and implement solutions to this problem. We present a methodology, centered on Mixed Integer Linear Programming, which has been implemented in practice by SAS to address the challenge of determining the optimal launch sequence of pharmaceutical products in a global portfolio. We have found that by implementing this framework for launch sequencing, better launch decisions can be made resulting in reduced price erosion and more revenue generation for the rest of the product lifecycle.
Redefining Trigger Design Based on Machine Learning ModelingTim Hare and Ewa J. Kleczyk, PhD, Symphony Health Solutions
High value physicians are those that are associated with cohorts of patients who have key diagnostic and/or prescribing attributes. Patients that acquire one or more of these key attributes can then be mapped to their associated physician, who are in turn ‘triggered’ for a sales call. The likelihood that a patient will be prescribed a drug is often strongest when a combination of key attributes are present. The space for even a modest number of triggers can be large. For example, testing 10 triggers with 2 states (True/False) represents a search space of 2036 unique binary patterns, each of which needs to be enumerated and tested. Here we present an efficient machine learning approach based on TREE algorithms, to search this space for optimal trigger combinations by building supervised learning classification models, and then extracting the TREE node-specific information as database query rules for the best patient cohorts within each model. These rules can then be used to monitor and trigger physicians. While the search is not exhaustive, it can be conducted in a fraction of the time it takes to test all possible combinations, and the process becomes more efficient as the space grows.
Start by Choosing the Right DatabaseShunmugam Mohan, Bayser
Our primary goal as business analysts is to answer business questions. This is of paramount importance as our answers inform and shape a slew of questions which in turn spawn questions that our answers inform and shape. That's how we ultimately define the trajectory and destiny of our company, for better or for worse. Answering a business question starts with choosing the right database. Unfortunately, this first step is too frequently the first misstep. But it does not have to be this way. We'll present a 2-step approach that ensures we'll choose the right database for the job.
We should not celebrate just yet. There are two more hurdles to clear. They look deceptively simple but have tripped even the most seasoned of analysts. The first one is what we call the Principal-Agent problem and it has to do with the challenges of describing a task to an analyst in a way that the results of the analysis are just as good or even better than if we had performed the analysis ourselves. The second hurdle which we refer to as the Galileo's Dilemma arises when we have to present findings that fly in the face of the company's beliefs, whatever those beliefs may be. We'll provide insights to increase the odds of clearing these hurdles.
A Test & Learn® Approach to Commercial Decision-Making: Applying the Idea of Clinical Trials to Optimize Commercial StrategyScott Beauchamp, Vice President of Client Services, APT
With more data than ever, life sciences organizations continue to invest heavily in using this data to enhance their commercial innovation. Such investments are leading to data-driven insights about how they might be able to alter their sales force and marketing strategies: new sales roles, key account models (KAM), innovative non-personal promotion, multi-channel DTC campaigns, and more. These types of commercial initiatives have the potential to increase outcomes – however, many investments will not generate the desired results. To reduce the risk inherent in innovation and to understand how to optimally deploy resources, organizations are embracing rapid and statistically-robust business experiments to accurately determine the cause-and-effect impact of each commercial strategy before taking on the risk and expense of rollout, as well to understand the adjustments necessary to generate maximum ROI.
Use Look-Alike Models to Support Lead AcquisitionQizhi Wei, Vice President, Analytic Consulting Group, Epsilon
One of the challenges for many pharmaceutic companies is to reach potential customers who are not yet aware of their products. In this presentation, we will illustrate how we use look-alike models to identify the most valuable prospects and significantly increase our clients’ marketing reach by targeting these prospects. We will discuss the steps we take to build and implement the look-alike models, and show how we used the model to support online display campaigns to acquire leads into CRM programs.