Webinars

Pharma organizations are undergoing changes where the focus is more on transformative medicines catering to unmet need and on smaller agile R&D cycles. Tackling a complex ecosystem of patients, providers, payers, and regulators among other entities requires organizations to strengthen their predictive modeling capabilities. Traditional predictive modeling can be complemented further through application of System Dynamics, Networking Modeling, and Simulation. These models enable future insights while utilizing historical behavior, provide the ability to incorporate real-world feedback instead of reliance only on linear cause-effect relationships, and account for a more exhaustive set of parameters that capture customer interactions.

Various industries are utilizing these System Dynamics and Network Modeling techniques to enhance models for product launch, market mix, and ROI optimization, among others. Pharma companies can benefit from these methods to improve their commercialization effectiveness across similar problem areas. One such instance is where we helped a pharmaceutical company leverage system dynamics to model the effect of a biosimilar launch on the market share. Being able to simulate the impact of a biosimilar launch made the organization more proactive and nimble in modifying strategies. In this webinar, we will look to answer two key questions:

• How have network modeling and system dynamics been applied by pharma and organizations across other industries to drive business value?
• How can these techniques help pharma commercialization teams tackle the uncertain and complex ecosystem?

Presenters:

• Sridhar Turaga, Head of Technology, Mu Sigma Inc.

The pharmaceutical industry today is evolving to develop patient experience as a core dimension when bringing new drugs to market. Shifting patient expectations combined with innovative technologies will have a dramatic impact on drugs and healthcare in the coming years. To cater to shifting trends, pharma companies are now turning towards patient data to power their decision making.

Real world data (RWD) accounts for 95% of the patient data, as opposed to the meagre 5% covered by clinical trials. Pharma companies are spending close to 20 Million USD annually on generating RWD-based insights. However, data fragmentation and non-standardized formats across RWD sources – coupled with incomplete and/or inaccurate data capture – raise concerns on the quality of RWD. In once such instance, the challenge was with low coverage of a key biomarker in one data source (<10%) while the coverage was better in another (>50%). We improved the coverage by experimenting with techniques such as Random Forest and Neural Networks to predict the values of the biomarker in the low-coverage dataset.

Parallelly, there is a boom in machine learning (ML) being used for data quality processes, which can aide stakeholders in overcoming the obstacles faced in the consumption of RWD. Various ML/DL algorithms can be implemented for the imputation of missing data, prediction of variables completely absent in a data source, and detect anomalies, thereby improving the completeness and accuracy of data. Effectiveness of the methods is measured through a combination of accuracy parameters, benchmarking against results from industry standard publications, and improvement in the number of potential studies. Through this webinar, we’ll be exploring:

• What are the challenges in using Real World Data for product commercialization?
• How can ML algorithms be leveraged to improve the quality of RWD sources?
• What are the RWD elements (such as biomarkers) that could enrich a study based on patient data?

Presenters:

• Bingcao Wu, M.S, Associate Director, Real-World Market Access Analytics, Janssen Scientific Affairs
• Siddhant Deshmukh, Engagement Manager, Mu Sigma

Diseases like NASH traditionally require an invasive liver biopsy to positively identify patients. When no therapies exist on the market, healthcare providers often hold off on performing a biopsy, resulting in a significant number of undiagnosed patients. By harnessing large clinical encounter data sets, we can begin to understand the patient populations at each stage and severity of disease, leading to better guidance of treatment development and delivery.

Presenters:

• Dr. Aswin Chandrakantan, Chief Medical Officer & SVP Corporate Development, Komodo Health

Pharma spends nearly $4 billion a year on direct-to-consumer (DTC) advertising with no signs of slowing down. Yet, many pharma companies still struggle to analyze and optimize linear TV and digital marketing because they don’t know which components most effectively drive patient behavior and impact sales. By linking TV and digital ad exposure to health data in a privacy-safe way, clients can customize metrics to measure real world health actions and ultimately ROI.

In this webinar, we will demonstrate closed-loop measurement success through a client’s experience analyzing a specific TV campaign’s reach to a qualified audience and the campaign’s success in prompting action (for example: visiting a doctor, scheduling a screening, getting a script for the brand, etc.). Attendees will learn from this real-world example how closed-loop measurement provides best practices for directly measuring an advertisement’s impact on the patient journey and collaborating with creative and media agencies.

Presenters:

• Greg Fry, Patient and Consumer Marketing Analytics, ZS Associates

In the last few years, many companies have started to implement AI/ML practices to elevate user experience. Common examples of AI/ML include movie recommendations on Netflix, self-drive cars from Google, and photo tagging face recognition on Facebook. Pharmaceutical companies are also exploring ways to leverage AI/ML to improve the efficiency of their Operations and Sales teams. For example, AI/ML techniques can be used to perform early-stage error detection during data ingestion processes, leading to significant time and cost reductions. Additionally, AI/ML techniques can be utilized to increase the efficiency and effectiveness of sales representatives by providing them suggestions on next best actions. Throughout this session, we will be covering use cases of advanced AI/ML techniques to help pharmaceutical companies answer three key questions when working on Commercial Operations projects:

• How can AI/ML techniques help improve incentive compensation execution?
• How can AI/ML assist the sales reps in determining the next best target to call on?
• How can AI/ML help with optimizing territory alignments?

Presenters:

• Tej Pandey, Lead, Commercial Excellence, Axtria;
• Ellie Houck, Consultant, Commercial Excellence, Axtria; and Landi Huang, Consultant, Commercial Excellence, Axtria

There is a lot of focus and investment in developing multiple analytical models each year by Insights and Analytics (I&A) teams. However, only a select few models are making the cut and being leveraged for translating strategy into actionable deployment. The objective of this webinar session is to provide an overview of various popular analytical models being leveraged across the Pharma Commercial Operations value chain. Additionally, the evolution/emerging industry trends will be addressed.

Presenters:

• Vineet Rathi, Principal, Axtria
• Erik Christianson, Director, Axtria

In order for pharmaceutical companies to achieve favorite formulary position, they are providing more discounts and rebates to payers. In addition, the key account management (KAM) team is also investing significant effort with HS/IDNs for influencing treatment guidelines. Manufacturers must consider the influence of payers and providers on treatment decisions and reconfigure their strategy and operations. The objective of this webinar session is to provide an overview of the HCP scoring and segmentation approach based on payer and IDN influence, and how HCP scoring can be leveraged to optimize call plans.

Presenters:

• Rakeshkumar Shingala, Associate Director, Axtria
• Anuj Sheoran, Senior Manager, Axtria

Rare diseases contribute a significant burden of illness due to the challenges associated with diagnosis. For the ~8,000 adult-onset or age-neutral rare diseases, patients can wait years between symptom onset and diagnosis leading to disease progression and poor outcomes. Biopharma companies struggle to efficiently engage and educate providers on rare diseases because most providers will see less than a handful of patients with a given rare disease over the course of decades of practice. Powered by large, provider identified clinical encounter data sets, machine learning models can now be used to compliantly “identify” providers who are managing patients with undiagnosed rare diseases.

Presenters:

• Aswin Chandrakantan, M.D., Head of Product, Chief Medical Officer, Komodo Health
• Neeraja Krishnaswamy-Bhagavatula, Alnylam

While many organizations are experimenting with machine learning in various corners of the organization, many are asking how to create something more robust. “How do we move from small, one-off experiments to something more repeatable and scalable?”. In this webinar, we’ll present a point-of-view on the critical elements and capabilities your organization will need to get there.

Presenters:

• Phil Daniels, VP, Data Ops and Analytics Platforms, Indegene

As the stakes get higher for pharma companies vying for a physician’s time, HCP preferences need to be closely monitored to justify their investments in the channel mix. The sustained demand for medical representatives over the years has forced many manufacturers to rethink the effectiveness of their digital channels and raise questions about the way forward. This webinar takes a look at how HCP behavior has changed over the last few years, what new parameters influence prescription and why pharma companies should relook at their website quality to support physicians.

Presenters:

• Mike Steward, Chief Analytics Officer, Indegene