Webinars

Pharmaceutical industry is rapidly adopting Machine learning and Advanced Analytics to enhance their commercial strategies. To make any successful marketing and customer engagement efforts, it is necessary to know your customer/physician you are targeting and predicting new prescriptions/ patient’s growth gives us an edge while planning brand strategy. The ML applied engagement model can help in segmenting the prescribers into Brand Champions, Loyalists, Churners which can further help the marketing and SFE teams to effectively formulate messaging tactics accordingly. Primary objective of this model is to devise strategies to retain market shares among churners and reach out to them to get them migrate to a business driving segment. In this Webinar, we will be discussing about machine learning approach to segment the prescribers into Churners, Loyalist and Champions. This information can be used for adjusting call plans & targeting exercise, field force communication plan, personalizing communication across various channels, used to drive next best engagement communication models, etc.

Ankit Kohli is Data Science Lead in the space of AI, Machine Learning and Big Data helping organizations across globe in enabling the application of Advanced analytics. With over a decade of his professional experience, he is the lead in data sciences at D Cube Analytics. Prior to this he has worked in data sciences business engagements at Absolutdata, EXL and Cognizant (MarketRX) across industries implementing analytical frameworks to business strategies to augment revenue streams for the businesses.

Dheeraj Kathuria is a Consultant at D Cube's India office, has 6+ years of Industry experience in Data Analytics. He has analytics and data science experience across various industries like Pharma, Retail, FMCG, Automobile and Digital OTT platforms.

Presenters:

• Ankit Kohli, Data Science Lead in the Space of AI, Machine Learning and Big Data
• Dheeraj Kathuria, Consultant at D Cube's India Office

Insights at the intersection of claims and integrated delivery network data can empower pharmaceutical and medical device companies to identify and prioritize high-value sales targets and opportunities. With many internal layers, connections, and networks invisible to outsiders, the modern Life Sciences universe has created new challenges for brand teams at pharmaceutical and medical device companies.

In recent years, Life Sciences organizations have operated in a challenging healthcare environment. Value-based care reimbursement models seek to improve outcomes while reducing costs; patient access to high-cost branded therapies is sometimes restricted; and acquisitions among hospitals and physician practices are now commonplace. One result of the altered operational landscape is a new model for care delivery: the Integrated Delivery Network (IDN).

Join Rebecca Hauck and Anja M. from LexisNexis® Health Care to learn how you can bring clarity to this complex universe by combining IDN provider and facility level data with medical claims volumes to help Life Sciences organizations determine the value of IDNs for a particular therapy area. These insights help transform challenges into opportunities by optimizing business processes, increasing market share, reducing coverage gaps, and improving revenue growth.

Presenters:

• Rebecca Hauck, LexisNexis Health Care
• Anja Maciagiewicz, LexisNexis Health Care

Anonymized Patient Level Data (APLD) has been gaining popularity in recent times in commercial analytics. Since it provides rich longitudinal patient history, it is routinely used to understand patient journey, payor dynamics, and define HCP targeting strategies.

However, this is only scratching the surface when it comes to realizing the full potential of the data asset. Patient Claims Data has many applications across various stages of the product lifecycle, right from pre-clinical stage to loss of exclusivity.

This data can be used to guide strategic decisions such as acquisition of pre-clinical assets and can aid in identifying geographic concentration of patients of interest for enrollment into clinical trials.

Anonymized Patient Data also offers what traditional HCP level Rx data is unable to offer – estimating the “Potential Opportunity” for your product among different providers. Machine Learning models that predict volume of patients in different stages of a disease across different time intervals are useful in targeting HCPs that have the larger volume of specific types of patients of interest. This enables precise targeting of HCPs for sales and marketing efforts, as well as customization of messaging based on prescription and referral patterns, particularly at launch.

When combined with call and promotional activity data, it can be used to measure campaign and message effectiveness, responsiveness to new communication channels, and to design near real-time tactics to gain and maintain market share. This is particularly critical in today’s times when sales reps are unable to meet with physicians in person due to the COVID-19 pandemic.

SRI has unique expertise in leveraging Anonymized Patient Data across all stages of the drug life cycle.

Speakers:/p>

• Sudhakar Mandapati, Principal, Strategic Research Insights, Inc.
• Harshad Chiddarwar, Director, Strategic Research Insights, Inc.
• Ren Zhao, Sr. Engagement Manager, Strategic Research Insights, Inc.
• Gina Nelson, Senior Director, New Product Planning, Gossamer Bio, Inc.

Hear from Sangeet Khullar, Director of Data Architecture at Daiichi Sankyo, as he walks through his experience on Data Quality, RDM, and MDM initiatives in Pharma, including:

• Reference data ( industry-standard dictionaries for data analysis and publications)
• Master Data – Consistency of Protocol Numbers
• Cost reduction through simplifying data processes
• Regulatory Mandates ( IDMP etc.)

The session will detail how Daiichi Sankyo uses the Ataccama platform to control the quality of their data and maintain strict, holistic data governance—thereby supporting and ensuring the success of drug development processes.

Presenters:

• Sangeet Khullar, Director of Data Architecture, Daiichi Sankyo

Classical approaches to market sizing, patient journey, patient finding, etc. all begin with a common assumption: that combinations of medical and Rx claims at the patient level can be deduced and combined to create a de-identified patient level cohort to anchor analysis. However, as the pharma landscape shifts from one dominated by primary care markets with high prevalence and a plethora of launch blueprints to draw upon, to one where diffuse specialty markets with low prevalence and a lack of analogs to anchor launch strategy, this assumption rarely holds true and creates significant commercialization challenges. This conundrum is particularly acute in Rare Diseases, where only about 500 of 7,000 have a diagnostic code in the International Classification of Diseases (ICD), 10th revision.

However, the combination of sponsored genetic testing, the democratization of de-identified patient level healthcare data, the rise of tokenization across entities in the healthcare eco-system, and pharma’s reluctant embrace of Machine Learning, finally enables the clinical promise of precision medicine to become an analytical reality. In this session, we provide an overview of tokenization and integration with RWE data, and share a case study anchored in patient outcomes, where the above obstacles were overcome to effectively facilitate diagnosis for 66 previously un-diagnosed patients.

Presenters:

• John Garcia, Alnylam Pharmaceuticals
• Jonathan Woodring, Executive Vice President and General Manager, IPM.ai

The coronavirus pandemic and responses in battling COVID-19 have created never-before-seen challenges for the pharma industry, such as public policy mandates (e.g., shelter-in-place orders, closing of non-essential businesses) and sweeping restrictions placed on industry representatives from entering offices and hospitals to see HCPs. These policy mandates and restrictions have generated an unprecedented adverse economic situation and caused companies to rethink their Go-To-Market strategy. The International Monetary Fund recently noted in their April 2020 World Economic Outlook, “The Great Lockdown. The world economy will experience the worst recession since the Great Depression.”

This webinar will conduct a strategic view on how the generation of economic effects from policy responses to COVID-19 are reflected through two critical processes: brand financial forecasting and marketing-mix analysis.

• Part 1: Discuss and measure how brand financial forecasts can be adversely affected by COVID-19. We will demonstrate the development of an urban-level econometrically estimated inferential new prescription brand model, specified with management control and economic variables. This model is then used to predict generated drag effects on brand financial forecasts from a COVID-19 recession. The prediction model will also show how changes in sales, marketing, and payer-related channels can be implemented and measured to mitigate brand financial drag effects.
• Part 2: Examine how a deep recession and continued industry representative access restrictions to see HCPs affect the optimal mix of commercialization activities. Pharma sales and marketing is evolving due to COVID-19, such as changes in sales rep access restrictions at physician offices and hospitals, greater use of digital channels to compensate for this decline in access to continue sales rep/HCP engagement, use of digital channels by HCPs as a means to acquire product information, potential changes in DTC spending to reflect patients decreasing their visits to their doctor, and a greater use of samples, copay cards, and other support programs to meet patient affordability and loss of insurance issues caused by the recession. Subject to caveats that pretty much everything is a bit uncertain (this is the first global pandemic for most of the living population), these changes will be analyzed by how they affect marketing mix optimization, scenario planning for budget allocation, and the measurement of budget trade-offs.

Presenters:

• George Chressanthis, PhD, Principal Scientist, Axtria
• Vipul Pandey, MBA, Director, Decision Science, Axtria
• David Wood, PhD, Senior Principal, Axtria

Using traditional methods, defining, analyzing, comparing, and finalizing a cohort of patients for HEOR and RWE analysis can often take months, impacting the ability to answer questions. Today, advanced technology, including AI and machine learning, enables a user to define and instantly generate a cohort including descriptive statistics such as age/gender distributions, patient counts, record counts, test, diagnosis, and Rx volumes, geographic distributions, and more. The ability to quickly adjust cohort definitions in a user-friendly format can also reduce the time it takes to understand results and their impact on the given analysis.

In this webinar, you’ll understand how advanced platform technologies can shorten the time it takes to create cohort definitions and iterate on cohort variables with output formats that meet unique business needs — such as charts, graphs, box plots, Sankey plots, and more. We’ll also look at:

• Patient journey discovery
• Real time cohort comparison
• Provider relationship graphs

Presenters:

• Jason Bhan, MD, Co-founder and Chief Medical Officer, Prognos Health
• Kristian Kaufmann, PhD, Principal Data Scientist, Prognos Health

When an unforeseen pandemic like COVID-19 causes catastrophic disruption to the global healthcare system, we can look at more innovative analytical approaches and technology to help guide brand’s planning and forecasting.

As the country reopens at different paces regionally, analyzing promotional mix in real time and against historical activities will be critical.

This webinar will share how to optimize promotional investments in this dynamic market using data to inform and predict factors that will impact brand sales. This includes identifying the optimal time, channel, and sequence at the brand, portfolio and regional level.

In this webinar, we will explore the impact of the pandemic on promotional execution strategy, focusing on success stories of how organizations are pivoting with agility to meet the challenges of the COVID-impacted environment; specifically, the following areas:

• Optimizing promotional investment to account for loss /reduced personal promotions and its impact on brand forecast
• Customizing channel sequence at segment and/or individual customer level

Presenters:

• Paulomi Patel, Principal, Analytical Wizards
• Mike Steward, Principal and Chief Digital Officer, Analytical Wizards

The COVID-19 crisis has upended 2020 commercial priorities and budgets, leaving many pharma organizations searching for a customer engagement strategy that can operate in the New Normal. Pharma organizations need to succeed with a new commercial model by improving planning, coordination, and resource optimization in a way that ensures sales, marketing, and customer excellence are truly recalibrated to the current situation and contribute significantly to profits. While sales rep enablement for remote or virtual meetings is important, winning in the New Normal would require deep content, technology, data, and analytics capabilities across personal and digital interactions.

In this webinar, we will be discussing about how the pharma sales and marketing landscape is evolving and the critical strategic planning and executional capabilities required for next-gen commercial operations. We will also share a new unified approach towards driving sales and marketing analytics consisting of the following key components: Customer Strategy, Channel and Content Strategy, Unified Marketing Mix, Digital Call Planning and Sequencing, Deployment and Execution, and Measurement Planning.

Presenters:

• Krishnan Raman, Director, Indegene
• Debasish Das, Sr. Manager, Indegene
• Tarun Shukla, Engagement Manager, Indegene

To make any successful forecasting model, the first step is to identify the key supporting metrics required to build an accurate forecast model. The conventional data sources (e.g. chart audit, survey data) present only a small part of the very big picture, where a lot of indirect factors affect the share of the drug, thus limiting the insights and scope of the analysis. Real-world data can be the supplementary evidence, if not the substitute for conventional data sources, in increasing the confidence of forecasting inputs.

In this webinar, we will be discussing how to efficiently merge information from both real-world data and secondary research data and build robust KPIs which can support development of an efficient forecasting model.

Presenters:

• Vishnu Prashanth, Principal Consultant, D Cube Analytics
• Ajitha Surendran, Consultant, D Cube Analytics