Webinars

AI-ML can be used across various use cases in Bio Pharma. While popularly known to be used for identifying and accelerating new drug discovery & clinical applications especially in precision medicine, there is relevant and powerful application within the commercial setting as well to enable the right patients access to the right treatment at the right time.

In this presentation, Definitive Healthcare will showcase use cases across functions.

• A primary care drug hone & target to optimize a narrow strategic opportunity successfully (T2d)
• An expensive first in class statin with a niche indication find appropriate patients & broaden access
• Enabled a Biotech to get to market faster via accelerated enrollment
• Optimized patient compliance and reduced Patient Support Program spend via targeted interventions thru AI-ML predictive risk scoring of patients for non-compliance along with AI to determine top reason for non-compliance enabling right intervention at right time for right patient within Patient Hub

Real world data is increasingly being drawn upon by the Life Science industry to inform a broad range of business functions throughout the product life cycle, including clinical trial support, market insight, comparative effectiveness, understanding existing care standards and much more. And despite the richness of insight available through E.H.R and claims-based data assets, we sometimes encounter concerns from end users about the data quality, and its ability to support the analytic goals of an organization. Oftentimes, such complaints are the result of misconceptions around what RWD reflects vs. their desired analytic purpose.

This discussion will provide a useful snapshot into some common pitfalls we encounter when it comes to data interpretation, use and expectations – along with recommendations as to how they can be managed/avoided.

Legacy self-service analytics require extensive data model training and deep technical knowledge to be used effectively. The alternative is developer-driven dashboards that have too broad a scope and are not unique to each user request, territory, or country. Every new data insight requires the creation of a unique dashboard, custom-built to address the specific needs of each request. These dashboards have long development cycles and can only answer the specific analytics questions for which they are built. With existing solutions it is difficult for business teams — especially non-technical users in sales and management positions — to use reporting and analytics to its full capacity.

These problems hurt an enterprise pharma’s competitive advantage in the industry.

Join us in this interactive presentation session to hear from industry stalwarts how life sciences-trained, AI-powered analytics enables accelerated decision-making to drive commercial success.

Key Discussion Points:

• The historical role of commercial insights and the exponential evolution of data and insights and what it means for commercial operations
• Key capabilities to look for when reimagining commercial insights and the role of AI, ML, and NLQ
• The business impact of democratizing access to insights to help business users make smarter, faster decisions at significantly lower cost

A new era is dawning in analytics, but organizations must be thoughtful in what augmented analytics technologies they incorporate to achieve the holy grail – a one-stop-shop where users can connect to all data, easily share all the insights across the business and do this for real-time and batch use cases. In this live webinar, hear from two leading data and analytics technology experts as they discuss:

• What do the next generation business insights solutions look like and how augmented analytics technologies fit into this vision
• How this solution can be commercial ready and coexist in your current ecosystem
• Where people and process come into the picture and considerations from an organization's perspective
• The challenge of bringing this all together so business users and analysts will use its full potential

Provider affiliations, between practitioners and facilities and facilities and IDNs, have been a foundational element of pharma analytics. Yet, building accurate provider affiliations is hard. One reason is scale. With millions of links across HCPs (healthcare professionals) and healthcare organizations (HCOs), manual or survey-based methods are limiting.

In this webinar, Compile, Inc. will describe a novel approach to constructing affiliations. Using a combination of graph networks and data triangulation, the drawbacks of individual data sources are mitigated. Further, participants will learn how graph structures are well suited in capturing the complex hierarchy that characterizes health systems today. Finally, the webinar will explain how this new affiliation structure, with quantitatively scored links, can help in common pharma use cases from sales attribution to account-based targeting. The webinar will be targeted toward data and analytics savvy audience.

The landscape in which pharma companies have been operating was changing even prior to the COVID-19 pandemic. The pandemic accelerated changes that were already happening.

• Industry’s access to healthcare professionals (HCPs) is diminishing
• Promotional materials need to be more personalized for the HCP to engage with them
• Broader changes in the healthcare market (centralization of healthcare organizations, broader restrictions on healthcare expenditures, regulations such as the Sunshine Act, etc) have diminished demand for reps
• Today’s HCPs demand more choice in both the message, frequency, and the medium

While untangling all these trends is a difficult task, Veeva has created a framework that is flexible enough to encompass the data available on many of these trends. In addition, this model takes into account that there is heterogeneity in the marketing approaches of different companies, and in how effective they have been in adopting each channel.

Pharmaceutical companies have volumes of market data coming from numerous third-party providers, as well as internal sales force data. Instead of being overwhelmed by the torrent of data, teams are now able to leverage AI-powered analytics to receive real-time insights to solve all the complex questions about their brand’s performance. Teams in areas such as sales force effectiveness, market access, and payer analytics are utilizing Tellius Smart Analytics Frameworks to quickly build consolidated views of data, speed data analysis with AI-driven automation, and simplify access to insights with search-driven intelligence.

Together, we will dive deeper into the analytics journey: data manipulation, ad hoc exploration, and uncover actionable insights to help increase your brand's market share. By the end of this webinar, you will be able to leverage AI-powered analytics to answer the most complex business questions, as well as implement best practices on receiving buy-in from upper management.

To make any successful marketing and customer engagement efforts, the first step is to have a well stitched Customer Master data with rich and accurate attributions. The conventional MDM Systems though have certain Fuzzy match capabilities , they still need significant Human Data Stewardship to audit merges and publish the master data. The Human interventions required to enable strong data stewardship introduces a lot of variability due to skill levels and increases costs significantly to scale up. The Data stewards leverage several resources typically unavailable to MDM systems such as Google searches, external websites, and looping through corrected Names, Addresses etc. which makes the data stewardship process difficult to reproduce within the conventional MDM assets.

In this Webinar, we will be discussing about a drastically different approach to Data Stewardship where an Assistant application complements the MDM app and leverages several technology advancements as well as AI/ML to accurately replicate or optimize the Human based stewardship process.

Sammed is a Technical Product Manager at D Cube Analytics, has 15+ years of Industry experience in Data and Analytics, Data governance and syndication. Sammed has managed various initiatives in the areas of building Pharma MDM platforms, Data Warehouse, and advanced personalization.

Pradeep is a Principal Consultant Data Scientist at D Cube's India office. He brings close to 12+ years of pure play analytics and data science experience across various industries like Pharma, Hospitality, Telecom and Retail. He has expertise in laying out complete analytical roadmap for the business. He has extensive knowledge of developing machine learning solutions to help support client in their decision making and process automation.

Presenters:

• Sammed Kumar, Technical Product Manager at D Cube Analytics
• Pradeep Kumar, Principal Consultant Data Scientist at D Cube Analytics' India office

Personalized communication is something the HCPs have come to expect. To do that, pharma sales reps need to know their customers well. In order to know the customer well, they need data. Enterprise pharma companies, however, have many data resources and lots of data at their disposal. In fact, there is a data explosion. So what do you do? How do you quickly provide field reps with contextual insights (not data) about their customers to have meaningful personalized conversations? How can sales ops teams leverage incentive compensation data to drive sales motivation and help sales reps surpass their quotas? Using real-time insights, how can commercial life sciences leaders skip the long, boring reports?

Early treatment is a fundamental principle of MS disease management to help lower the risk of disease progression and prevent disability. However, patient awareness of the positive impact of early treatment and long-term continuous therapy remains an issue in MS disease management. Understanding key factors that delay new patient starts on therapy are critical to reaching and educating patients and getting them on the right therapy early on to prevent disease progression and keeping them adherent to prevent disability.

While pharma has been using real world data (RWD) to generate real world evidence (RWE) for clinical trials, post-marketing, and R&D for decades, the emergence and applicability of RWD to the sales, marketing, and commercial side of the house is now ramping up. Many organizations are exploring the possibilities related to targeting, segmentation, sales force effectiveness, and adherence, allocating growing budgets to acquire, analyze, and visualize data. How can pharma manufacturers leverage those resources to ensure they’re engaging HCPs and patients in a timely and precise manner to increase early start of MS treatment? That’s where advanced analytics in RWD translate into actionable insights that can drive positive outcomes.

In this webinar we’ll discuss how applications in advanced analytics and machine learning, can leverage clinical expertise in analyzing RWD to create predictive profiles of patients who meet the criteria for early MS treatment. In the same way, advanced analytics i can be applied to identify HCPs treating those patients along the journey and predict the best moments to share MS treatment information with those HCPs at the point of care – within their workflow. This ability to reach HCPs supports a proactive approach to treatment since they can be reached using an omni-channel approach, providing information beyond the EHR and even when they’re not with the patients.

Presenters:

• Eze K. Abosi, Head, Real World Evidence Solutions
• Adam Almozlino, Vice President, Data & Products, OptimizeRx
• Mark Bard, Co-Founder, The DHC Group
• Rebecca Love, RN, MSN, FIEL