Webinars starting with H

Hyper-automated AI has moved beyond futuristic speculation and firmly established itself in the present, revolutionizing industries worldwide and delivering superior efficiencies and insights through a comprehensive suite of machine learning, robotics, natural language query, and other technologies.
 
Life sciences companies, immersed in the daily data deluge, stand to benefit greatly as well. This technology can significantly enhance existing digital capabilities, transforming complex healthcare data into actionable intelligence in a fraction of the time it would traditionally take.
 
However, while all this certainly sounds very promising (and be that as it may), it is impossible to ignore the necessary changes that organizations will have to undergo before reaching this milestone.
 
Join industry experts Nuray Yurt (Merck), Ravi Shankar (Novartis), Nadia Tantsyura (Boehringer Ingelheim), Nitin Raizada (Indegene), and Vikas Mahajan (Indegene) as they unpack top use cases and winning execution strategies for Hyper-automated AI in life sciences.
 
Key questions that will be addressed include:

• Where and how hyper-automated AI can be implemented in life sciences
• Why this technology should be a strategic necessity versus a tactical choice
• How to prepare for such disruptive technology
• Why it’s not a robotic overthrow of humans
• How to deal with interoperability challenges

Moderator:

• Vikas Mahajan, Senior Director of Data and Analytics, Indegene

Speakers:

• Ravi Shankar, Executive Director of Data and Analytics Enablement and Strategic Data Products, Novartis
• Nuray Yurt, Business Engagement and Activation Lead for Digital Data and Analytics, Merck
• Nadia Tantsyura, Global Capability Owner of Data Domain and Analytics, Boehringer Ingelheim
• Nitin Raizada, Vice President of Enterprise Commercial Solutions, Indegene

Not only are artificial intelligence (AI) and machine learning (ML) two of the hottest buzzwords in the life science industry, but the underlying technology is changing the entire landscape of pharma, biopharma, and biotech. Increasingly, these companies are utilizing AI/ML analytics to inform decision making across the development lifecycle – reducing costs and shortening timelines associated with traditional data analysis.

In this session, industry experts will share how powerful AI / ML analytics can be used in pre- and post brand launch to provide treatments more quickly to patients, followed by specific applications to:

• Identify early signs of disease
• Speed up clinical trial patient recruitment
• Predict patients likely to respond to treatment options
• Monitor adverse events and side effects
• Estimate high-risk and rare disease patients
• Understand patient’s journey from multiple lenses
• Maximize promotional marketing and budget allocation

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

With more and more specialty pharmacy products being launched (and in the pipeline) utilizing a combination of sophisticated distribution, complex dosing & administration, and high cost and high touch patient services requirements, the reliance of pharmaceutical manufacturers on specialty pharmacy is only getting stronger.

Understanding the payer’s influence on patients access and adherence to medications is also becoming increasingly important as various payers look to implement utilization management tools for high cost medications; this can significantly impact not only the patient’s brand experience but also the overall utilization of the medication.

It is more critical than ever for analytics teams to understand the power and control that payers are having on their brands, and which policies can hurt or help them in the marketplace. Rising pressures on margins have led to the prioritization of predictive analytics, as organizations look for insights to support commercial decision making.

Join this webinar to hear how one organization created a process for connecting BIN/PCN/Group Identifiers to unlock payer information and connect data sources to create a holistic data mart. Hear their case study and understand actions taken by one organization to:

• Analyze copay card buy downs by plan
• Make impactful decisions on payer contracting
• Develop refined forecast models

Presenters:

• Ashwin Athri, MBA, SVP, Commercial Data Management, Precision Xtract

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

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