Webinars starting with I

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 COVID-19 pandemic has impacted every walk of life, and businesses are still in the midst of evaluating the pandemic’s impact and mitigating potential losses during these times. The pharmaceutical industry, which is at the forefront of finding an effective cure for the pandemic, has also faced challenges. The industry needs to explore innovative ways to engage with its core customer groups (including patients, prescribers, and payers).

Even before the pandemic hit the globe, the pharmaceutical industry was grappling with the notion of enabling omnichannel strategies to engage with key customers. As the pandemic has resulted in complete lockdown on sales rep access to physician offices, the industry has been forced to devise an omnichannel engagement strategy “on-the-fly.” Most companies had to set up remote detailing capability in their reps’ CRM platforms. But omnichannel engagement goes beyond just remote or virtual detailing – it also requires changing the focus from the brand/campaign to the customer.

In this webinar, Axtria will review the current trends toward omnichannel migration in the industry. The webinar will discuss how the customers of the pharmaceutical industry (i.e., patients, prescribers, and payers) have embraced omnichannel to connect with each other and how the industry needs to adapt to meet needs based on customer-preferred channels. The webinar will also cover new approaches to using population mobility information to devise a differentiated omnichannel strategy during the time when local economies start to open up. Axtria will also recommend a path for pharmaceutical companies to transition their traditional multichannel planning approach to an orchestrated omnichannel planning approach.

Presenters:

• Dhaval Mukhatyar, Senior Director, Axtria
• Bob Mozenter, MS, MBA, Director, Marketing Solutions, Axtria
• Devesh Verma, PhD, Principal, Axtria

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

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

Organizations are transforming their sales functions from being reactive to proactive, and from intuition-driven to insight-driven. The emergence of vast amounts of sales and patient level data from multiple sources and platforms has provided companies with more information than they’ve ever had before. Machine learning is a branch of artificial intelligence that enables computers to recognize patterns in existing data, update with new patterns from incoming data and continuously optimize recommendations. Innovative machine learning methods, together with clinical insights and continuous model enhancements, provides superior proactive and precision targeting results. It allows sales functions to improve their sales performance and effectiveness.

In this webinar, we will discuss the following aspects:

• Business objectives and benefits of proactive sales strategy
• Proactively identify sales potentials among targeted universe
• Help sales functions update/prioritize their target and call focus with patient insights
• Improve sales performance
• Outline of machine learning methodology and process
• Data collection, cohort define and feature calculation
• Model build and selection
• Model validation & application
• Model enhancements and validation on precision targeting
• Model introduction: Logistic, Random Forest, XBG, Deep learning
• Enhancement: Stacking, Ensemble, etc (flexibility of using models)
• Demonstrate how proactive and precision targeting optimizes sales performance
• Illustrate how to validate model accuracy on a timely manner
• Describe case study objective
• How selected model is implemented
• Validation on precision on a timely manner
• Other insight of targeting in a timely manner
• Patient profile
• Doctor insight

Presenters:

• Li Zhou, Sr. Principal, Advanced Analytics Organization, Global, IQVIA
• Zhang Zhang, Manager, Advanced Analytics Organization, IQVIA
• Lynn Lu, Senior Principal, Advanced Analytics Organization, IQVIA