October 6, 2022

Leveraging AI-Powered Analytics to Drive Commercial Success in Life Sciences

Rahul Karkhanis
Rahul Karkhanis
Leveraging AI-Powered Analytics to Drive Commercial Success in Life Sciences

AI-powered life sciences commercial analytics are helping pharmaceutical companies navigate a healthcare landscape that’s more complex than ever before. 

First, decision makers and stakeholders are continually changing. Over the past five years, 350 healthcare mergers have led to greater consolidation, and many physicians have become employees of provider organizations rather than practicing independently. 

Next, pharmaceutical companies are developing an expanded range of products to meet the demand for personalized medicine. Now, sales teams offer everything from small molecule drugs that treat millions of people to specialized treatments targeting conditions in only a few thousand people. Pharma companies also see the negative impacts of a more complex patient journey. About one-third of prescriptions are never filled, and only about 50% adhere to treatments after one year. 

Life sciences commercial teams need an effective path to data-based decision making to maintain market share. AI-powered analytics is the solution to keeping up with the complexity and speed of business. 

Life Sciences Commercial Analytics - Past, Present, and Future 

Historically, pharma companies used syndicated data, marketing data, and real-world data/real-world evidence (RWD/RWE) in analyses. Insights were limited, but even so, legacy platforms required extensive training and custom dashboards to address each specific question. 

Now, pharmaceutical companies have more data sources available to them, including datasets from digital health, integrated patient data, payer and provider data, and information from the partner ecosystem.  However, legacy life sciences commercial analytics dashboards aren’t capable of keeping up with the complexity of today – and won’t scale as data volumes increase in the future. 

A survey from Event Research Group reveals that 70% of enterprises are unsatisfied with their current business intelligence (BI) stack. Additionally, they believe it must be upgraded with new capabilities and domain-specific functionality.

There’s an explosion of data, but companies are still starved for knowledge.” Rahul Karkhanis, Commercial Analytics Subject Matter Expert at WhizAI

Reimagining Life Science Commercial Insights

Artificial intelligence (AI), including machine learning, natural language query, and natural language generation, bring new capabilities to life sciences commercial analytics. In order to accelerate decision-making in commercial teams, Rahul shared four key capabilities to consider:: 

  1. Scalability: Look for a platform that can analyze hundreds of millions of data points today and quickly scale to analyze billions of data points for future analyses. The platform should also make insights accessible anywhere – the web, mobile, Microsoft Teams, Veeva, Salesforce, or other business applications. 
  1. Agility: Some questions are relatively simple to answer, but others require complex analysis. The platform must be able to provide ad hoc analysis and give users the ease of a zero-code environment with on-demand visualizations and calculations.  
  1. Domain-specificity: Life sciences data is unique and filled with nuance that the platform must understand. A life-sciences-specific algorithm will deploy quickly and immediately provide meaningful insights. Additionally, the machine learning algorithm will continue to hone responses and identify new insights with use. 
  1. Built with purpose: When the platform is designed and supported by life sciences experts and pre-trained for life sciences, the platform will be equally valuable to all stakeholders in a life sciences organization. Everyone, from sales to patient services, market access, R&D, and business leadership, can access insights.  

Moreover, an augmented analytics platform will provide insights that all stakeholders can understand. Many professionals on life sciences commercial teams are experts in their fields, but they aren’t data scientists. The platform must answer questions posed in natural language, from simple to complex, and provide insights to these augmented analytics consumers needed to make data-based decisions. 

 A Real-World Success Story 

During the webinar Accelerating Decision-Making with AI-Powered Analytics in Life Sciences to Drive Commercial Success, Karkhanis shared the story of a pharma company that used an AI-powered life sciences commercial analytics platform. The organization turned to the platform to answer the question, “Why are my sales down?” And the root cause was somewhat unanticipated. 

Using patient-level claims and other data, the platform confirmed that sales were not keeping pace with the brand’s key competitor. In the zero-code environment, the platform quickly visualized sales, and then the user was able to request the 13x13 change in claim counts.

The user requested insights on claim types, revealing that rejected claims had increased during that quarter by 17% while the competitors had remained relatively stable. Furthermore, the platform was able to show the user that rejected claims were occurring at the intermediary level. The platform then provided suggestions for correcting the issue, such as sharing insights with the sales force, identifying approving physicians, and working with them to improve formulary status. 

The analysis took minutes rather than weeks or months that legacy life sciences analytics processes would have taken to arrive at the same conclusions. 

Paving the Way for Change

Gartner predicts that by 2023, 35% to 50% of insights will be driven by vertical- and domain-specific augmented analytics solutions. However, not every life sciences organization is ready for change. 

Dharmesh Thakkar, Senior Director of Commercial Operations for Janssen Pharmaceuticals, explains, “Some people have to see it to believe it. Other people need to believe it to see it – get those people involved, get early wins, share successes, and eventually, you’ll prove the value.

However, in an increasingly complex healthcare landscape, an AI-powered life sciences commercial analytics platform’s value will be readily apparent. The speed, scalability, agility, contextual insights, and automatic visualizations will give life sciences commercial teams the information they need to navigate complexity and achieve success. 

Watch the webinar Accelerating Decision-Making with AI-Powered Analytics in Life Sciences to Drive Commercial Success on demand to learn more.

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