Takeda Oncology is on a digital transformation journey with a strong focus on elevating its analytics programs. While adopting a digital-first approach, the biopharma company has focused on enabling in-house predictive and artificial intelligence (AI) capabilities, expanding its use of real-world data (RWD) and embedding the rigor needed to optimize every business decision. Ultimately, the goal for its Insights and Analytics team is to fully enable its sales force with these capabilities and then roll them out to other customer-facing groups. The COVID-19 pandemic simply accelerated the launches it already had underway.
One critical project on the list for Mayank Misra, head of business insights and analytics at Takeda Oncology, was to develop an application that would bring together the attributes of real cancer patients with the treatment choices available to oncologists. Its goal was to assess this data to inform the Takeda Oncology sales team of next-best actions to take in their outreach. This new application would deliver valuable information to the biopharma company’s customer-facing roles.
“Drivers for such an application are not new,” said Misra. “As the marketplace becomes increasingly competitive with targeted therapies, pharma at large and oncology in particular, face an urgent need to educate stakeholders on the differentiated value of their treatments. With access to physicians increasingly becoming difficult, it is paramount that every engagement provides contextually relevant information to support healthcare providers in choosing appropriate treatments for their patients.”
Misra explained that the Insights and Analytics team at Takeda Oncology selected ZS because of its deep knowledge of the oncology business and its knowledge of healthcare-related data and technology. In particular, the team valued ZS’s expertise in applying AI and machine learning to solutions tailored to the pharmaceutical industry. “ZS was a natural fit from that perspective,” he said.
The AI-machine learning solution that Takeda Oncology and ZS partnered on analyzes the treatment choices of individual healthcare providers rather than the traditional approach of analyzing physician groups segmented by market share, which can dilute or distort their unique needs.
The core capability of the solution armed Takeda Oncology’s sales force with contextually relevant messages that were likely to best resonate with that healthcare provider, as well as the next actions to take. The AI-machine learning solution not only enabled next-best actions for the field, but it also surfaced real-world insights on subpopulations of patients and the nuanced choices physicians made when caring for these small, but similar, patient groups.
“The model we’ve built can predict the next step in the patient's journey with a certain likelihood,” said Misra. It overlays HCP treatment choices for these micro patient populations, clusters them and maps appropriate key messages and actions. “It’s designed to identify relevance and timing for both the patient and the physician.” The solution uses both machine learning models and human feedback to continue to learn over time.
“I think the time-to-solution increases exponentially with a partner like ZS that understands your business and your disease area of focus,” said Misra. “The choice of when to include or exclude a certain data point may sound tactical and basic, but these types of choices are critical for solutions that use AI and ML. It is the difference between finding the proverbial needle in the haystack or adding more hay, making the task to find insights more difficult. This ability to differentiate only comes when you understand the patient journey, the disease state, the reimbursement dynamics and the interplay between health economics. This project took about four months—which is unheard-of in my experience but was not a surprise given the strengths of our partners.”
The Takeda Oncology customer-facing roles that use this solution eagerly anticipate the new insights released every two weeks because these help them manage their workflows and engage customers in a more deliberate and intentional manner. The solution is enhancing pre-engagement planning for these roles by helping them better understand the healthcare provider and the patient journey, instilling confidence and elevating the quality of their conversations.
“With this project and this capability, we are able to—in a very visceral manner—connect the investment in analytics and data directly to actions and decisions we are making in the field on a day-to-day basis,” said Misra.
Misra also sees this project as a success because it is driving interest across the Takeda Oncology organization. “You always know you are succeeding when different brands and franchises approach you on ways their brands can benefit from a similar capability. That’s the momentum we have built.”