The time is right for drug and device companies to follow the lead of these organizations and adopt a data-driven commercial strategy to drive operational excellence and successfully compete in an environment defined by high-speed change. Adopting chatbots specific to the needs of core consumers is one way pharma companies can enhance the patient and physician experience.
Consider a hypothetical oncology company commercializing a second line of treatment that decides to offer a chatbot to answer questions of patients with a particular type of cancer. Over time, the oncology company can learn a lot about its consumers based on questions they ask. This information can feed into a machine-learning tool that segments patients based on the risk of failing a first line of therapy. Similar to the way major airlines use specific codes to anticipate engine issues, a database of questions or a sequence of questions can be established to identify these high-risk patients. Once patients with a high risk of failing a first line of treatment have been identified, nearby pharma sales representatives could receive a notification and start a conversation about a second line of treatment with a patient’s physicians, even before the first line of treatment has failed.
Simultaneously, the oncology company also can partner with cancer hospitals, academic medical centers and other providers in this space, and use advanced analytics to automate notifications for patients at the time they fail the first line of treatment. If the company does not have a strong data-driven commercial model, a third-party data provider can be engaged to provide the clinical data needed for the analytics function.
To get to an advanced analytics commercial application, such as the oncology example described above, pharmaceutical and life sciences companies will need to make strides in each of the six analytics maturity areas. Strong data integration, quality and security is needed to surface the triggers that feed into an alert system. Technology and infrastructure is needed to connect appropriate commercial staff into the system.
Company culture and the right people are needed to pivot away from legacy commercial strategies and processes. Governance and organizational structures will need to change, in order to support and facilitate new applications of advanced analytics, and to effectively monitor results.
Organizations that successfully improve analytics maturity will move closer to patients as well, by anticipating behaviors and hurdles to positive health outcomes. Analysis and insights gathered from real-world evidence, such as prescription fills and patient outcomes, combined with patient and physician profiles, can help to facilitate value-based contracts or subscription payments based on patient outcomes or financial performance. In an HRI survey, 82 percent of provider executives said they believe that data sharing with pharmaceutical companies will be important in the future.
As new technologies, tools and data sets enter the analytics environment over time, drug and device makers learn more about what is happening in the market and why it’s happening. The “why” is especially important—understanding unique customer behaviors leads to additional insights and a greater ability to predict which investment options will deliver the largest return. The effectiveness of this approach has been demonstrated by companies in other sectors. Pharmaceutical and life sciences companies, however, must overcome challenges with sourcing talent and skills, as well as creating a culture and data environment that facilitates advanced analytics and fast access to insights.