Adoption of advanced analytics is beginning to transform the healthcare ecosystem

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Ben Comer Senior Manager, Health Research Institute, PwC US March 26, 2019


Some drug and device companies have had early wins in deploying analytics to drive commercial excellence.

At one major pharmaceutical company analyzed by HRI, the use of analytics for sales force effectiveness—a strategy that helps focus efforts on highly profitable customers—drove six years of continued growth by improving physician targeting and identifying “success principles” of top-performing sales force members. At another major pharmaceutical company, sales force effectiveness resulted in a market share increase of three percent in 18 months, aided by a supportive executive culture and increased focus on data and analytics.

Although many pharma companies are establishing analytics centers of excellence and have achieved a varying degree of success with technologies such as AI and machine learning, the capabilities gap is still wide between pharma and organizations from other industries.

Other stakeholders in the healthcare system are capitalizing on the explosion of available patient data collected in electronic health records, a result of meaningful use requirements.

Providers, payers and new entrants are ahead of the curve compared to traditional drug and device companies in the implementation of big data, machine learning and predictive analytics into their commercial strategy. In 2018, 87 percent of provider executives and 83 percent of health insurance executives said they currently use predictive analytics, or plan to in the next five years, according to a Society of Actuaries survey.

Hospitals in France, for example, are actively using big data and machine learning tools to predict admission rates, leading to a more efficient allocation of resources—in staffing among others—and improved patient outcomes. In the insurance sector, one major US company is using electronic medical records systems that can trigger warnings when a patient is due for a medical exam or send notifications when a prescription has been filled— an indication the patient is following doctor’s orders. Patients who received automated medication reminders refilled their prescriptions at a 14 percent higher rate than patients who did not receive the reminders.

The time is right

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.

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.

Steps need to be taken

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.

For more on this topic, read the HRI Report, Advanced analytics fuel tomorrow’s commercial strategy for drugs and devices.

For more of HRI’s insights and content, visit our Regulatory Center and report library

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Ben Comer

Ben Comer

Senior Manager, Health Research Institute, PwC US

Benjamin Isgur

Benjamin Isgur

Health Research Institute Leader, PwC US

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