shutterstock_344859035.jpgRohan Fernando co-wrote this blog post with Dharmendra Sahay.

Through data and analytics, healthcare is on the verge of a transformation, one that will change the way that providers, payers and pharmaceutical manufacturers help people manage wellness. The various players in today’s healthcare ecosystem are racing time—and each other—to collect and control patient information, all in the name of making quicker, more effective decisions and optimizing outcomes.

While pharma’s highly regulated nature makes disruption challenging, the forces shaping other industries are beginning to infiltrate the healthcare realm: There’s an abundance of information and data, patients are beginning to take ownership of their own health, and stakeholder partnerships are on the rise. Data and analytics are positioned to change the way that patients are being diagnosed and treated, and also how they find and consume information. 

Meanwhile, pharma's customer base is changing from primarily individual healthcare providers to include patients, payers and institutional providers. These customers are increasingly using digital channels to learn about new drugs or seek new treatments. Pharma needs to find a way to respond to these market forces and adapt to an evolving healthcare ecosystem powered by technological shifts.

Here are four steps that pharmaceutical companies can take to keep pace with healthcare’s analytics-enabled future:

  1. Change the analytics “mindset.” The industry is in between two versions of analytics capabilities: The current version produces analytics that support humans in making better decisions, but companies need to begin developing artificial intelligence and automation engines that gather information from many sources and determine what action needs to be taken next. Data and analytics capabilities are ready to shed their supporting role in favor of a transformative, decision-driving function.
  2. Break down siloes to enable a centralized model. Traditionally organized by product, pharma companies have created data siloes that prevent cross-team collaboration. To enable the data-driven decisions needed to make a difference in today’s healthcare ecosystem, companies need to centralize their analytics functions and data ownership models. This will help develop a comprehensive data platform, create end-to-end analytics automation and enable a critical mass of skills such as data science.
  3. Find the right balance between machines and humans. We’re rapidly approaching a time when we’ll rely on machines to make important healthcare decisions such as diagnosing a rare disease or determining the right treatment protocol. The trick is learning to trust the machines’ recommendations, and that comes with maintaining a human element. In the case of reaching valuable HCPs, the machine's role is to determine which customers' engage  with specific types of content and channels.
  4. Teach algorithms to adapt to human input. Machine learning and AI-based solutions introduce new ways to improve the customer experience and drive better business decisions, but learning to maximize the machine-to-human interaction will be key. For example, a sales rep can disregard the machine’s prompts when he has better information about a target account, just as a driver might ignore an upcoming turn recommended by a car’s GPS. When the driver chooses to take a different turn, the GPS adapts and moves on to the next recommendation.

Once pharmaceutical companies have overcome these obstacles, they’ll move beyond point analytics to create automated platforms and AI systems capable of personalizing treatment regimens and driving better outcomes. The companies that create these capabilities and reorganize quickly will gain a competitive advantage. The race to conquer analytics-enabled healthcare decisions is on.

Dharmendra Sahay and Rohan Fernando explored this topic in-depth on a podcast with Mike Walsh, a futurist and the CEO of Tomorrow, an innovation consultancy. Listen to it here.


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Topics: big data, Pharma, Analytics, centralization, healthcare ecosystem, Customer Expectations, personalized medicine, artificial intelligence, AI, machine learning, wellness