shutterstock_608550530.jpgAccording to historical accounts, 19th-century English artist Sir Samuel Luke Fildes was determined to “put on record the status of the doctor of our time” when he painted “The Doctor,” a depiction of a concerned physician tending to his young patient. And though it was created in the midst of the Second Industrial Revolution, the artwork includes no sign of advancing medical technology. Instead, it presents what has become an iconic portrayal of patient-centered medical care.

With smartphones in our pockets angling to replace the stethoscopes around doctors’ necks and companies breaking ground on futuristic hospitals offering coordinated care at a lower cost, the World Economic Forum asserts that medicine is once again on the cusp of an industrial revolution, so how would an artist portray the delivery of care today?

Artificial intelligence would surely get a nod in the present-day depiction of medicine—but only if its contribution to patient-centered care could be properly defined. AI is cutting down on human error and speeding up the delivery of care in busy hospital emergency departments by lending a hand in triaging patients into risk pools and coordinating patient care, according to JMIR Medical Informatics. And an Artificial Intelligence in Medicine study reports that University of Indiana researchers created a system that reduces treatment costs by 60% and improves patient outcomes by 35% based on an AI simulation framework.

In the future of artificially intelligent decision-making, pharmaceutical companies will need to add computer systems to an already long list of customers that includes patients, payers, distributors and providers. As my colleague Pratap Khedkar pointed out in response to an AI article in The New Yorker, that means that manufacturers likely will need to respond to AI with an overhauled marketing strategy, moving from developing promotional messaging that spreads information to developing evidence that spreads applicability.

Here are three ways that drugmakers can tailor their value propositions to align with the needs of these faceless decision makers:

  • Add new information to your customer insights. To get a pulse on evolving definitions of value, pharmaceutical companies need to increase collaboration and data sharing with their stakeholders to move beyond large sets of blinded market research data, which are becoming insignificant compared with the more targeted and specific data used in machine learning. In addition, companies should be tapping into their key account management teams to gather direct customer feedback, which flips brand-centric thinking to customer-centric thinking—a more likely “source of truth” for training AI. Companies can pair those customer insights with existing data to create and customize value propositions that address specific customers’ needs and pain points, better ensuring that manufacturers are developing a customer-focused menu of offerings beyond the pill.
  • Experiment with innovative provider contracts. Market analysis suggests that provider organizations will continue to balance cost with quality, and will thereby train their internal systems to select products based on their overall value. With outcomes emerging as a critical component of value, large pharmaceutical players are experimenting with outcomes-based agreements. This analytics-based approach to contracting allows companies to showcase the long-term benefits of a therapy from both a patient’s and a system’s perspective. Creating a link between outcomes and value is a necessary part of the discussion with payers and providers as we prepare for the widespread adoption of value-based contracts. Only with a collaborative approach will drugmakers achieve the scale to analytics-based contracting work.
  • Uncover ways to optimize drug distribution channels. Provider contracts and drug distribution are attractive levers that can help pharma companies differentiate themselves from the competition. Companies already are using algorithms to predict product demand and marketing costs, and even to establish baseline prices for wholesale distributors, but pharma organizations could train algorithms to tackle complex distribution channels, particularly as the industry moves to a more targeted and personalized medicine model. Increasing the efficiency of pharma’s supply chain presents an attractive opportunity to organizations well beyond the pharmaceutical industry, reportedly including retail and logistics monolith Amazon.

The fourth industrial revolution has been galvanized by machine learning and artificial intelligence. These tools can digest research and results in quantities that no single human being ever could rival, all in the name of presenting a batch of potential treatments and outcomes in the blink of an eye. Though the possibilities are exciting, we shouldn’t lose sight of the power of the physician-patient relationship. Fildes’ iconic portrayal of medicine, unveiled to the world against a rapidly changing social backdrop, reminds us of the crucial importance of patient-centered—and human-delivered—medical care.


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Topics: customer insights, distribution, customer centricity, Pharma, contracting, artificial intelligence, AI, value proposition, machine learning, drug distribution