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Humanizing of Machine Learning Technology

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Digital engagement is becoming the new normal, but it can inhibit the individual connection that is foundational to building long-term customer relationships. How can reps, brand teams, and medical science liaisons strike the right balance of personal and non-personal engagement? The next generation of AI has an answer, writes Derek Choy.

With the explosion of personalized brand experiences among consumers, it was inevitable that healthcare professionals (HCPs) would expect more personalized, integrated interactions from life sciences companies. COVID-19 has heightened this need as most companies were forced to transition nearly all face-to-face interactions with doctors to digital engagements, which is likely to remain standard for the foreseeable future.

However, even as digital engagement becomes the new normal in commercial life sciences, it can inhibit the individual connection that is foundational to building long-term customer relationships. So, how can field reps, brand teams, and medical science liaisons strike the right balance of non-personal digital interactions and authentic, human engagement?

The next generation of Artificial Intelligence (AI) offers an answer.

Early commercial AI offerings were hampered by the one-note nature of their approach. Most solutions fell neatly into one of two buckets: machine learning or expert systems (an if-then, rules-based approach). The problem is that neither technology, by itself, does a great job replicating the way humans really think. Without necessary context, machine learning technologies can generate conclusions that aren’t practical in the real world. At the same time, it would be impossible to catalog every possible scenario a user might encounter with rules alone. If AI 1.0 was defined by technologies working in isolation, the next generation is all about synergy. The most advanced AI solutions in commercial life sciences are now an artful blend of various analytics technologies and human insights, and they’re far better at achieving human-enhancing levels of intelligence. AI 2.0 is here, and it’s helping companies deepen HCP engagement in a digitally driven commercial model.

Layering human insight over big data

The life sciences commercial model is incredibly complex. There are more channels, more content, more data, more stakeholders, and more external pressures than ever. Companies cannot simply dip a brush into an AI paint can, slap it across the surface of the commercial process, and expect results.

Consider a comparison to online retail. Amazon offers personalized recommendations based on browsing and shopping history. Most data reside within Amazon and make very direct connections – for instance, you shopped for this, so you may also like this.

In life sciences, however, there are many more variables to consider, including:

  • Data sets exist in many places, both within the life sciences organization and externally.
  • Brand strategy is unique to each product, and a single rep may carry several.
  • HCP interactions differ greatly between therapeutic areas and regions.
  • Data privacy is essential, and data can only be used in certain environments and forms.

An environment with so much at stake requires a well-rounded AI solution to conquer go-to-market complexity — made even more challenging by the pandemic. AI 2.0 leverages the best of both humans, who are good at decision-making in complicated situations, and machines, which excel at data processing but struggle to make basic judgments, to create more meaningful relationships with HCPs.

Additionally, AI 2.0 is actionable and resides in the daily workflows of its users. “We are seeing the power of AI at EMD Serono, but my early experiences with AI at previous companies were not as successful. One of the challenges was that they seemed unnatural and disjointed with the users’ workday,” explained Joel VanderMeulen, senior director of commercial strategy and operations at EMD Serono, a Merck KGaA company. Now, recommendations are fully integrated into the workflow of our field teams. Each day they are provided new, actionable insights that fit naturally into their day.”

The right blend of ingredients

Advanced technologies — business logic, machine learning, explainable AI (xAI), and optimization — plus human contributions have transformed nascent forms of AI into the advanced solution it is today. While the analytics technologies are remarkable, it is the overlay of human intervention that provides critical context to not only make the recommendations more relevant, but also improve user confidence.

A company’s communications with HCPs must reflect their preferences or behaviors across different interactions if they are to be relevant. This intelligence can come from machines — such as email response rates and actions, or humans –— such as call notes from the field. Third-party data sets and the insights captured in a company’s customer relationship management application, marketing automation platform, and other tools provide a foundation for understanding how HCPs typically interact with a brand. AI 2.0 considers all these sources in real time, while separating the signal from the noise.

Optimal results will only come when every interaction and data point is analyzed in conjunction. Together, they provide the circumstances, environment, and background to clarify meaning and dial in messaging.

AI that aligns

In addition to ensuring communication is relevant to its recipient, AI 2.0 ensures that all customer-facing teams are aligned across touchpoints. If an HCP receives an email on Tuesday, a visit from a rep on Wednesday, and a call from a medical science liaison hours later, HCPs will grow frustrated, overwhelmed, and tune out. Incongruous outreach shows a disregard for customers’ time and a woeful lack of understanding of their needs. However, if all efforts are coordinated and teams share context from their past interactions, HCPs will be enriched by each engagement with the brand.

AI 2.0 captures and catalogs every interaction and incorporates it into context-driven recommendations across teams. From prescribing habits and patient demographics to research interests and event attendance, every relevant piece of information should be considered. This includes qualitative intelligence from the field, behavior trends gleaned from past campaigns, and any recent interactions with the brand. It is a lot to consider, but no longer impossible. Today’s AI makes this doable at scale.

Dynamic market requires dynamic AI

Development cycles may be long and predictable in life sciences, but commercial market factors are not. They are dynamic and unpredictable with competitive threats, regulatory changes, and ever-changing HCP preferences. AI 2.0 helps companies keep pace by constantly learning. As campaign elements succeed or fail, next-generation AI incorporates those learnings and optimizes execution in real-time. AI 2.0 redesigns and retrains the commercial model to incorporate new context at every step.

Determining a physician’s channel preference offers an example. It is possible to make broad assumptions about channel affinity based on audience segments and market research (think: younger HCPs may prefer email over phone calls). However, HCPs are people first, and people are complex: different attributes will carry different weights depending on the physician. Working with one rule-of-thumb heuristic at a time is too simple and time-consuming to keep up with the speed of change.

Next-generation AI, on the other hand, quickly makes complex assessments at the individual level, evaluating every characteristic — from specialty and patient coverage data to when they graduated medical school — against a large dataset of HCPs to determine communication preferences and likelihood to engage on a one-to-one level.

The machine learning tools in AI 2.0 are also extremely effective at pinpointing the best time to reach out to an HCP. They excel at evaluating multiple inputs simultaneously to consider their impact.

Knowing when an HCP has historically accepted visits or opened emails is one part of the equation, but how can you predict the optimal time to engage when a new channel is added to the mix? In addition to using key HCP attributes as predictors, AI that can apply what it learns from one environment to the next is critical. It provides context, which is the difference between AI that “thinks” like a human and AI that performs rote calculations like a machine.

“AI innovation that incorporates human context would be incredibly valuable,” said VanderMeulen. “Combining primary data, such as unstructured call notes from field teams, with secondary data, such as script writing and other data from market research companies is the next level. It’s extremely exciting.”

In the legacy commercial model, when regional planning of a brand campaign occurs, a sales plan is created within a pre-set budget limitation that identifies a call plan, channel mix and headcount. That plan is typically reviewed and modified about once every 6-12 months. More than likely, that plan is outdated in just one month.

In contrast, once AI 2.0 is applied, companies go to market with dynamic plans that are continually refined by new insights for more personalized engagement. Here are five immediate improvements:

  1. Data and human-driven suggestions are delivered to users daily and include reasons why the suggestions are being made to increase trust.
  2. Brand executives can run outcome simulations using different types of call plans, channel mixes, messaging changes, and HCP journeys.
  3. Commercial teams can adjust in real-time to local physician and formulary access conditions and coordinate with local patient and advisory groups.
  4. Sales, marketing, and medical affairs can engage in coordinated customer interactions to avoid overwhelming busy physicians.
  5. Learnings and outcomes from every interaction inform immediate optimization and long-term strategy refinement.

Physicians’ increasing expectations for personalization coupled with decreasing access have driven a slow-drip of changes to the commercial model in recent years, but the ultimate catalyst has been COVID-19. As one colleague put it, the future just showed up. Even if it is not clear what HCP engagement in a post-pandemic world will look like, the need for commercial transformation could not be more apparent.

Fortunately, the life sciences industry is ready. After years of steady, incremental progress, most commercial teams now have a strong data and analytics infrastructure in place and are already turning on the right digital and non-personal communication channels at scale. The groundwork is well underway. Now, it is time to fully embrace maturing technologies like AI — which has evolved into a powerful blend of the best of both man and machine.

As the industry navigates this challenging environment where HCPs are even more burdened by new methods of communication and care, a contextual AI approach ensures that the information HCPs receive is tuned to their specific needs and preferences. It digitizes, analyzes, and incorporates every piece of data and feedback to personalize the customer experience. AI 2.0 enables productive interactions with HCPs to ensure they can remain focused on patients while staying informed of the latest treatment options.

VanderMeulen concluded, “As an AI advocate, I’ve always believed that it holds great promise to revolutionize how we work with doctors. Early on, I wondered, how quickly can we incorporate new communications channels into AI? How can we bring in different data sources, both internal and external? How can we mine for valuable, unstructured insights from our frontline field teams? The answers are in AI 2.0. Now, it’s up to us to fully leverage it.”

This article has been published from the source link without modifications to the text. Only the headline has been changed.

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