Introduction

Jose Maria "Chema" Guido Avila, Global Patient Experience Lead Dupixent Asthma and COPD at Sanofi, believes the pharmaceutical industry suffers from a "shiny toy syndrome," constantly chasing new technologies without properly considering their business value. As AI becomes the latest fascination, he advocates for a strategic approach that uses these tools to create more meaningful human connections rather than simply adding more digital noise.

"We, in the pharma industry, are spending our budget targeting patients on only when they are engaging with content related to their disease. That corresponds to only 3% of the time they spend in online media," Chema explains. "We need to stop treating them as patients with symptoms and start understanding them as humans with diverse interests and needs."

Changing Customer Expectations in Healthcare

Consumer experiences with platforms that leverage AI like Netflix, Spotify, and ChatGPT are fundamentally reshaping expectations about personalized experiences across all industries.

"Doctors are people too," says Aguido Avila. "They're also being spoiled by ChatGPT, Netflix, and Spotify. They are extrapolating those expectations into pharma."

The challenge for pharmaceutical companies is to avoid simply adding AI to existing omnichannel strategies that already overwhelm healthcare professionals. Instead, companies should leverage AI to create more human-centered experiences.

Precision Marketing: Understanding Patients as People

One successful experiment involved using AI to develop deeper patient personas that went beyond traditional medical classifications. Rather than focusing solely on disease characteristics, the team identified broader interests and concerns that dominated patients' attention.

The analysis revealed that patients spent only 3% of their time seeking information about their disease, with the remaining 97% focused on other life interests like dating, job searching, healthy recipes, and stress management.

By creating content that connected these broader interests to disease management, the team achieved remarkable results: "With this approach, we actually got 100% increase in website conversions and reduced the cost per conversion by 60%," notes Aguido Avila.

AI Content Generation: More Human Than Human?

In content development experiments, Aguido Avila found that AI-generated content often demonstrated greater empathy and quality than human-created alternatives. However, the AI content required more medical-legal-regulatory (MLR) reviews, offsetting some efficiency gains.

"AI is giving us much more work, at least in my experience," he explains. "We are generating so much content that we are not humanly capable of reviewing it all."

The experiments showed that while AI excelled at creating empathetic content and representing diverse ethnicities, humans still had advantages in understanding regulatory requirements and creating linguistic variety.

Future Applications: Virtual Humans and Digital Twins

Aguido Avila highlighted several emerging AI applications with significant potential:

1. Speech Analysis: Training patients to better explain their symptoms to doctors or helping physicians communicate more empathetically with patients.

2. Virtual Medical Assistants: Digital humans that can assess symptoms and recommend diagnoses, validated by healthcare authorities like the Mayo Clinic.

3. Digital Twins: Simulation technologies that can run in parallel with conventional clinical trials, enabling external control arms without affecting human lives.

4. Simulation Agents: Virtual replicas of sales representatives that embody their values and expertise, potentially reaching healthcare professionals who are currently inaccessible.

5. GenAI Influencers: AI-generated personalities that can drive engagement for health conditions while avoiding the costs and risks associated with human influencers.

Key Takeaways

1) Look beyond specialties for personalization: "We haven't even got that step to understanding doctors beyond their specialty. We believe that because they share the same specialty, they will have the same interests, which isn't true."

2) Treat patients as humans: "This is much more human because in the past, we were treating them as patients. On the other side, we're like, 'you're a human with interests affecting your life. We understand you.'"

3) AI content can be more empathetic: In controlled tests, AI-generated patient content demonstrated higher quality and empathy than human-created alternatives.

4) Virtual humans increase disclosure: "Virtual humans increase patient willingness to disclose because computers lack the proclivity to look down on people."

5) Fix omnichannel execution first: "We can use AI for personalization, but we need to fix our omnichannel execution first. We need to stop the shiny toy syndrome in pharma."

DISCLAIMER: This article reflects the personal opinion, statements and ideas of Jose Maria “Chema” Guido Avila. None of what is stated is to be regarded as his current employer Sanofi or its Group of companies’ statement, opinion or similar. Some of the recommendations made by Chema and their context are based on both his own research and his 20+ years experience in the local and global pharmaceutical industry across different therapeutic areas

"Getting people to talk to each other sometimes is like step zero of the whole process. And a lot of times we realize there's a lot more we have in common in our goals than we have different."

Leveraging Data for Meaningful Engagement

Data serves as the foundation for Otsuka's customer engagement strategy, particularly the data collected from interactions and activities. "We kind of passively collect a lot of that data from our CRM system or from other platforms," Urban notes. "Those pieces of data allow us to stitch together an internal picture of all the different people in Otsuka that are talking to a given clinician."

This comprehensive view enables more meaningful interactions. "One of my pet peeves is I really don't think clinicians should have to figure out our org chart," Urban says. "Whoever shows up at their door should have at least some cursory knowledge to be able to say, 'I know what's been happening in your office over the last few weeks, I know where we're headed, I know some events that have happened recently and I can speak to those and have a really meaningful and value-add conversation live in the moment.'"

Adapting to the Modern Clinician

Today's healthcare professionals are increasingly digital natives, requiring pharmaceutical companies to evolve their engagement strategies. "In 2014 or so, people who are practicing medicine in the United States became more digital native than not in the population of clinicians," Urban explains. "If we continue to offer up the same sort of services and support that we've offered for the last 30, 40 years and expect that to be just as impactful as it was when it was sort of novel, I think that's where we maybe are missing the boat."

This evolution is particularly important as access to physicians becomes more limited. "We have not even close come back on frequency," Urban notes, referring to post-COVID engagement levels. "The number of times we get in front of these clinicians nowadays is going to be a lot fewer per time period. So each one of those conversations needs to be that much better and that much more planned out and that much more thoughtful."

Innovation Through Flawless Execution

Rather than pursuing flashy innovations, Urban emphasizes the importance of executing fundamentals exceptionally well. Otsuka's "decision engine" exemplifies this approach, running monthly analyses of clinician environments and incorporating weekly data updates to provide field representatives with timely, relevant information.

"We have what we call our decision engine, which is running on a monthly basis a full kind of relook at the environment of clinicians that we have and what's going on in their world," Urban explains. "That context can help give a much more timely set of information to our salesperson to engage with that clinician based on relatively recent information."

Looking to the Future

Looking ahead, Urban sees several areas poised for evolution. Data analytics and insights will remain fundamental, but with greater emphasis on "analytics-ready data sets" and standardized metrics. Operational changes will likely respond to clinicians' growing preference for on-demand services, potentially creating more direct-to-clinician capabilities.

Urban also anticipates disruption in promotional review processes: "How do we make that brain power triage to the most important, really tough decisions and simplify the way that the stuff that's kind of the same and is lower risk is handled?"

Artificial intelligence will play an increasingly important role across functions. "We're already experimenting with how can we use AI in the med-reg-legal review process, in training, in data quality cleanup, in analytics," Urban shares.

Perhaps most importantly, Urban emphasizes the need for cross-functional thinking.

"That's one of my big missions in my team – that people are getting a flavor and a taste of all of the different components of this ecosystem between the data, the digital and the operations,"

she says. "The more we are thinking of that holistically across this whole area, the better off we're going to be anticipating and seeing around corners."

Key Takeaways

1) Start with the customer experience: "A big part of it is really starting with our customer as the first point. How do we think about that person's experience? The things that are making them frustrated or standing in their way."

2) Embrace difficult change management: "Standing true to the notion that ultimately, on the other side of those tough decisions, there's a better future for the organization and maybe even for those individuals who are impacted."

3) Adapt to the digital-native clinician: "Our clinicians are changing and that inherently means that the way we engage with those clinicians has to evolve with them."

4) Maximize each interaction: "The number of times we get in front of these clinicians nowadays is going to be a lot fewer per time period, so each one of those conversations needs to be that much better and that much more planned out."

5) Foster cross-functional thinking: "I want the entire team to be thinking about data, thinking about digital and thinking about operations, regardless of which place you are in your function."

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