Three Steps to Revitalizing the Life Sciences Industry with AI-Enabled Technologies

The pharmaceutical commercial landscape has become much more complicated in recent years, as patient, healthcare provider (HCP), and stakeholder needs have evolved. In today’s competitive environment, traditional multichannel healthcare provider engagement strategies do not meet the demands of the marketplace.

Our world is changing–so are patient and provider needs

Over the past 18 months, the life sciences industry has undergone a global transformation. The COVID-19 pandemic ushered in a turning point in how pharma companies operate, forcing patients and HCPs to increasingly rely on technology for healthcare needs. Traditional engagement models were disrupted as in-person access to physicians became severely limited in the context of COVID-19 lockdowns. This left customer-facing teams no choice but to evolve to digital interactions, leading to a 500% increase in the use of remote channels to engage HCPs, according to IQVIA’s Institute for Human Data Science. Since many HCPs discovered that they ultimately prefer a virtual experience, successful pharmaceutical companies must now adapt accordingly.

While core capabilities haven’t changed, the singular model of commercialization that was once standard across the industry has completely transformed. The former model, typically viewed as “one-way” or “pushed,” has evolved into a “pull”-based model almost overnight. Now, engagement needs to occur on the HCP’s terms–in most cases, digitally. Pharmaceutical representatives once walked through the doors of hospitals and doctor’s offices to educate HCPs about a new medicine or vaccine. Now, they interact with healthcare providers in a radically improved way by leveraging embedded intelligence technologies such as AI, analytics and automation.

Take Netflix, for example. Legacy paid content is one-way, or ‘pushed’ to the viewer. The viewer watches what the provider has made available. Netflix on the other hand encapsulates the pull-based model by leveraging analytics to provide a digital, on demand product. This same concept enables the healthcare representative to provide the HCP’s with a superior experience based on preferences. In the life science industry, pharmaceutical products must be as customized and conveniently available to HCPs, just as Netflix programming for its customers. Pharma companies must adapt and deliver content and information in whatever capacity an HCP prefers. Companies choosing to embrace AI and ML analytics in their business practices now have the opportunity to reimagine their approach to HCP engagements.

What does this mean for the future of pharmaceutical marketing?

As the industry demands new ways to discover, deliver and receive personalized and affordable care faster, patients and providers now seek cohesive engagement experiences with 24/7 access to personalized information. Personalization requires customized engagement models since customer preferences vary from face-to-face interactions, digital interactions and hybrid interactions. The challenge for the life science industry is to optimize resources across each of these mediums. A key determinant of success for this personalized strategy will be interaction quality. This requires teams to consider insights beyond mere demographics, such as: What time of day is best for the customer? Do they prefer to be contacted by phone or email? Knowing their customers are historically accustomed to engaging face-to-face, pharmaceutical representatives must adjust strategies and utilize insights to deliver quality interactions.

What will be the driving factors for success in this new commercial landscape?

Commercial leaders of today and tomorrow will be those that adapt quickly to a healthcare future that prioritizes patient and HCP experience.

To meet the pace and scale of today’s evolving commercial landscape, the industry must adapt in these three ways:

1: Reinvent HCP engagement with AI/ML powered insights for targeted, personalized outreach

One of the major changes occurring in the pharmaceutical marketplace is that demand is shifting from lower cost universal drugs with huge potential patient populations to more high-cost, targeted therapies that treat smaller populations. According to data from IQVIA’s Institute for Human Data Science, the cost of targeted therapies ranges up to $300k per patient, per treatment. Pharmaceutical companies can no longer rely on mass appeal or individual HCPs to prescribe the same drug to hundreds of patients. With a more niche consumer base, a more sophisticated, personalized outreach with each HCP will enable better treatment decisions. Thus, establishing a single view and message is critical for campaign planning and execution, as HCPs now expect seamless, personalized engagement.

Clean, consistent customer data will be the foundation of these strategic engagements. For meaningful interactions, the data must factor in and contextualize all previous engagements with the HCP. As HCP preferences evolve and adapt over time, their engagements will become more personalized as real-world feedback is continuously incorporated to deliver more precise insights. This ultimately shows the customer that a brand values their relationship and is prepared to personalize messaging to meet their needs. With a more holistic and single view of HCP preferences and behavioral patterns, companies will be better equipped to improve their overall engagement and respond to changing preferences with agility.

2. Inspire company adoption and use of embedded intelligence for improved customer experiences

Success will depend on an organization’s ability to understand, optimize, and leverage personalized HCP and patient interactions across digital and real-world channels. AI-driven recommendation platforms uncover precise insights about HCPs and their patient populations to maximize the engagement. These detailed insights include an HCP’s preferences, background, conferences attended, papers written, what drives their treatment decisions, which forms of messaging they respond to, the time frame they are most likely to respond, as well as insights on their patient populations. This method delivers the right message, at the optimal time, informing better treatment decisions.

Once these insights have been identified, organizations should view interaction quality as a key determinant to their success. Were the customer’s needs identified? Were their questions answered? Were they reached via their preferred medium–phone, email, video call, face-to-face, etc.? Were they satisfied with the interaction?

To optimize the customer experience, these AI-driven insights must be embedded within business processes, so customer facing teams do not have to disrupt their work and go to their inbox for recommendations. Data management solutions enable companies to remove data siloes and uncover growth opportunities. Consolidating HCP data from disparate systems, data management solutions create a single source of truth to inform the organization’s HCP engagement strategy. By removing these siloes, organizations create accurate, integrated HCP and patient views (a profile of the HCP and their preferences) that enable the delivery of timely and contextually relevant HCP engagements. Companies can then confidently create general and predictive analytics that reveal previously hidden customer insights for better decision making.

3. Report and integrate feedback into the business through a smart, continuous learning loop built on data-driven analytics for operational scalability and transparency

One of the most challenging aspects the industry faces in executing these transitions is getting pharma customer teams to abandon their former, less sophisticated guidance models and adapt to a new approach. This is often difficult because customer facing teams want measurable proof of results before they risk changing their strategies. Since feedback is necessary to continuously guide the algorithm’s improvement, companies must incentivize their teams to rate the suggestions provided. Then, to maximize success, companies should compare and report these results across the enterprise to validate whether the analytics are effective and continuously improve the algorithm for better insights.

Finally, life sciences companies should identify how they intend to measure value and return on investment. Decision makers should focus less on how many end users are on the system, how many alerts are sent out, or how many times people clicked on those alerts. It is far more important to define metrics with business value. For example: Did the team uplift the number of successful digital sales calls? Were more HCPs progressed through the marketing funnel? Was there a Reduction in compliance issues with HCP engagements? Was there an increase in prescriptions? Once pharmaceutical companies identify their goals and verify the benefits of this new model, they can expand their success across multiple brands, therapy areas and countries.

Conclusion

As we look ahead to what seems like a perpetually unpredictable future, industry leaders must now adapt to the realities of a socially distanced, uncertain healthcare environment through a virtual transformation. Personalized consumer engagement models that cater to customer preferences will define the future for the life sciences industry. With AI and MH insights powering their engagement strategy, companies will yield more profitable results while providing better insights to physicians and ultimately, more personalized patient care.

Tanveer Ahmed Nasir, Senior Director of Global Technology Solutions at IQVIA