Today’s conversational AI agents – intelligent, automated software programs that mimic human language to respond to questions and share information – can facilitate conversations with HCPs that are as equally efficient and satisfying as conversations with human experts. However, knowing how and when to use AI agents for HCP engagement, as well as when to use human specialists, is the key to successfully overcoming the challenges of always-on, 24/7 HCP access to MI.
Benefits of AI-enabled HCP interactions
While the digital era has provided a plethora of benefits for HCPs, including constant access to MI and the ability to submit inquiries to life science companies around-the-clock, keeping up with demands for MI and responding to inquiries in a timely manner has been a challenge for life science companies. Employing human MI experts to respond to HCP inquiries at all hours is a costly approach due to often low inquiry volumes received out-of-house and can also lead to resourcing challenges when inquiry volumes surge unexpectedly. There simply aren’t enough trained human workers to manage MI request volumes for all volume scenarios, especially when an unexpected surge can cause inquiries to skyrocket in number.
When human experts are overloaded with MI requests, life science companies run the risk of providing a poor HCP customer experience through delayed responses and introducing the potential for human error. Automated conversational AI agents remove that risk by immediately responding to simple inquiries right away and flagging more complex inquiries that require human input. This not only reduces the workload of the human MI team but also provides them with additional time to review and respond to more complex inquiries that require critical thinking. AI agents, unlike human workers, don’t need to sleep and can continue responding to inquiries throughout the night, reducing the number of inquiries workers must respond to first thing in the morning.
While these advantages have been proven in real world situations, many companies are still hesitant to deploy AI for MI purposes. AI adoption rates, and the productivity of MI teams, can be greatly improved through better understanding of when and how to use a blend of AI agents and human workers.
Balancing AI- and human-driven HCP interactions
While AI agents offer numerous benefits to MI teams, life science companies cannot currently completely replace their human workers with intelligent agents due to customer expectations and preferences. As intelligent and human-like as today’s AI agents are, there will undoubtedly be inquiries that are too complex or sensitive for AI to handle, as well as situations where the inquirer would still prefer to talk to a human. Successfully integrating human workers and AI agents to best fit these requirements, rather than replacing one with the other, is the ideal approach to take.
AI is great at analyzing historic data and automating processes to increase productivity and provide valuable insights. AI is not as good at understanding and processing completely new information without historic context and access to relevant and approved content. That’s why human workers are still a critical component of MI activities and HCP interactions, which is expected to continue for some time to come. MI teams should look to use AI agents to rapidly respond to inquiries of a simple nature that require simple responses – especially if the inquiry is similar to previous interactions upon which the AI has been trained. Such simple inquiry types often form a large proportion of total volumes and so AI support provides a substantial operational efficiency. This approach ensures that HCP inquiries are responded to in a timely manner, while allowing human workers to maximize their value and apply their expertise to supporting more complex requests.
Overcoming barriers to conversational AI agent adoption
The biggest obstacle to AI adoption for MI teams typically comes in the form of challenges from senior leaders, who may be jaded by their past experiences with much more traditional “chatbots.” Early chatbots were rudimentary in their understanding of human language and their ability to process questions and so reduced overall customer satisfaction and did not produce an acceptable ROI on investment. It’s natural for leaders to be skeptical of using AI for MI activities if they equate today’s AI agents with such chatbots of the past. But today’s AI technology has proven to be far superior to first-generation “chatbots” in every way and is becoming increasingly accepted by a wide range of pharma customer types including HCPs. It’s now up to MI and IT teams in biopharma companies to work together to help their leadership recognize the benefits of implementing modern conversational AI agents.
Many biopharma leaders may also be convinced they can solve current MI inquiry volume challenges by outsourcing operations to call centers. While this approach may have worked in the past, it is untenable, as well as lacking scalability and flexibility from a long-term perspective to cope with ever-increasing inquiry volumes. It also prevents agile responses to increasing or decreasing customer demands and expectations, particularly since MI has moved from a traditional 9–5 Monday to Friday model to full 24/7/365 omnichannel access customer expectation. Using conversational AI agents to enhance the human team will also allow support for drastic increases in HCP inquiries without notice, as experienced during the COVID-19 pandemic, where a human-only model was often challenged and led to inquiry backlogs and other issues.
In addition to agility and cost considerations, local country and regulatory requirements must also be weighed when deciding whether to use a call center for MI purposes. MI teams, and by extension their AI agents or call centers, must be able to directly handle or translate MI from local languages to English to facilitate HCP interactions and allow global oversight. AI agents possess natural language processing (NLP) and translation capabilities that ensure any localized translations are accurate. Call center workers may have the ability to translate inquiries, but the accuracy and effectiveness of those translations will depend on human expert availability. AI agent support greatly enhances such HCP experiences.
Optimizing HCP interactions by blending AI and humans
In the case of MI teams, optimal customer experiences are the result of timely responses that contain relevant and accurate information to answer HCP inquiries. Human workers cannot keep up with the constant, 24/7 demands for MI and to ensure success life science companies must look to successfully integrate AI agents into MI workflows and processes. Along with the ability to scale MI operations to meet shifting demands, AI agents enable life science companies to maximize the value of their human workers by providing more time for critical thinking and carefully constructed messaging. Blending conversational AI and humans together is the key to success for MI teams today and organizations that successfully deploy AI agents will be best equipped to handle the next surge in MI inquiries as their market presence and customer expectations continue to grow.
Simon Johns has more than 25 years of experience supporting customer projects across all stages of drug development and the full product lifecycle. As director of medical information (MI) and marketed product safety at IQVIA, he has been managing global MI projects focused on process optimization and technology enablement that drive enhanced efficiency and customer engagement. Simon is a member of the European DIA Medical Information and Communications Training Team, advising pharmaceutical companies on best industry practices, innovation and automation. He speaks regularly on topics ranging from implementing suitable technologies and innovations to optimize medical information to the benefits of integrating MI and pharmacovigilance to increase compliance and product value, leveraging IQVIA’s Local Affiliate Product Services (LAPS), which provide full support for MI and local country pharmacovigilance requirements.