In recent years, the integration of Artificial Intelligence (AI) into the pharma landscape has been nothing short of transformative, with the industry testing and implementing AI in a few key areas:

Drug Discovery: Improved initial assessment of drug compounds, predicting their success rates based on biological factors and advanced next-generation sequencing.

Pre-Clinical: Deeper comprehension of the impacts and outcomes associated with a particular drug.

Clinical: Optimized efficiency, inventory management, and service quality of clinical trials.

Manufacturing: Improved operations, efficiency, and production costs.

Commercial: Improved experience and communication between patients
and doctors.

Pharmacovigilance: Early identification of adverse drug reactions, higher data integrity and faster response times.

With today’s current possibilities, there is still so much more on the horizon for precision engagement, especially when combining it with the new and revolutionary Generative Pre-trained Transformers Artificial Intelligence (GPT AI).

So, what is precision engagement?

Precision engagement is a data-driven approach developed for marketing and sales needs. It uses customer insights to deliver personalized experiences. In the pharma industry, precision engagement can be used to present patients, healthcare professionals (HCPs), and other stakeholders with relevant information and support at the right time.

Is getting the customer’s attention the sole purpose of precision engagement?

No, in fact, it can be leveraged to spread the right information via an improved user experience, resulting in a better journey for both patients and caregivers.

• Offering the physician support from a virtual assistant during the differential diagnosis process where they have access to a more extensive knowledge and experience base. This can make a difference in all cases, but particularly with rare diseases.
• Allowing for people with health conditions (and their caregivers) to become more aware of the disorders they face, thus streamlining the patient journey and reducing the risk associated with following incorrect treatments.

What needs to be done before implementing AI in precision engagement?

First, it’s worth noting that this will not be a small change. Instead, it will be a transformational process that affects various functions of the organization, triggering considerations related to data privacy and security, regulatory compliance, and change management.

However, in keeping with the nature of this short article, I will highlight a couple of the most relevant activities:

Data Aggregation Strategy and Analysis AI-powered systems gather data from diverse sources. Some of them could be internal (CRM data and physicians profiling, etc.) but others must be external such as: electronic health records, prescription data, medical literature, social media, and more

Clustering and segmentation Once the data is aggregated, AI algorithms are employed to cluster the physicians or the patients into meaningful segments or groups. Multivariate clustering helps in identifying similarities and differences among physicians and patients, simplifying the process of tailoring engagement strategies to specific groups

Data governance A commitment to data governance is the foundation for delivering precise, incremental, secure, and compliant engagement experiences.

How is AI being used in precision engagement?

Pharmaceutical companies have a fundamental requirement to establish and cultivate relationships with various external stakeholders, such as:

• Company <> Healthcare Providers (HCPs)
• HCPs <> HCPs
• HCPs <> Patients
• Company <> Institutions

The customer journey, once a linear path from attraction to conversion, has evolved into a dynamic and multifaceted experience. This transformation is driven by the integration of Artificial Intelligence (AI) at various stages of the customer journey.

HCP Attraction and Engagement Physicians often find themselves involved in repetitive tasks that may have limited clinical significance. Consequently, they are compelled to reorganize their schedules, reducing the time they have for ongoing education and caring for patients. Therefore, the initial but crucial step in engaging physicians is getting their attention.

This process is relatively straightforward when high-value medical-scientific content is readily available. However, in cases where such content is missing, it becomes necessary to grant access to a wide array of materials. Importantly, these materials do not need to originate solely from pharmaceutical companies but can also come from external sources.

Implementing content factory approaches with AI systems, using Natural Language Processing (NLP) and text mining techniques, allows us to analyze and comprehend the content of third-party sources, perform automatic and incremental tagging, suggest specific content to user clusters, and, finally, employ machine learning algorithms to profile physicians.

This iterative process yields several benefits, including:

1. Creating clusters of customers based on actual physician behavior.
2. Facilitating cost containment and expediting the content approval process in accordance with regulatory compliance.
3. Generating “data-driven” editorial plans.
4. Enhancing effectiveness of engagement, thereby increasing the likelihood of reaching and retaining the target audience.

Medical Information Chatbots: Since 2020, all market analyses have demonstrated that in the New Normal, healthcare professionals require engagement from Pharma Companies not solely through push strategies but also through pull approaches.

Both push and pull marketing have their merits, and companies often use a combination of these strategies in their marketing efforts to reach a diverse audience and cater to different stages of the customer journey.

The Medical Information Chatbot (HCP virtual assistant) is one of the tools that has been most impacted by the advent of GPT AIs. This artificial intelligence-powered conversational agent is designed specifically for the healthcare and medical domain. In the past, multiple virtual assistants were launched by companies but encountered limited success for two primary reasons:

1. The overall experience was good but not as seamless as the HCPs were expecting. This poor user experience hindered the adoption and employment of the tool, a common frustration experienced by users of voice assistants who repeat information multiple times before giving them up.

2. Regulatory limitations, especially in certain countries. In this highly regulated Industry, it has become necessary to address and manage issues related to

• Data Quality: quality and integrity are crucial for compliance.
• Interpretability: ensuring transparency in AI decision-making.
• Regulatory Acceptance: pharmaceutical companies need to ensure that AI-driven solutions are accepted and validated by regulatory agencies.

The use of GPT AIs has transformed the user experience, as these new tools have been designed to make human-machine interactions as natural and flexible as possible. In fact, when healthcare professionals interact with the virtual assistants, they can refine initial searches, expand their scope by incorporating new and relevant elements, conduct in-depth investigations into specific domains or explore new areas by comparing them with initial searches.

Compliance concerns have also been addressed, particularly concerning specific project areas. One approach has been restricting GPT AIs’ access to data sources from private knowledge bases.

Transforming Commercial and Medical Functions with Precision Engagement

AI is reshaping the pharmaceutical industry, from drug discovery to customer engagement. Precision engagement, powered by AI, drives personalized experiences, improves healthcare, and streamlines processes across the industry. As AI continues to evolve, its impact on pharma is expected to grow, delivering benefits to patients, healthcare professionals, and companies alike.

This article is part of the Innovation as Strategy series.