How Automation and AI will revolutionize the Medical Information function

Commercial

Executive summary

The life sciences industry is on the cusp of transformative change.  While technology capable of automating the medical information process is already largely available, the healthcare sector has traditionally relied on human oversight due to regulatory demands and risk mitigation, therefore not fully leveraging the potential of AI. Our industry leverages AI technologies 3 to 4 times less than other industries1, it is time for us to explore a fully automated future.

This paper aims at defining a medical information process as automated as possible leaving time for the medical affairs team to focus on value-adding interactions with HCPs and clinical colleagues.

Introduction

The medical information process encompasses the intake, classification, response generation, and delivery of information to healthcare professionals (HCPs), patients, and other stakeholders. Today, while some aspects of medical information leverage automation, human oversight remains central due to stringent regulatory requirements.

Looking forward, fully automating medical information processes is not just feasible—it is inevitable. In the coming years, technologies like natural language processing (NLP), AI-powered classification, and real-world evidence integration will enable a seamless, accurate, and scalable system. This paper assumes that full automation will be achieved and explores:

The Vision

What a fully automated medical information process would look like.

The Implications

The value this transformation will deliver to the life sciences industry.

The Path Forward

Steps the industry must take in the coming years to realize this vision.

The Vision: Fully automated Medical Information

Imagine a future where medical information requests, regardless of language or complexity, are handled seamlessly by AI-powered systems.

Key Components of the Automated MedInfo Process include:

Automated intake and transcription

AI technologies equipped with advanced voice and text recognition capabilities can process inquiries seamlessly across various communication channels, such as phone, email, and chat. These systems transcribe, translate, and standardize inputs from healthcare professionals (HCPs) and patients in real time. Leveraging language-agnostic natural language processing (NLP) models, they ensure that multilingual inquiries are handled effortlessly, eliminating language barriers and enabling accurate, efficient communication.

Classification and triage

AI systems achieve precise classification and tagging of inquiries, ensuring tasks like identifying adverse events and directing them to pharmacovigilance teams, as well as distinguishing between on-label and off-label queries, are handled with exceptional accuracy. These systems leverage machine learning algorithms that continuously refine their performance by learning from new data, enhancing their efficiency and reliability over time.

Content matching and response generation

AI systems efficiently match inquiries to existing, approved response content, ensuring quick and accurate resolutions. For more complex or off-label queries, these models go a step further by synthesizing clinical data, real-world evidence, and published literature to generate responses that are both compliant and highly relevant.

User-friendly delivery

Responses are seamlessly delivered through the requester’s preferred communication channels, such as email, chat, or interactive dashboards. To further enhance usability, AI enriches these responses with visual aids, concise data summaries, and dynamic charts, ensuring the information is both accessible and engaging.

The Implications: Transformational value for life sciences

Because it will allow Medical Information team to focus on their most value-added activities, automating the handling of medical information requests delivers value across three critical dimensions:

  1. Enhanced Outcomes for HCPs and Patients
    Automation delivers significant benefits by enabling faster access to accurate and relevant medical information, reducing the time healthcare professionals (HCPs) spend searching for answers. It also allows medical information teams to prioritize high-value activities, such as engaging in personalized, qualitative discussions with HCPs that enhance patient care. Ultimately, this streamlined access to data supports timely, evidence-based decision-making, leading to improved outcomes for patients.
  1. Better cross-functional collaboration and medical insight management
    Automation transforms data into a strategic asset by centralizing and analyzing information that is often scattered across third-party call centers or internal silos. AI-driven systems uncover valuable insights, identifying trends, weak signals, and emerging patterns that foster collaboration with clinical teams and empower R&D to accelerate drug development and enhance product quality. This deeper understanding of healthcare professional (HCP) and patient inquiries also enables proactive strategies, such as targeted educational campaigns and timely product improvements, driving greater value across the organization.
  1. Significant cost savings
    Automation significantly reduces reliance on third-party call centers, minimizing variable costs associated with inquiry handling and enabling organizations to manage these processes internally. By internalizing medical information activities, companies reduce overhead expenses while ensuring closer alignment with their strategic goals and operational priorities. Moreover, automated systems offer scalable solutions that can efficiently handle growing volumes of inquiries without proportional cost increases, providing a sustainable and cost-effective approach to managing medical information in the long term.

How did we get there? The path forward

Achieving a fully automated medical information process requires a deliberate and phased approach. Here are the key steps the industry must take:

Build awareness and advocacy

Make the case for automation: Medical information teams must articulate the long-term value of automation through compelling business cases. These should quantify improvements in efficiency, cost savings, and HCP satisfaction.

Educate leadership: Address skepticism by demonstrating the feasibility of automation and its alignment with regulatory standards.

IT compliance must adapt the traditional computer validation approach

Develop guidelines: Advocate for industry-wide standards that enable safe and compliant AI adoption.

Tailor CSV strategy to AI and automation: adapt your validation strategy and define how automation applications can be delivered in GxP setting, from assessing the risk, to qualification and documentation requirements.

Redefine operating models

Rethink HCP engagement: Shift the role of medical information teams from handling routine inquiries to offering strategic, qualitative interactions.

Optimize third-party partnerships: Reduce reliance on agencies while ensuring continuity during the transition to automation.

Implement incrementally

Start small: Begin with automating straightforward tasks, such as responding to on-label queries using pre-approved content.

Expand gradually: Progress to more complex tasks, including adverse event classification and off-label response generation.

Iterate and improve: Use feedback loops to refine AI systems and improve accuracy over time.

Focus on targeted AI applications

Define problems clearly: Identify specific challenges (e.g., high response times or inconsistent classification) and develop AI solutions tailored to address them.

Avoid generalization: While exploratory projects can provide insights, focusing on actionable problems delivers tangible value faster.

Conclusion

The automation of medical information processes represents a paradigm shift for the life sciences industry. In the coming years, AI-powered systems will transform how HCPs and patients access critical data, driving better outcomes, enhancing knowledge management, and reducing costs.

While the journey requires careful planning, collaboration, and incremental implementation, the rewards of a fully automated medical information process are too significant to ignore.

To prepare for this transformation, the industry can already act now:

  1. Develop a roadmap for transitioning to new operating models.
  2. Define the future of compliant automation and AI in GxP environment and adjust your system validation strategy.
  3. Invest in AI technologies and pilot automation projects with specific use cases.

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