Introduction
While Artificial Intelligence (AI) promises transformative efficiency in various fields, its role in
pharmacovigilance (PV) case intake—the process of collecting and processing adverse
drug event reports—comes with significant limitations. Many pharmaceutical companies are
now overcoming the initial hesitation and starting to rely on automation to streamline PV
case management.
With the advent of so many AI models and products mushrooming around these models
begs this analysis – are AI-based solutions really the silver bullet to case intake
automation? AI/ML models present a very interesting proposition for use in various use
cases, such as extracting ADRs, skimming through literature documents, and early signal
detection. The use of AI allows large volumes of data to be processed in much less time,
and it provides humans with insights and time to focus on more important aspects of the PV
lifecycle. However, purely AI-based solutions often introduce challenges such as
inconsistent outputs, data gaps, and poor scalability.
Why AI Alone Falls Short for Case Intake Automation
Generative AI and Large Language Models (LLMs) represent two highly dynamic and
captivating domains within the field of artificial intelligence. They have left the traditional AI
models far behind in terms of capabilities and accuracy in performing generic tasks.
Generative AI is a comprehensive field encompassing a wide array of AI systems dedicated
to producing fresh and innovative content, spanning text, images, music, and code. In
contrast, LLMs constitute a specific category of generative AI models, which are trained on
vast amounts of text-based data to generate human-like textual output. From a suitability
perspective, LLM would be more suitable for feeding an abstract or unstructured text for it to
identify and generate ADRs, and other AE data
AI-based solutions for pharmacovigilance struggle with certain inherent limitations:
1. Inconsistent Outputs: Even when the same model is fed identical inputs, AI
solutions can generate varied results. This happens due to model dependencies on
probabilistic algorithms, context interpretation, or slight changes in how text is
parsed.
2. Data Completeness Issues: AI models are often trained on historical data, making them vulnerable to missing or misinterpreting critical information in new contexts, such as non-standardized adverse event reports or abbreviations used in clinical data.
3. Handling Complex Inputs: Adverse event data comes from a variety of sources, including free text in emails, structured forms, and scanned documents. AI models often struggle to extract reliable information from such diverse input formats with precision.
4. Lack of Control and Transparency: AI solutions can behave like a "black box," where it’s unclear how decisions are made. Regulatory standards in the pharmaceutical industry require transparency for auditing and compliance, a need AI-only tools can fail to meet.
2. Data Completeness Issues: AI models are often trained on historical data, making them vulnerable to missing or misinterpreting critical information in new contexts, such as non-standardized adverse event reports or abbreviations used in clinical data.
3. Handling Complex Inputs: Adverse event data comes from a variety of sources, including free text in emails, structured forms, and scanned documents. AI models often struggle to extract reliable information from such diverse input formats with precision.
4. Lack of Control and Transparency: AI solutions can behave like a "black box," where it’s unclear how decisions are made. Regulatory standards in the pharmaceutical industry require transparency for auditing and compliance, a need AI-only tools can fail to meet.
These challenges collectively hinder the ability of pharmaceutical companies to consistently
meet compliance targets and ensure reliable drug safety reporting. Thus leading to the
reluctance of several other companies to adopt automation solutions altogether. Modern-day
AI models are much like young children who need to be closely watched, guided at every
step of the way, and their outputs constantly reviewed before being used for regulatory
reporting purposes.
However, what if there was a way to overcome the limitation of AI and instead use it
effectively to augment the abilities of an already robust solution? This is where the NOESIS
platform comes into play. Drogevate’s NOESIS Platform addresses these pitfalls by
combining AI with proprietary algorithms, delivering consistent and accurate case intake
that far outperforms typical AI-only approaches. NOESIS adopts a Human-in-command
approach where all the data flowing out of the system is duly controlled by humans on top of
the system.
NOESIS Platform: A Hybrid Approach by Drogevate
The NOESIS platform by Drogevate bridges the gap by integrating AI models with
definitive proprietary algorithms. This blend ensures that data extraction is not only
automated but accurate and reproducible, mitigating the inconsistencies typical of AI-only
solutions.
Key Features of the NOESIS Platform
- Proprietary Rules-Based Processing: NOESIS applies pre-defined business rules to extract data, ensuring a consistent structure regardless of input variations. This proprietary logic is tailored for pharmacovigilance needs, including handling E2B reports and clinical safety documents.
- AI-Augmented with Validation Layers: AI models support but do not solely determine the final output. NOESIS applies multiple additional layers to cross-check results, ensuring accurate and complete extraction of case data.
- Adaptability to Different Input Formats: NOESIS can handle inputs from PDF forms, scanned images, Excel & Word documents and emails while maintaining high extraction accuracy. This adaptability ensures that adverse event reports from varied sources are captured correctly.
- Consistent Outputs: The system ensures that the same input always yields the same output, in contrast to fluctuating AI-only solutions. This consistency is very important for business teams and decision-makers in adopting a solution for automation.
- Human Control: The configurability of the NOESIS platform puts the humans manging the system in the driver’s seat and in full control of the PV data. This is very important from a regulatory perspective.
- Scalable: NOESIS offers reliable performance even with high-volume workloads, making it suitable for global pharmacovigilance operations. NOESIS is carved out of a true cloud architecture which ensures automated rule-based scalability as per varying workloads.
- Cost Effective: The costs with NOESIS platform do not spiral out of control with increasing volumes, rather they diminish due to economies of scale coming into play. Even though cost may not be the primary consideration for many of the pharma companies looking for automation solutions, but it is an important one.
Comparative Analysis: AI-Only Solution versus NOESIS
Aspect
AI-Only Solutions
NOESIS by Drogevate
Consistency
Variable output for identical input
Always consistent results
Transparency
Limited interpretability
Fully traceable workflows
Data Completeness
Susceptible to missing data
Comprehensive extraction every time
Handling Input Variations
Struggles with unstructured formats
Handles diverse input formats easily
Scalability
May degrade with large workloads
Scalable with reliable performance
Cost Effectiveness
High upfront and per case costs
Per case cost diminishes with volume
Control
Autonomous/ Human-in-the-loop
Overseen as Human-in-command
Customisability
Requires model retraining with little or no customisation possible.
Allows configuration controls to align output inline with the organization’s Data Entry conventions
Overcoming the Challenges: Real-World Applications
The NOESIS Platform has already been successfully deployed across multiple customers
(pharmaceutical organisations and contract research organisations) for pharmacovigilance
process automation. The platform’s adaptability allows companies to quickly adjust
business rules for new regulatory changes without overhauling the AI models, a critical
advantage over AI-only tools.
According to industry reports, companies that rely solely on AI for adverse event intake
often face IT-related challenges, including system validation and infrastructure
limitations. Drogevate’s hybrid solution not only addresses these barriers but also
provides a faster return on investment by reducing manual effort and ensuring compliance
with minimal human intervention.
Conclusion
AI in pharmacovigilance is not a silver bullet. While AI provides valuable automation benefits,
relying solely on AI models for case intake introduces risks such as inconsistent
outputs and incomplete data extraction. Drogevate’s NOESIS Platform offers a hybrid
approach that blends AI with proprietary rules-based logic, ensuring accurate, consistent,
and reliable data intake every time. This solution enables pharmaceutical companies to
scale operations efficiently while maintaining the highest levels of compliance and
transparency. As pharmacovigilance demands grow, such hybrid platforms represent the
future of reliable case intake automation.
For more information, you can explore the NOESIS Platform directly and learn how
Drogevate’s expertise in drug safety can transform your organization’s pharmacovigilance
capabilities.
