How Automation is Improving Case Intake and Data Entry in Drug Safety

In the world of pharmacovigilance, ensuring timely and accurate reporting of adverse drug reactions (ADRs) is a cornerstone of patient safety. Yet, one of the most resource-intensive and error-prone steps in the drug safety workflow is case intake and data entry. Traditionally performed by safety professionals or data entry operators, this process involves manually reviewing, validating, and transcribing information from various sources into pharmacovigilance databases. As the volume of individual case safety reports (ICSRs) continues to rise—driven by increased regulatory requirements, real-world data, and global reporting mandates—manual processes are proving inadequate.

Enter automation: a transformative force that is revolutionizing the case intake and data entry processes in pharmacovigilance. By combining robotic process automation (RPA), optical character recognition (OCR), natural language processing (NLP), and machine learning (ML), organizations are now able to streamline workflows, reduce human error, and scale operations—all while maintaining compliance with stringent regulatory standards.How

Automation Works in Case Intake and Data Entry

Automation in pharmacovigilance is not about replacing humans entirely—it's about augmenting human capabilities to handle repetitive, rule-based tasks at scale. Here’s how key technologies are making an impact:

1. Robotic Process Automation (RPA)

RPA uses software robots to automate repetitive, rules-based tasks such as:

• Downloading ICSRs from email or regulatory portals

• Logging into databases or safety systems

• Copying and pasting structured information

• Moving files between systems

RPA bots operate 24/7, eliminating delays and reducing human error. For example, an RPA bot can log into the FDA’s Electronic Submissions Gateway, download incoming ICSRs, and route them to appropriate folders—all without manual intervention.

2. Optical Character Recognition (OCR)

OCR is used to digitize printed or handwritten documents such as CIOMS forms, PDFs, scanned physician notes, or handwritten ADR reports. Advanced OCR solutions—especially those augmented with AI—can convert even messy handwriting or low-quality scans into machine-readable text.

Once digitized, the data can be extracted, interpreted, and entered into safety systems.

3. Natural Language Processing (NLP)

NLP allows systems to interpret unstructured text from narratives, emails, and literature. NLP models can:

• Identify and extract key data fields (e.g., drug names, adverse events, outcomes)

• Classify events based on seriousness or causality

• Perform sentiment analysis or contextual understanding

For instance, an NLP engine can parse a narrative like “Patient developed severe rash two days after starting Drug X” and correctly extract the adverse event (rash), its severity, and the suspect drug.

4. Machine Learning (ML)

ML models improve over time by learning from historical data. In pharmacovigilance, ML can be used to:

• Predict missing data fields

• Identify duplicates

• Classify reports (e.g., serious vs. non-serious, expedited vs. non-expedited)

• Improve entity recognition accuracy in NLP tasks

Together, these technologies create an end-to-end automation ecosystem capable of receiving, interpreting, and entering safety data with minimal human input.

Key Benefits of Automation in Case Intake and Data Entry

1. Speed and Scalability

Automated systems process cases significantly faster than humans. A process that once took 45 minutes per case can now be reduced to 5–10 minutes. As case volumes grow, automation can scale without proportionally increasing headcount.

2. Improved Accuracy

By removing human variability, automation ensures consistent data entry, accurate MedDRA/WHO-DD coding, and reliable case handling. NLP and ML models reduce interpretation errors in narratives.

3. Cost Savings

With fewer manual tasks, companies can reallocate human resources to higher-value activities such as risk assessment, signal evaluation, and regulatory strategy. Automation also reduces overtime costs during peak reporting periods.

4. Enhanced Compliance

Automated systems can enforce compliance rules programmatically, ensuring each case is complete, valid, and submitted on time according to regional requirements (e.g., within 15 calendar days for serious, unexpected ADRs).

5. Real-Time Monitoring

Automation allows for near real-time case processing, enabling earlier signal detection and risk mitigation. This agility is crucial in post-marketing surveillance and during drug launches or safety crises.

Use Cases in the Industry

Case Study 1: Large Pharma Automates Global Case Intake

A top-10 pharmaceutical company implemented RPA and NLP to automate 60% of its global case intake from spontaneous sources, reducing average processing time by 40%. The system extracted data from emails, scanned documents, and web forms, with human review only needed for exceptions.

Case Study 2: CRO Uses AI for Literature-Based ICSRs

A global CRO deployed AI-based tools to extract ICSRs from medical literature. The solution scanned journals, identified relevant case reports, extracted structured data using NLP, and routed them into the safety database. Human reviewers validated the output, achieving 90% time savings.

Case Study 3: Automated Translation and Coding

A biopharma company used NLP-powered translation engines to process non-English ICSRs and automated MedDRA/WHO-DD coding using AI. This helped meet regulatory timelines for multilingual submissions and standardized terminology usage.

Conclusion

Automation is reshaping how pharmacovigilance teams manage case intake and data entry, turning previously slow and error-prone processes into agile, scalable, and intelligent systems. By embracing technologies like RPA, OCR, NLP, and ML, organizations can unlock unprecedented efficiencies, reduce operational costs, and deliver higher-quality data for better patient outcomes.

Far from replacing human expertise, automation amplifies it—freeing up professionals to focus on critical thinking, scientific evaluation, and strategic decision-making. In a world where drug safety is non-negotiable, automation isn't just a competitive advantage—it's a necessity.

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