Automation Accelerates and Reduces Inconsistencies in Drug Development

Commentary Contributed by Emmanuel Belabe, ArisGlobal

January 19, 2024 | New drugs can take over a decade to receive approval. While drug development cannot be rushed, advancements in AI and automation can help life sciences organizations ensure shorter timelines to market, enhanced patient safety, and maximal resource utility. The accelerated approval of the COVID-19 vaccines and antivirals created opportunities to revamp existing practices and altered public expectations of reasonable time to market. While this occurrence was anomalous, it proved the industry can expedite its processes without sacrificing safety. 

Safety Team Challenges

Pharmacovigilance teams need to navigate numerous factors as they evaluate and process safety signals. The sheer volume of safety cases is one major hurdle, with caseloads growing 15% year over year (YOY) on average. This is compounded by increasing complexity created by: 

  • Growing datasets and sources. 

  • More complex drugs. 

  • New regulations. 

  • Expanded adverse event reporting. 

  • More global and diverse patient groups and partnerships. 

The complexity is exacerbated by an increased pressure on teams to deliver while budgets remain static. Traditional manual processes hamper signal detection amid the data deluge, making it challenging to conduct pharmacovigilance accurately, efficiently, and effectively. 

Benefits of Automation

Leveraging cognitive computing and real-world data (RWD) turns the plethora of available data into an advantage, unlocking nearly limitless insights and shortening the time-to-market by 500 days. About 75% of life sciences organizations believe automated risk management and safety signal detection offer substantial—even game-changing—benefits. 

  • Faster signal mitigation. 
    Safety teams using AI can spot and respond to signals much faster, quickly analyzing enormous datasets to identify patterns or relationships and enabling pharmacovigilance teams to transition from more reactive to proactive signal detection. 

  • Better signal detection accuracy. 
    An AI system dissolves data silos with a centralized repository so algorithms can analyze a complete dataset to find subtle trends humans might miss. Automation also eliminates the potential for human error, resulting in better detection accuracy and fewer false positives. 

  • Improved workflow efficiency. 
    Teams can optimize their roles by delegating data-heavy and repetitive tasks to cognitive algorithms, which maximizes efficiency and resource utilization and allows safety scientists to spend time applying their expertise toward more complex and value-adding functions, including signal detection and analysis. A recent survey showed half of all respondents believe automating manual processes is automation’s biggest advantage for safety teams. 

  • Ability to gain insights beyond immediate safety risks. 
    Automating signal analysis allows teams to look beyond causal relationships for adverse events. If the system can find safety concerns, it could also uncover correlations between a drug and a positive event or benefit. 
    Additionally, predictive safety signal detection could use statistical models to predict the likelihood of a particular adverse event occurring in association with a specific product or treatment. Signal detection management is currently moving in this direction, creating exciting new potential for pharmacovigilance. 

Automation Types 

PV teams looking at adding automation capabilities should consider these three options. 

  • Rule-based automation operates on a predefined set of rules and logical reasoning created by humans. This type is useful for handling repetitive tasks and automating simple decision-making processes. 
  • Knowledge-based automation (AI, natural language generation, or machine learning) uses a domain-specific knowledge base to reason and make decisions. These sophisticated algorithms unlock insights into large datasets. 
  • Knowledge-assisted automation (natural language processing or machine translation) combines automation with human insights and expertise. This approach helps organizations harness the power of AI while retaining the benefits of human critical thinking, creativity, and adaptability, yielding more efficient and effective operations. 

AI provides new ways to augment human performance and transform safety from a cost center to a strategic pillar of innovation. 

Considerations When Adopting Automation 

As with most major changes, automation requires time and effort to implement. These steps can help organizations successfully adopt AI and automation. 

  • Assess current processes.  
    Evaluate the safety team’s specific needs and pain points to understand essential tech functionalities and which processes would benefit most from automation. Leaders should also define objectives and set expectations for tool performance. One of the key challenges is resisting the urge to automate inefficient legacy processes as opposed to taking the opportunity to redefine the process to meet current needs. 
  • Incrementally implement. 
    When evaluating automation, start small with just a few of the more fundamental processes. Once teams gain experience, leaders can scale up. Gradually implementing tech empowers teams to refine each procedure for maximum value, instead of creating inefficiencies by trying to automate all processes simultaneously. 
  • Provide training and support.  
    Employees need comprehensive training to fully leverage the technology, understand its benefits, and develop comfort with the change. Ongoing engagement and communication addresses concerns, improves performance, and promotes buy-in from all stakeholders. 
  • Monitor and evaluate. 
    To achieve success, safety teams must continuously monitor and evaluate AI’s performance. Key metrics like processing times, error rates, compliance adherence, and resource utilization combined with stakeholder input help identify areas for improvement, refinement, or expansion. 

Pharmacovigilance automation holds immense potential. Reliable signal insights let safety teams work faster and smarter, help get medication into the hands of patients sooner, and drive enhanced drug safety. 


Emmanuel Belabe, better known as “Manny,” has worked within ArisGlobal in several different roles over his 18 years with the company. These roles have included positions as the Head of Customer Support, Head of North American Services, Head of Safety Product Management and his current role as VP – Solution Consulting. During that time, he developed an approach that sought to educate clients on best practices for leveraging ArisGlobal products while advocating the best way to deliver additional value through the implementation of additional ArisGlobal solutions. This led to his current role of Product Owner for Safety Solutions, where he is responsible for implementing tightly integrated tools that fit within the company’s vision of providing a unified platform across all R&D domains. He can be reached at  

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