In today’s world of empowered patients and increased attention to drug safety, the role of pharmacovigilance has never been more crucial. Healthcare organizations need to instill robust practices to detect, assess, report on and prevent adverse effects, both to ensure regulatory compliance and reduce risk for patients.

Pharmacovigilance processes, however, are traditionally highly manual and resource-intensive. Information on adverse events can be gathered during clinical trials and also following the drug’s market release. As such, adverse events are reported across the globe in multiple languages and formats and in structured, unstructured and handwritten documents from affiliates, partners and distributors. In addition, companies must scan scientific literature to identify, extract and capture additional adverse event-related information.

Typically, large pharma companies receive anywhere from 300,000 to 500,000 adverse events a year. These documents are processed manually by large teams that identify and extract relevant information and enter it into the safety system. This is followed by quality and medical review before the data is reported to regulatory bodies.

A Changing World

Regulatory pressures, patient awareness and new data sources such as social media will exponentially increase the volume of drug safety case processing in the coming years.

While compliance and patient safety are equally important, pharma companies need to shift their focus from safety case processing to proactive risk management. By using automation and artificial intelligence (AI) technologies, pharmas can reduce the effort and spend required for safety case processing, thus freeing resources to focus on proactively managing risk.

Enter Intelligent Automation

While there are IT systems and applications that automate case processing and reporting activities, the overall process still requires much human intervention and manual effort, especially in the areas of case intake and data entry. The rules-based, repeatable and deterministic nature of the process, however, makes it a suitable candidate for automation by using technologies such as natural language processing (NLP), machine learning (ML) and robotic process automation (RPA).

The complete process, from case receipt to reporting, can be automated, thereby limiting the amount of human intervention needed for exception handling, quality checks and reviews. By automating the process end-to-end, pharma companies can free up resources (both financial and human) that can be utilized for more productive and proactive safety surveillance activities.

Automating Individual Case Safety Report Creation

Because most popular safety systems do not offer automation capabilities, interested companies can instead start the automation journey by building RPA-based bots that automate data extraction from documents and enter it into the safety system.

For example, we are building an RPA-based automation solution for individual case safety report (ICSR) E2B case processing for a large European pharma company. ICSR case processing typically requires manual extraction of data from a source document and, post verification, entry into safety data for case creation. Our proposed solution will automate the source data extraction, validation and system data entry activities.

Once deployed, we estimate the bot will reduce manual effort by 20% to 30% and decrease the turnaround time for case creation by 50% to 60%, while ensuring zero data entry errors.

For now, the solution supports only data extraction from structured documents. However, we are in the process of building another solution that will extract data from unstructured and hand-written documents, and provide language translation capabilities.

Advancing Pharmacovigilance

Employing automation in safety case processing will not only reduce costs and accelerate processing; it will also eliminate the chance of human error and improve quality and accuracy. Cost savings in case processing can then be utilized to establish a comprehensive pharmacovigilance system that is global in nature, and utilizes modern technology to capture and analyze new sources of medical information that will transform the current reactive system into a more proactive risk benefit management system.

 

Harpreet Kanwar

Harpreet Kanwar

Harpreet Kanwar is Chief Technology Officer within Cognizant’s Life Sciences business unit.  He is responsible for driving innovation, emerging technology adoption, architecture... Read more