Harness the Power of Intelligent Automation in Drug Safety

- Slobodan Vucinic, Senior Business Consultant, NNIT

The advent of advanced technologies like Robotic Process Automation (RPA) and Artificial Intelligence (AI) has opened up new horizons for enhancing pharmacovigilance and drug safety processes.

With the current developments in healthcare regulations, clinical research and novel treatments, it is crucial to prioritize patient safety. Right now, life sciences companies are being challenged by an exponential increase in the number of individual case safety reports (ICSRs), shifting regulatory requirements and a virtual tsunami of data that needs to be processed, analyzed and acted upon.

As in many other industries, the increasing maturity and availability of artificial intelligence (AI) appears to be a promising method for handling these challenges. Indeed, numerous proven use-cases exist for applying AI to drug safety, driving efficiency, accuracy and proactive risk management. But before you let AI take over the management of your pharmacovigilance inbox or task it with correlating data for periodic reviews, you need to understand both the risk and limitations concerning the use of AI in a drug safety setting.

– Intelligent automation has untapped potential to reduce human error and repetitive tasks, improve both quality and response time in handling serious cases, and boost overall efficiency of pharmacovigilance. However, unlocking this potential requires a deep understanding of both the technology and the impact on existing drug safety systems, business practices and regulation, says Slobodan Vucinic, Senior Business Consultant, Drug Safety Consulting at NNIT.

Applying the right tools for the task

When discussing intelligent automation, the broad term of “AI” is often used to describe several distinct technologies, each of which has its own characteristics, limitations and use cases.

Robotic Process Automation (RPA) is the automation of business processes using software robots. This technology is well suited for handling repetitive rule-based tasks with a high degree of predictability and standardization, such as importing data from an XML document.

Artificial intelligence (AI) is the umbrella term for multiple technologies that are able to simulate human reasoning, decision-making and learning. An AI model can carry out more complex assignments but typically needs to be trained to perform a specific task, and this requires large, high-quality data sets. Some of the most relevant AI technologies in a drug safety context are:

Helps computers understand human language by extracting and interpreting text from unstructured sources (such as an e-mail or text document) and understanding important aspects to create meaning.

Is a computer process that automatically transforms structured data into a written or unstructured narrative.

Recognizes characters within a digital image. It is commonly used to recognize printed or handwritten text in scanned documents.

Automation use cases in drug safety

While most steps in a typical pharmacovigilance (PV) workflow for processing ICSRs must still be performed by trained professionals, there are often multiple opportunities for implementing automation during case intake, case processing and reporting.

Automating parts of the PV workflow enables a pharma company to significantly reduce the need for manual handling of routine tasks, thereby freeing up resources that can be devoted to more complex and time-consuming activities, which ultimately has a positive impact on patient safety, signal detection and profitability.

Sample use cases include:

Each new ICSR must be checked to determine whether it is a new case or related to an existing case. This process can be automated using a combination of OCR and NLP to identify unique case identifiers (such as a handwritten case number or patient ID) combined with RPA to carry out a cross-check against the PV database and record the outcome.

Case triage is an example of a step where a combination of RPA, NLP and OCR can be employed to perform a “first pass” of incoming cases to tag them as either serious or non-serious before they are evaluated by a trained human. This helps ensure that serious cases are given priority.

Using a simple template, NLG can be used to auto-generate written narratives for cases based on the available case information, thereby greatly accelerating case processing. Similarly, NLP can convert unstructured data into structured data, and check to ensure compliance with data privacy regulations.

Once a case has been completed, NLG and RPA solutions can be used to compose and automatically send follow-up letters to reporters to request any missing information. This includes sending periodic reminders if the reporter does not respond within a specified time frame.

A robot can be employed to check reports and input fields against specific business rules before submission, thereby eliminating numerous common errors. For example, an automated check would catch if a stop date was prior to a start date, if a required field was left empty or if a reporter did not have the required qualifications.

Understand the risks and limitations

Considering the highly regulated nature of pharmacovigilance and drug safety, and the potential impact on patient safety, it is important to thoroughly evaluate the risks and take the necessary precautions before implementing intelligent automation.

– Among the key things to consider are the technical skill level of your end-users, data protection and maintaining business continuity. Changes in pharmacovigilance procedures impact multiple other downstream processes, such as aggregate reports or quality management, and it is vital to ensure that any unforeseen problems do not cause you to miss reporting deadlines or bring your regulatory compliance into question. An experienced partner can help you manage these risks, but you need to be aware of them ahead of time, says Slobodan Vucinic.

Mitigating the risks requires extensive knowledge of AI technology, drug safety systems and life sciences business processes. Fortunately, expert insights and meticulous planning can effectively unlock the benefits of intelligent automation in drug safety without risk to patient safety or your business as a whole.

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Interested in knowing more about how NNIT can help with automating drug safety processes using RPA and AI? Feel free to contact us with any questions.

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