Automation in Pharmacovigilance: The Future of Drug Safety

Mar 02, 2023

Life Sciences | 4 min READ

This article was originally published in ET HealthWorld - Source link

Pharmacovigilance involves multiple checks, such as pre-clinical and actual trials, once the drug is introduced to the market. This process involves a large amount of data, sometimes unmonitored and unutilized.

Mukul Singhal

SVP, Global Head – Life Sciences (Delivery and Operations)

Birlasoft

Data in Pharmacovigilance: Rising Challenge

The ever-growing volume of data generated by clinical trials, drug usage, and other sources presents both opportunities and challenges for pharmacovigilance. Identifying and evaluating potential safety signals is essential to maintaining the safety of drugs and other therapeutic products. Big data analytics tools can help Pharmacovigilance professionals sift through large volumes of data to identify patterns and relationships that may indicate a safety signal.

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However, not all of the data gathered from multiple sources are equal. Clinical trial data, for example, is subject to Selection bias, Information bias, and Observer bias, which can all lead to inaccurate conclusions about a drug’s safety profile.

Drug use data can also help identify potential safety signals, but it is essential to consider the source of information. For example, patients may be more likely to inform about adverse events than those not taking the drug (confounding by indication). In addition, the information is often unstructured and unverified, which makes it more difficult to draw accurate conclusions. The bottom line shows rising data complexities resulting in misaligned and delayed informed decisions paving the way for intelligent automation to manage drug safety.

Automation in Pharmacovigilance

Automation in pharmacovigilance can help to improve the efficiency and accuracy of drug safety monitoring by tracking and managing a large amount of data.

Automation can track and report adverse events, monitor drug utilization, and conduct literature searches to ensure that potential safety concerns are identified and addressed on time. Automated systems can also generate alerts when new safety concerns are identified.

The Role of AI and ML

Artificial intelligence (AI) and machine learning (ML) in pharmacovigilance are still early catalysts but can potentially revolutionize the field. With extensive data at hand, AI can assist in pulling out data insights to facilitate intelligent information. At the same time, explainable AI can point out reasons to show how medicines behave after use.

It can be used to identify patterns in large data sets that would be difficult for humans to discern. For example, AI can identify previously unknown adverse events or drug interactions. ML can then be used to develop models that predict the likelihood of these events occurring.

AI and ML can also be used to improve the efficiency of clinical trials. For example, trial designers can target enrolment specifically at those patients by using predictive modeling to identify which patients are most likely to respond to a particular treatment.

This could save enormous amounts of time and money and reduce the number of patients exposed to ineffective treatments. With the potential benefits being clear, these technologies will likely play an increasingly important role in the field in the future.

The Business Drivers

There are many business drivers that are pushing pharmacovigilance into the realm of automation. The first and most obvious driver is the sheer volume of data that needs to be processed. With the ever-increasing number of clinical trials and the global nature of drug development, the amount of data that needs to be collected and analyzed is growing exponentially.

The second is the need for speed. In an industry where time is of the essence, being able to collect and analyze data quickly is a major competitive advantage. Automation can help with both aspects by reducing the time it takes to collect and process data. It can also bring transparency in the information for patient monitoring, spontaneous reporting, and greater regulatory control over patient data.

Finally, there is a growing need for cost efficiency. Pharmacovigilance is a costly endeavor, and as budgets continue to tighten, companies are looking for ways to do more with less. Automation can help by reducing the labor costs associated with manual data collection and analysis.

The Future

Automation is a future-forward step for medical companies and healthcare service providers as it optimizes the safety of data processing and workflows. With strategic innovation, AI shows promising aspects to heighten governance and safety in the industry.

As Pharmacovigilance continues to help manage risks faster- AI enabled case processing, cognitive learning, and literature review can assist companies in achieving early warning signals alerts, integrating services, and creating centralized information for greater visibility and access.

As companies strive to adapt to AI, the exponential growth in extensive data entails real-time data mining to gain actionable insights. To address the challenges, life science companies and healthcare providers must digitally transform with a future workforce. The transformation can be brought about by four key factors:

Process Standardization
Efficient Operations
Streamlined Analytics
Accurate Signal Investigations

Automation, AI, and ML are not new trends. They have been deployed successfully in industries including automobile manufacturing, utilities, and FMCG manufacturing companies in the past few years. The success of these technologies in other industries has paved the way for healthcare service providers and life science companies to explore the power of automation. As the technologies keep advancing, new tools to transform data processing, drug management, and governance will help serve patients with improved healthcare worldwide.

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