AE Detection: Upgrading from an Exacting Task to an Automated Utility

In this article, Alison Sloane, general manager, vigilance detect, IQVIA, addresses the challenges pharmacovigilance teams experience. She further discusses what to do if they incorrectly forecast adverse events (AEs) and how automation, AI/ML, and natural language processing (NLP) can help them scale to manage hundreds of thousands of additional AEs.

COVID-19 vaccines remain under intense public and regulatory scrutiny. During their short time on the market, these vaccines have shined light on the critical need for reliable long-term safety monitoring and an established safety profile. Even after treatments and vaccines are approved post clinical trials, they must be monitored closely and continuously following market distribution for patient safety and regulatory adherence. Adverse events (AEs) occur when a patient has a negative reaction or experiences an adverse effect because of the medication they are taking. AEs must be monitored, collected, and analyzed so that pharmaceutical companies stay on top of any potential safety or efficacy concerns surrounding their drugs.

Pharmaceutical regulators require pharmaceutical companies to be aware of any potential AEs, as well as have documentation proving that the pharmaceutical company is following regulations to document and report any AE and compliance risks. However, as patient data sources have expanded to include unstructured formats such as social media and online forums, the amount of AE data pharmaceutical companies must analyze to remain compliant has skyrocketed. Current manual efforts to detect, collect, analyze, and glean insights from patient data are challenged to keep up with the volume or speed of external data. But intelligent technology solutions can help pharmaceutical companies remain compliant by supporting AE research and operations.

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How to Monitor AEs

When drugs first go to trial, companies make predictions surrounding the volume of specific AEs they expect to see based on the type of the product, the composition of the drug, and its purpose and intended effect. These details are compared with data that has been collated on the constituents of the compound, comparable compounds, and the indication the drug is intended to treat, which then inform companies of what potential AEs they can expect for the new drug. This not only helps regulatory agencies determine the level of patient safety data they can expect but also shows that the pharmaceutical company is aware of and prepared to manage the AEs once they occur. 

When it comes to vaccines and drug treatments, the volume of AEs that must be identified and monitored can differ greatly. Vaccines are either seasonal – like a flu vaccine – or only required once over a period of several years – such as a Tetanus shot – while drug treatments can be prescribed either continuously or as needed. Therefore, you can expect a much higher prevalence of AEs in ongoing treatments that take place over an extended period. So, there will be a much higher rate of AEs anticipated for oncology treatments than there would be for treatments of something more benign, such as ear infections.

Today’s AE Tracking Challenges

Today, it is becoming more commonplace for patients to discuss their drug reactions on social media and other online channels or report their experiences directly to commercial or medical information call centers. Patient data is also now available via wearable technologies such as smartwatches that monitor the user’s heart rate and breathing. 

To remain compliant with regulatory requirements and maintain patient safety, pharmaceutical companies must also monitor their unstructured external patient data sources to make sure they process and report AEs associated with their products. The issue is compounded when you consider the growing adoption of the company-owned social media pages, websites, virtual assistants (formerly known as chatbots) and other forms of digital B2C communication. Regulators require companies to monitor these pages and respond to any patient or healthcare provider (HCP) inquiries surrounding drug AEs.

The pandemic has also increased the availability of virtual patient appointments with HCPs to comply with social distancing mandates. Regulators are constantly updating guidance and legislation to ensure companies stay abreast of drug safety, regardless of the medium in which the events may be mentioned: in-person, digitally, audio recordings, etc. The explosion of available patient data will only continue to increase in volume, and constantly shifting regulations mean that manually monitoring AEs across every medium of communication requires serious consideration.

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Technology Provides a Better Path Forward for AE Detection

The scale of available patient AE data might tempt companies wary of technology solutions to simply throw more bodies at the issue to ensure that AE detection and identification is performed correctly. However, they may not realize that there is an option to deploy automated, scalable solutions developed by a partner with an intimate understanding of the life sciences and pharmaceutical space. An in-depth understanding of pharmaceutical processes and regulations enables partners to develop specialized solutions to industry-specific problems, such as AE identification. Additionally, software solutions don’t take sick days or require downtime. So, they are always working to ensure that any AEs are recorded and reported for analysis.

Many pharmaceutical companies operate in markets around the globe, with varying regulatory requirements. The large number of languages spoken around the world also presents an obstacle for AE identification, as any recorded AEs must be translated before they can be compiled with other AEs from around the world for analysis.

An automated AE detection and identification solution that leverages natural language processing (NLP) to identify an AE in any reporter’s native language, whether patient, healthcare professional, or sales rep, for example, will ensure timely and efficient detection of AEs. Manually translating and analyzing patient data from multiple countries requires expending significant human resources with various levels of expertise surrounding medical terminology, foreign languages and pharmaceutical processes. Eliminating this manual effort opens huge opportunities for AE detection and identification costu-saving, freeing up experts to focus on downstream processes such as case processing and reporting to health authorities.

Deploying an automated solution to manage AE detection enables increased reporting compliance, increased speed and quality than manual data aggregation and translation and reduces regulatory risk. By creating a centralized, standardized process to ease the worry of AE identification, technology solutions can streamline the entire process across a pharmaceutical enterprise. Companies that leverage automated AE identification capabilities today will be in a better position to find AEs and ensure Compliance with Regulators. Early adoption of automated AE identification solutions will also set companies up to more efficiently adopt new technology capabilities that are coming to market and reap their opportunities to improve data and mitigate manual tasks, such as audio file processing. Ultimately, technology is a catalyst to enabling better business practices. In the healthcare industry, better business practices mean safer treatments for patients.

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