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As the world emerges from the COVID-19 pandemic, pharmacovigilance (PV) teams face the same attrition and staffing crisis as other healthcare environments. However, timely detection of adverse events (AEs) remains an ongoing priority. If there’s anything we learned from the pandemic, it’s the critical need to analyze patient outcome data in real time to stay informed on the safety and efficacy of treatments in the real world.
Using artificial intelligence (AI) to automate data analysis can relieve some of the burden on PV teams—without the risk of compromising work quality.
Current methods used to detect adverse events create challenges for the PV community. Manual processes as well as voluntary reporting by patients, HCPs, pharmaceutical companies, EHR reviews, and patient interviews are all subject to under-reporting. The PV community began using AI and machine learning some years ago to combat these issues, but the trend has accelerated in the past two years.
This surge in uptake is partly due to the rise in telehealth and omnichannel reporting. For example, social media conversations involving COVID-19 vaccines burgeoned during the pandemic. In fact, more than 80% of customer interactions could move to digital in the future. With 50 million interactions per week about the vaccines recorded over a four-month period, the sheer volume of data shows just how much information is out there that requires analyzing.
Healthcare already generates some 30 percent of all data, and the volume grows exponentially every day. Global data is projected to total 181 zettabytes by 2025, an increase of 181.9 percent over five years. There are a couple of important points buried in that statistic:
Non-automated processes and a lack of sufficient integration between clinical trial management systems, safety systems, data management systems, product performance databases, social and digital data also lead to duplication and oversight, resulting in a wholly inadequate outcome for patient safety.
Automating AE detection through a combination of human and artificial intelligence offers the solution. These days, patients discuss prescription drug reactions on websites, forums, and social media channels, or they report them directly online or by telephone to patient support programs or medical providers. Wearable technologies monitor users’ physical responses and return data via their apps.
Despite these methods, regulatory agencies place the onus on pharmaceutical companies to monitor unstructured data sources for risk associated with their products. A digital solution using natural language processing (NLP) and voice detection technology that supports multiple languages provides a means of overcoming these challenges, enabling the PV community to detect AEs across platforms. Patients can report AEs directly in their own language using a website or mobile device and submit it straight to the safety system using end-to-end encryption. Data from CRM reports and chatbots also get added securely to the system.
Such a solution narrows the distance between the person reporting the AE and the PV reviewers and clears the runway of obstacles such as a high number of manual reviews.
The automation of the AE detection process offers substantive benefits for pharmaceutical companies, HCPs, and patients. These include:
Over the next few years, the pharmaceutical industry foresees growing quantities of data, increased regulatory compliance, and more complex reporting demands. Advancements in safety technology through the combination of human and machine intelligence will become essential for accurately identifying AEs.
Companies that lean in and embrace the opportunities afforded by technology to improve and maintain detection processes will ultimately provide better pharmacovigilance and ensure safer treatments for patients.
Marie Flanagan is Director, Offering Management, Vigilance Detect at IQVIA.