Surveillance of pharmacovigilance (PV) and product quality-complaint (PQC) data is not a new activity. For years we have known that adverse events temporal to drugs occur. Moreover, they are consistently underreported, impacting analysis of the data in unknown ways.
Nearly every regulatory authority across the world has guidance and requirements in place for marketing authorization holders to monitor the safety and risk/benefit profile of their products for potential “signals and trends” and for an honest assessment of the risk/benefit profile of the product over its entire lifecycle. The spirit of this monitoring is to alert the healthcare provider, regulatory authority, and the patient of any changes related to the product. This is to mitigate the risk of these events occurring in excess and minimize the severity. In PV, the main objectives are to keep patients safe, keep drugs in proper risk/benefit balance with the disease for which they are intended to treat, and to provide treatment options to patients quickly.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
In a perfect world, utilizing PV data in real time can improve patient outcomes by identifying potential drug-drug or drug-disease interactions, AE trends, or benefits not realized in clinical trials. The world is imperfect, however, and we are now rapidly moving to a more complex regulatory environment at a higher rate of speed. There are several ways to overcome the challenges of complex large volume data and resource constraints and still fulfill the responsibility of patient safety, which is at the center of all we do. In addition, we utilize PV data to be good stewards of organizational growth.
Despite technology advances supporting PV activities, not changing the way of working with PV data leaves most researchers struggling to obtain a deep understanding of the data. In analyzing the data retrospectively, if a change to the AE profile occurs, one simply cannot pivot early and control outcomes. The knowledge gap from the science of pharmacovigilance to the actual implementation into clinical practice still exists. Worse, the value of pharmacovigilance is underrated and still considered by many as a necessary exercise and nothing more.
This lack of vision in the value of the PV data is unfortunate. A unified community of mixed science, clinical, regulatory, and technology providers, can partner to overcome the negative cloud of the cost of good PV practices. View the data in a positive light to demonstrate that it is not only for patient safety, but it can be used strategically within an organization as a return on investment.
To learn more about the history of pharmacovigilance, current challenges and ROI opportunities, and how to remain proactive in the future, you can download our guide here. Or, you can submit the form below.