Addressing The Data Challenges Of Pharmacovigilance

Increasing data volumes as well as increasing data complexity are currently forcing the drug safety industry to look for solutions to reduce case processing costs while remaining compliant with continually changing regulations worldwide, as well as maintaining or even improving the quality of information contained in individual case safety reports. As pharmacovigilance adopts next-generation technology by leveraging artificial intelligence (AI) and the cloud, new possibilities are opening up for knowledge generation – and thus value – from the data collected and processed.

Cognitive computing has been changing the world significantly. Many industries have been employing such technologies effectively and efficiently for some time now. The adoption of cognitive computing has also started in the medical sciences yet key questions about how to integrate it successfully into the various global healthcare systems need to be addressed. In order to achieve successful integration, there is a need to overcome technical and medical limitations, as well as regulatory obstacles. In addition, ethical concerns, in particular regarding the safety and integrity of data, need to be resolved. Several areas of medicine are unthinkable without the application of cognitive computing and artificial intelligence, for example personalized medicine. The sheer depth and wealth of data required for streamlining patients to allow them decision-making on the optimization of their personal health benefit-risk balance in light of their health state, their lifestyle choices, genetic and metabolomic factors, and preventative and curative medications, require advanced computational approaches. At the same time, the tendency to oversell the technology needs to be curbed and a rather realistic, step-wise approach should be followed to successfully implement and optimize cognitive computing and artificial intelligence approaches in order to really meet the needs of stakeholders.