Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
How Big Data Transforms Reactive Drug Safety to Proactive Pharmacovigilance
Posted on March 7th, 2019 by Marnix Wieffer in Pharma R&D
Adverse drug reactions can be costly. Prolonged hospital stays and clinical investigations cost as much as $30.1 billion every year — and clinical facilities carry much of that burden. And cost is just the tip of the iceberg. This is highlighted by an FDA website which cites troubling statistics such as the 100k deaths and over 2 million serious adverse drug reactions experienced each year: Adverse Drug Reactions: Prevalence and Incidence
In extreme cases, a drug’s regulatory approval may even be revoked, rendering a developer’s multi-billion-dollar investment , while maybe achieving some learnings, overall unprofitable and unsuccessful.
In response to these challenges, a growing number of clinical organizations are using drug and patient data to move from reactive drug safety toward a more proactive global pharmacovigilance system, in which adverse drug reactions can be anticipated before the drugs in question are ever prescribed.
Evolving Standards for Data Collection
The introduction of consistent systems for organizing electronic medical records marked the beginning of an industry-wide shift toward data standardization. Although this digital transformation has already brought many benefits in the realm of patient care, significant work remains to be done. Experts estimate that at least 6 percent of hospital patients experience at least one adverse drug reaction per visit — and many of those adverse drug reactions could be prevented by a more accessible database of pharmaceutical reports.
Bringing Together Drug Data
As more clinical providers adopt consistent policies for the reporting and organizing of drug data, this information can be correlated with manufacturers’ product updates and new regulations — as well as pharmacokinetic and genomic data gathered from millions of patients worldwide. When this data is brought together in a unified format, clinicians will gain access to a wealth of actionable insights about likely adverse drug reactions, enabling them to shorten patients’ hospital stays and provide safer care.
Tomorrow’s Global Pharmacovigilance System
Comprehensive and standardized data collection is only the first step. To accurately predict adverse drug reactions, clinical providers need to undergo a shift in thinking: from reactive drug safety protocols toward a proactive pharmacovigilance system. In such a system, data from other healthcare facilities, drug manufacturers, regulatory authorities, and other sources will be fed through predictive analytics algorithms, which will forecast potential adverse drug reactions and recommend alternative prescriptions. With the help of this emerging global data network, healthcare professionals can make strides toward more effective prevention of adverse drug reactions. As more providers shift toward a proactive pharmacovigilance paradigm, healthcare facility operators will enjoy lower operating costs and decreased risk — while billions of future patients can anticipate safer hospital visits.
Drug Safety Information Specialist
As drug safety information specialist, I support pharmaceutical companies in identifying potential drug toxicity concerns and adverse effects. During my first 3 years at Elsevier, I have been working with both academic and corporate customers in northern Europe. As senior marketing manager, I am currently responsible for Elsevier’s drug safety messaging, internal training and market engagement.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
Drug Safety Information Specialist
- Webinar: Using machine learning to identify adverse events from scientific literature
- Why literature is a valuable source for signal detection: From the EU point of view
- PharmaPendium team collaborates with pharma companies on drug-drug interaction risk calculator
- Elsevier uses machine learning to benefit pharmacovigilance
- The Role of Data in Propelling Clinical Trial Progress