Pharma today embraces the concept of patient-centered research. Innovators view Patients as informed collaborators whose participation is core to the overall success of clinical research. The risk-based Patient Profiles tool is a key contributor to the modern concept of patient-centered thinking, as It enables early detection and real time control of patient safety issues.
Adverse Event (AE) reporting, identified by the FDA as one of the most important challenges in clinical research, is known to be prone to under and over-reporting. When starting a project and considering the various ways available to capture the adverse event data, it’s important to ask yourself some analytical questions that help you improve AE and SAE capturing and reporting.
Blockchain technology is the biggest achievement in cryptography of the past decade. Source Data Verification (SDV), which consumes about 20–30% of the clinical trial budget nowadays, will become redundant when blockchain technology disrupts the pharma industry.
Quality is a business decision. Everyone instinctively knows that poor quality doesn't come cheap, however until now no attempt was made to calculate the real difference between good and poor quality in clinical trials. Most of the issues that impact cost and expenses are lurking just below the tip of the iceberg.
It is widely broadcasted that pharma companies will have to accelerate adoption of adaptive clinical trial designs to reduce study timelines and costs while increasing success rates. Risk-based Monitoring “Real RBM” integrates the Adaptive Monitoring (AM) process, which addresses all aspects of Quality Risk Management.
Believe it or not, it was Guinness’ biochemist Gosset who developed the first Six Sigma statistical test in the early 1900s. The Guinness brewery was far ahead of its time by hiring statisticians, chemists and other scientists to improve the quality of its beer.
In our previous "AI-driven predictive analytics in RBM" article, we started a discussion about algorithms of machine learning (ML), predictive analytics, and artificial intelligence (AI). We also covered that a risk software needs to calculate forecasts of Key Risk Indicators (KRIs) proactively and alerts [...]
AI-driven Predictive Analytics is a very useful tool in risk-based monitoring and overall risk-based study management. It increases the proportion of correct decisions once the decisions start to become more data-driven. It also helps to understand for a central CRA or study manager the [...]
How to avoid audit findings in a Risk-based Quality Management (RBQM) and risk management environment? What are auditors looking for?
Risk-based Monitoring jargon doesn’t require rocket science. You don’t have to be a statistician to analyse and interpret KPIs, KRIs and KQIs. We explain their meaning and differences through simple examples, so you can optimise key performance of your clinical trials.
