MyRBQM® Academy | Webinars

Section 5.20.1 of ICH E6(R2) refers to root cause analysis for significant noncompliances in clinical trials. This should lead to corrective and preventive actions. Does this relate at all to section 5.0 on Risk-Based Quality Management? After all, isn’t a risk a potential issue? And where an issue has occurred, the actions taken to reduce the likelihood or impact of recurrence are risk controls, aren’t they? Can you use historical data on issues to help with risk evaluation and control? Join us at this live workshop as our two experts provide their perspectives on these questions and, if you’d like, take the opportunity to ask your questions, brainstorm examples, and join the discussion.

Key Risk Indicators (KRIs) are an integral part of Risk-Based Quality Management. They are one of the most important ways of detecting risks that are emerging as issues - so that you can take time to act and reduce the impact of those issues. In this interactive session, after providing a background, we will explore questions such as: How do KRIs link to the risk assessment? What does an effective KRI look like? What is the likelihood that a KRI indeed detects a risk increase? What is the likelihood that a KRI 'fires' but the risk did not materialize? How do you set thresholds? How are KRIs typically used? Can you change thresholds during the study? Can KRIs be muted if not needed anymore?

Part 1: Centralized statistical monitoring (CSM) is a way to look at data to detect any abnormalities and risks that were unexpected and unlikely detected via KRIs. What type of data are needed for such an analysis and what is it one can expect to detect? Is it only outliers or can CSM deliver more? What are the appropriate steps to take once abnormal data have been detected? We will demonstrate what such a CSM functionality can provide to the study data quality and the patient safety.

Predefined Quality Tolerance Limits (QTLs) are mentioned in ICH E6 (R2) section 5.0. As significant deviations to QTLs need to be reported in the Clinical Study Report, setting up QTLs can be challenging. In this interactive session, after providing background, we will explore questions such as: Are QTLs and KRIs related? How do you define a QTL with thresholds? How do you determine if a deviation is essential? Can you change QTLs during a study? Does it make sense to create QTLs for the study level, the country level, and the site level? How many QTLs are reasonable? 

We are required to implement a risk-based approach to quality management (RBQM) for clinical trials. The process has changed significantly from the traditional model of regular on-site monitoring visits. In this interactive session, after providing background, we will explore questions such as: How do you know if the process is working as you expected it to? How do you know if RBQM is having an impact? Should RBQM be able to control all risks, or should we also expect the unexpected? What other options are available to manage the unknown risks?

Part 2: Centralized statistical monitoring (CSM) is a way to look at data to detect any abnormalities and risks that were unexpected and unlikely detected via KRIs. What type of graphics are available and how can one interpret the various graphical displays of the data. What are the skills required of the staff reviewing those graphs? We will demonstrate what such a CSM functionality can provide to the study data quality and the patient safety. 

ICH E8 (R1) encourages us to create “a culture that values ​​and rewards critical thinking and open, proactive dialogue…”. Some are surprised that we need regulators to tell us to think critically. In this interactive session, after providing background, we will explore questions such as: What happens if critical thinking isn't used during RBQM? How can we encourage people to think critically as they define, prioritize, control, and manage risks? Does thinking critically mean we should throw out all those checklists? Do we need humans to work in an RBQM environment, or can't the machines/systems manage the risks for us? 

What's your organization doing to protect against fraudulent activity? Have you taken the necessary measures to reduce monetary loss, keep brand reputation high, and keep organizational efficiencies on track?

Either intentionally or unintentionally, fraud and error happen in clinical research. Even today, data manipulation and tampering are continuing issues that bio-pharmaceutical companies and clinical research institutions are trying to combat.

DO YOU WANT TO BE THE NEXT 'SCANDAL'?

Learn how intelligent, risk-based methods and technology can detect instances of data fraud as well as other data problems at an early, treatable time point and.....help you stay out of prison.

We will highlight and illustrate with real-life examples how to detect and prevent fraud and sloppiness by adopting a risk-based approach.