In March 2018, the FDA adopted the ICHE6(R2) Good Clinical Practice (GCP) Guidance. A quality management system for clinical trials and the Sponsor’s requirement to properly oversee trial conduct are now a part of GCP.
Most clinical trial management approaches focus on whether the pages have been SDV’ed, number of open queries, and queries opened for a certain period of time. These metrics, along with other metrics such as whether onsite visits occurred as contracted have been used to manage trials for many years. They are easy to measure and easy to check performance against “goals”.
But do those metrics really indicate quality?
If we closely review the guidance and think about the goals of a quality management system, the important metrics become more obvious as follows:
||Evaluation Method Examples
|Study conduct follows the protocol.
||Identify and evaluate deviations to protocol conduct including patterns indicating issues to be addressed.
|All data meet ALCOA requirements (Attributable, Legible, Contemporaneous, Original, Accurate)
||Review of audit trail to confirm contemporaneous entry.
|Human Subject Protection is maintained
||Informed consents (IC) will be reviewed within 5 business days to assure all components of the IC process are followed correctly (in addition to confirming correct forms and dates)
|Data quality supports confidence in primary endpoint(s)
||Person assessing primary endpoint is identified in the data collector, is properly trained, and has been delegated the responsibility to perform the assessment.
|Investigational Product (IP) chain of custody and administration is complete and correct
||Data documenting site IP receipt, preparation, storage, dosing, return, and destruction are collected so errors can be quickly identified.
Moving to a quality management system or approach requires both processes and systems or technology to ensure that all aspects of trial conduct are evaluated (i.e., study processes, training, analysis data). Simply checking that analysis data is present and within the expected range is insufficient quality oversight.
Traditional metrics used today determine whether certain activities occurred. A quality management system focuses on determining what critical errors occurred and why.
For example, in a recent study, critical lab samples for a safety endpoint were received thawed from several sites. A traditional project management approach would be to send an email to the sites telling the sites their samples were received thawed or providing instructions that may not be based on a root cause analysis of the reason for the thawed samples. A quality approach would evaluatewhy
the samples were received thawed (I.e., a root cause analysis). This included evaluating how the instructions were provided, when and from where the thawed samples were shipped, the size of the shipping container, the amount of dry ice, the number of samples shipped, etc. Remediation included revising instructions based on the root cause analysis to assure future samples were received frozen, and follow up to assure that the revised instructions corrected the issue. This example shows how much more effective a quality management system is compared to SDV and traditional management systems used today.
In the mad dash to embrace quality metrics, ask first what SHOULD be measured not what CAN be measured.