Jenya is a co-founder and VP Clinical at Flaskdata.io. Jenya has a masters degree in biotechnology from the Hebrew University and is a doctoral candidate at Tel Aviv in medical science. She is GCP and CRA certified and leads FlaskData.io customer operations with super-human devotion to customer delivery. Jenya has 2 children – Adam and Adel.
Clinical trials are based on collections of time-based clinical data. If the dates and time-stamps in the data set are low quality, everything else will be low quality: measurement of study progress, enforcement of visit protocols and study schedules, measurement of site progress and any clinical parameter that is a function of time, such as cumulative dosing, pregnancy and hundreds of other time-based use cases.
Jenya talks about bad dates and how really bad quality dates that can spell disaster for your clinical trial – and suggests what do to about it.
I think everyone knows that secure and accurate clinical trial data, no matter where and what data, should be correct, well written, accurate and qualified. Without that, no one will trust your data, your results and all your hard work will be non-appreciated by others and too frustrated to you. Today I’d like us to be more specific and focus on date quality in clinical trials. Dates in clinical trials are used in every visit, in any form, and across changes. I think that we can all agree that your clinical data will be full of date and time information.
Do we take date quality for granted?
The importance of date information for remote monitoring.
Date/time issues can be visualized as a triangle.
Never underestimate the capability of people to make mistakes
The consequences of low quality dates in clinical trials
How to resolve date quality issues?
There are several ways to reduce date quality issues, but I think the most important thing is to properly understand the importance of date data and the bad influence of bad data to the clinical trials. The first thing must be better TRAINING of site and CRA teams. They should know and remember that dates must be written in same formats during the trial. Moreover, all data must be entered into the EDC system as soon as possible, and in any case not before, in order to reduce any human errors. Another consideration is the EDC design; a well-designed study build will implement robust date validation at field level including cross-form validation (date of visit 2 must be at least 30 days after visit 1, etc).
And the last but not least we can ensure good quality dates by using remote monitoring tools (and I’m not biased!) that give clinical trial operations a clear and current picture of their date fields quality at all times; and believe me, remote monitoring can rescue your trial!