Continued from Part 1 and Part 2…

Let’s say a sponsor is developing a blood pressure medication designed to bring blood pressure in hypertensive patients down to a “normal” range of 130/80. The medical charts for Subject 1 show a blood pressure of 145/80 at Visit 5, after the Subject has been on study for 2 months.

Scenario 1

The Study Nurse enters 145 in the field for systolic blood pressure and tabs to the diastolic blood pressure field.
A warning message pops up: “Systolic blood pressure is too high. Please correct.” (You might think the language used could be perceived as coercive, but that’s the topic of another talk.)
The Study Nurse checks the medical record; leaves the 145 unchanged; closes the warning message; enters the diastolic blood pressure; and saves the eCRF.

Scenario 2

The Study Nurse enters 154 in the field for systolic blood pressure and tabs to the diastolic blood pressure field.
A warning message pops up: “Systolic blood pressure is too high. Please correct.”
The Study Nurse checks the medical record; realizes they entered the value incorrectly; changes the 154 to 145; and tabs to the diastolic blood pressure field.
A warning message pops up: “Systolic blood pressure is too high. Please correct.”
The Study Nurse closes the warning message, enters the diastolic blood pressure, and saves the eCRF.

The problem in each scenario is that the EDC system has been designed not to record the:

Original value entered in Scenario 2
Fact that the query fired in either Scenario
New value entered in Scenario 2

It’s all been done using invisible ink.

In my experience, this happens because IT has not thought through the data integrity implications, and it’s a generally held belief that…

“It’s not data until the site saves it!”

Having The Discussion

What’s important to scientists about their data? Quality, integrity, and reproducibility.

I can’t promise success, but I can tell you I’ve been successful most of the time when I walk a scientist through their process and demonstrate to them where they’ll have problems with quality, integrity, and reproducibility.

I nearly always see the light come on – the penny drops – they get it.

I just planted a seed and watched it germinate. Now, whether it’s germinated in a rocky place, among thorns, or in good soil is difficult to tell. So much of what happens next depends on senior management and the culture they establish and the the behaviors they reward and punish.

Senior management may have a different understanding of data quality, integrity, and reproducibility than a bench scientist. To be successful, the conversation may have to sound different.

Where have data integrity Warning Letters affected stock prices or the ability to do business?
How have data integrity problems delayed submissions, resulting in lost revenue?
When did the lack of data reproducibility cause an expensive merger to go south?

We found the trail!

In GLP studies, we work with animals (who cannot give their consent to participate) to help predict safe doses of experimental compounds to give to human subjects.

In GCP studies, we work with sick people (who could be considered vulnerable just on the basis of being sick) who do give their consent to participate in an experiment to help predict safe and efficacious doses to give to human patients in the future.

In the manufacturing process, people work with machines and ingredients to create a medicine that has been approved for use in human patients on the basis of the GLP and GCP data.

We are seeing very clear messages about data integrity in the manufacturing setting.

It’s my hope that we can agree how to think about data integrity in GLP and GCP research, align it with the regulatory expectations for GMP data, and eliminate the use of invisible ink.

To that end, I make the following proposal, which I hope will start a conversation here and which can be continued with your company and with your service providers.

Proposal

The same requirements for data integrity apply to GLP, GCP, and GMP data.
There is no regulatory basis for claiming data are draft data in GLP or GCP studies.

Implications

Two of the many implications of this proposal are that:

All changes to data, including metadata (like gate settings), should be recorded and maintained with the electronic record of interest.
Queries prompting investigator site staff to change data on eCRFs should only fire after saving the data.

Wrap-up

I feel confident we can work together as QA professionals to bring all parties back to the trail to data integrity, whether they’re currently in a rocky place or are surrounded by thorns.

About the Author

Jamie Colgin is Govzilla’s GCP Product Manager and is the recipient of the prestigious Charles H. Butler Excellence in Teaching Award. She joins Govzilla from Colgin Consulting, Inc.