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Data systems are important in ensuring data integrity, but what should laboratories do beyond looking at data systems—with respect to people, processes, and culture? To understand more, we spoke to Mark Newton, of Heartland QA, in Lebanon, Indiana, in a podcast interview.
Corporate culture is probably at least 50 percent of the problem; I think regulators would tell you it’s more like 60 percent of the problem. With the cultural aspect, you have to establish a set of expectations as to what are acceptable and unacceptable actions. This will help people get a real firm sense of how this impacts them in their job. This starts with messages like, “Thou shalt not cheat,” “Thou shalt always tell the truth,” “You will report things that are suspicious,” “You will report things that could impact our product,” and so on.
Those high-level messages are great, but they don’t get down to the low-level tactical elements, such as “Only do manual integration when there’s something wrong with the automated process.” There’s often a disconnect between the high-level edicts from upper management and the tasks that analysts need to do day by day in their jobs. So we have to have the training and examples for people to bridge that gap, so employees understand what data integrity means in regard to their specific actions in their workplace. That’s an important part of this question.
The other part, though, along with culture, is capability. If we’re not using capable processes and capable equipment, we’re going to have data integrity issues over time. The reason is that when you add the extra element of pressure—such as scheduling conformance, unplanned events, people getting sick or leaving—those are the things that stress the environment. If the environment and the equipment are capable and the people know what to do, when unplanned things happen, they respond and they react to those unplanned occurrences and continue to work well, because they have the room to respond and they have processes and equipment that are capable of meeting the unplanned demand. But when you have an organization that’s using old methods and old equipment, and the processes they’ve got aren’t designed to work at the manufacturing scale they’re running at now, they’re already behind the eight ball. Then as soon as something else happens, it all falls apart, and people are looking for ways to keep things on schedule. Now your environment is ripe for data integrity issues to come in, because people are simply trying to survive, and they’ll engage in bad practices because they need to get the product out the door to make the senior leaders happy. I believe it’s that one-two of culture and capability in the environment of stress that determines the likelihood that people will go into those bad behaviors, just simply to get their work done.
How can a laboratory add capability, as a way of ensuring data integrity?
In the short run, training is the heart and soul of your data integrity program. If you did nothing else and you wanted to improve your data integrity position, at least insofar as regulatory questions are concerned, I would advise you to be working on your training program, because that impacts everybody in everything they do. It doesn’t make any of your processes better, but it at least tells people what is the acceptable way to respond to issues. It also, hopefully, opens a valve so that when there are issues, people are willing and are confident about speaking up about the various problems that are going on in the workplace so that those things get put out in the open where they can be addressed. So even if training only changes that aspect of the workplace, it’s a positive change, because it at least enables the focus on quality and long-term improvement.
So in terms of culture, you get a lot of improvement in a short period of time with training. That will take you a certain distance. To go further, you have to start working on your quality processes. Ultimately, data integrity is interwoven into all your quality processes. If your quality system is weak, your data integrity is ultimately going to be weak as well. You can’t be strong in terms of data integrity and do poor investigations. Ultimately, those elements feed on each other. So you don’t just improve data integrity; you improve your quality system as a whole. If you improve the system, your culture feeds on that, feeds into it, and feeds from it as well.
Ultimately, all those all these elements interplay with one another, so you have to address that interplay over time. You can start with training, but in time you have to bring in the rest of your quality system to continue that journey.
This interview has been lightly edited for style and space.