Even with several guidance documents published by various agencies, companies and associated staff continue to struggle with data integrity issues.
Data integrity can be one of the most complex areas of compliance, but a few simple steps can ensure that your company maintains fundamental Attributable, Legible, Contemporaneous, Original, Accurate (ALCOA) principles and minimizes data integrity risk.
USDM can help you with a plan of action.
1. Data Integrity
Many data integrity issues can be traced back to human error; therefore, ensure that data integrity starts with the user. Your employees should be trained on data integrity, data entry, and fundamental ALCOA principles. An active and evolving training program should be in place based on the operational needs of your employees and business processes. Procedures regarding data (i.e., data entry, review, and approval) should be easy to understand and available for reference. Programs should also be put in place to ensure that system administrators provide the correct level of user access based on training and role.
2. Understand your Process Workflow and Data Lifecycle
From sample hand-off, to release of results, to archiving data, your laboratory process workflow—and consequently your data flow—should be well understood and documented. A simple data map for a laboratory can help consolidate workflows, highlight potential risks, work as a road map for process improvements, and ultimately mitigate data integrity risks.
3. Automate Data Workflows
Manual data entry or manual transcription can lead to poor data integrity behavior (for example, data that is jotted down on a sticky note during testing and then transferred to an approved form, or manually entered in an electronic system). The transferred data is not original, possibly duplicated, and potentially inaccurate. A data map can be used to identify areas where these types of risks to data integrity exist. Automated solutions like Electronic Lab Notebook (ELN) and Laboratory Information Management System (LIMS) should be put in place to ensure real-time data and metadata capture, and do away with paper-based or hybrid (paper and electronic) processes.
4. Review Data for Quality and Completeness
When data errors go unnoticed, there is a greater potential for bigger issues. Reviewing critical data should be conducted by someone with expert knowledge of the operation. Reviews should be done in near real-time to ensure data integrity discrepancies are dealt with before they escalate.
Contact us with any questions you may have.