Data Integrity on Hybrid systems: Manual vs Digital Data
By Sandra Gonzalez 09 April 2025
Regulatory agencies have been increasingly focused on data integrity to ensure the accuracy, reliability, and trustworthiness of data, especially in industries like pharmaceuticals, healthcare, and finance.
The FDA (U.S. Food and Drug Administration) has issued several guidance documents to ensure data integrity in compliance with current Good Manufacturing Practices (cGMP). Key aspects include:
· Backup and Security: Data must be exact, complete, and secure from alteration or loss.
· Documentation: Activities must be documented at the time of performance, and records should be retained as original records, true copies, or accurate reproductions.
· Electronic Records: Compliance with electronic signature and record-keeping requirements.
The GAO (U.S. Government Accountability Office) has highlighted the importance of data governance in federal agencies. Key recommendations include:
· Data Quality Plans: Developing plans to ensure data quality and transparency.
· Data Literacy: Assessing and improving staff data literacy skills.
· Data Maturity Assessments: Conducting assessments to evaluate data and infrastructure maturity.
These new standards and guidelines aim to enhance data integrity across various sectors.
Early Beginnings
· 1963: The U.S. Food and Drug Administration (FDA) published its first guidelines to ensure data accuracy during the drug development lifecycle. This marked the beginning of formal regulatory oversight on data integrity.
Development of Key Principles
· ALCOA Principles: The FDA introduced the ALCOA principles, which stand for Attributable, Legible, Contemporaneous, Original, and Accurate. These principles were designed to ensure that electronic data met high standards of integrity.
· ALCOA+: The principles were later expanded to ALCOA+, adding Enduring, Available, Accessible, Complete, Consistent, Credible, and Corroborated to address more comprehensive data integrity needs.
Regulatory Expansion
· 1997: The FDA introduced 21 CFR Part 11, which set forth regulations on electronic records and electronic signatures, ensuring that they are trustworthy, reliable, and equivalent to paper records.
· European Union (EU): The EU also developed its own set of guidelines and regulations to ensure data integrity, particularly in the pharmaceutical industry.
Modern Developments
· 2010s: Increased focus on data integrity led to more stringent guidelines and frequent inspections by regulatory bodies like the FDA and the European Medicines Agency (EMA). This period saw a rise in warning letters and enforcement actions related to data integrity violations.
· 2020s: The emphasis on data integrity continues to grow, with new technologies and methodologies being adopted to ensure data accuracy and reliability. Regulatory agencies have been updating their guidelines to keep pace with technological advancements and the increasing complexity of data management.
Up to date, most companies have not moved completely to digital systems which minimize the human factor error from the processes and remain with hybrid systems. Ensuring data completeness in hybrid systems, which involve both manual and digital processes, requires a comprehensive approach. Here are some strategies to consider:
1. Standardize Data Collection Processes
· Manual Processes: Develop clear guidelines (procedures) and checklists for data entry to minimize human error. Include the process flow and data flow mapping to procedures for easy understanding of the process and data impact. Regular training sessions can help ensure that all personnel understand the importance of data completeness and adhere to the standards.
· Digital Processes: Implement automated data validation rules and mandatory fields in digital forms to ensure that all required information is captured before submission. Include data flow mapping to the instruments for easy understanding in data impact of the instrument.
· Data Flow Mapping: Data flow mapping is crucial for maintaining data integrity as it provides a detailed visual representation of how data moves through various processes and systems. This is important to identify risks and vulnerabilities, ensuring data accuracy and consistency, improves data governance, enhance efficiency and facilitates communication.
2. Integrate Data Sources
· Use data integration tools to consolidate data from various sources into a single repository. This helps in identifying and filling gaps in the data. Ensure that both manual and digital data sources are included in the integration process. This includes to perform back up of the data from different sources into one server that is always accessible.
3. Regular Audits and Reviews
· Conduct periodic audits to compare the data against predefined standards. This can help identify missing, inconsistencies between manual and digital data or incomplete data. Auditing the hybrid system will help to identify data risks and areas of improvement. Use both manual reviews and automated tools to ensure thoroughness.
4. Implement Data Quality Metrics
· Define and monitor key data quality metrics such as completeness, accuracy, and consistency. Use dashboards and reports to track these metrics over time and identify risks and areas for improvement.
5. Leverage Technology
· Machine Learning: Use machine learning algorithms to predict and fill in missing data based on patterns in the existing data.
· Data Validation Tools: Employ advanced data validation tools that can automatically check for completeness and flag any missing information.
6. Feedback Loops
· Establish feedback loops where users can report issues with data completeness. This can help in quickly addressing any gaps and improving the overall data collection process.
By combining these strategies, organizations can ensure that their data sets are complete, reliable, and ready for analysis, regardless of whether the data is collected manually or digitally.
SbyS group can help you strategize/train/assess your data integrity needs. With over 25 years of experience managing successful Inspection/Audits to a variety of customers with different challenges and no observation on data integrity systems, SbyS has helped clients to reach goals improving the regulatory status. We also provide detailed assessments of your quality systems, identify current problems, risks and trends to recommend actions, and work with the Staff in continuous improvement of the processes.