Ensuring Data Integrity in Medical Device Industry: Techniques for Data Life Cycle Management

Data Integrity in Medical Device Industry
Article Context:
  1. Tips for Data Management

Maintaining Data integrity for medical devices is still a challenge in the current healthcare industry. Data Integrity means that information is accurate and has not been altered in any way. Incorrect data may pose serious health risks to patients and a huge administrative burden for manufacturers. Problems can also include fraud and data theft, which can destroy a company's reputation. Inadequate maintenance of medical records and data can cascade into a domino effect of issues that can eventually put patients in life-threatening situations.

The process of Data Lifecycle Management makes it easier to find, implement, and improve upon data quality while preventing an organisation from incurring data hazards. The lifecycle includes the creation of data, its use in related business applications, and its eventual retirement, erasure, or archival. Establishing this data management process will allow companies to mitigate risks while also vastly improving product quality and client retention.

Here are 7 essential tips for successful data management

Risk-based Validation

Save time and resources by validating only the GxP-compliant systems and make sure that associated protocols cover data dependability and quality. In some circumstances, it’s more affordable to have the system vendor qualify and validate the systems. You may use the ISPE's GAMP5 (Good Automated Manufacturing Practise) categorizations to assess your system's validation complexity before choosing between internal validation resources and external validation services. During validation, one should consider every electronic data storage location, including printouts and PDF reports. Ensure that the frequency, roles, and duties for system validation are specified in your Quality Management System. Your validation master plan must detail the implemented strategy used in examining relevant metadata, such as audit trails. After the initial validation, plan frequent re-evaluations.

Establish Audit Trails

A system's audit trail must consistently record all the data it contains, including changes to databases and files. The following questions, Who, What, When and Why must be addressed. The data that is relevant to GxP (Good Practise Guidelines) should be identified and documented in the audit trail. The complexity of the system and intended use should be considered when selecting the audit trail review scope. Discrete event logs, history files, database queries, reports, or other techniques that display system-related events, electronic records, or raw data within a record are examples of audit trail components that must be recognised.

Choose the right service

It's a good idea to choose service providers that follow regulatory expectations and offer proof in the form of audit trails and other documentation. That way it's easier to leverage information provided by them regarding the systems intended use and ability to meet expectations. Always do some background research on service providers you're interested in, making sure they have a track record of sharing sufficient documentation to businesses they work with.

Have a plan

Create a strong disaster recovery plan. Your plan should outline the potential consequences of any data loss as well as how quickly operations can be restored. To preserve data from being lost during power failures or network disruptions, look for software that can record and store data automatically. Use UPS (Uninterrupted Power Source) systems, battery-operated standalone recorders, or other devices that can switch to a different power source as needed. For example, data loggers are also battery powered.

Practice Routine Backups

Electronic data, including metadata, should be backed up and saved on a predetermined timetable to a secure location. During internal audits, confirm that all the data was retrieved. Throughout the data life cycle, electronic archives should be verified, secured, and kept in a controlled form.

Maintain Accuracy

Its critical to get into the habit of checking system input to verify accuracy. For example, an environment monitoring system will require its sensors calibrated regularly. For Networked systems, you’ll need to verify that data is coming from the correct locations. It's wise to choose systems that send out alarm messages in the event of a device malfunction, communication failure, or data tampering.

Stay Up to Date

Changes to your system must align with current regulations, always stay up to date on the latest regulatory requirements when updating or configuring a system. Sit down with system providers to gain more understanding on any new updates and their downstream impact to functionality.

The Final Verdict

Establishing data integrity in medical device industries is crucial for achieving patient safety and regulatory compliance. Implementing data lifecycle management practices can assist organizations in maintaining data security, reliability, and accuracy. By following our Data Management tips, organizations can establish a strong foundation for secure data storage, data governance, data transmission, data verification/validation, and Data backup/recovery.

Need Help?

Is Data Management too much of a headache to handle or are you just too busy to deal with it? Compliance Group Inc specializes in supporting businesses in meeting industry standards and regulatory requirements. With our support, partnered companies gain access to industry best practices, tailored solutions, and full guidance in enhancing data management initiatives.

Find out more and connect with one of our experts at sales@complianceg.com

FAQ's

What is data integrity as per FDA?

FDA Data integrity guidance refers to maintaining consistency and veracity of data over its full life cycle. The FDA expects that all information provided to the organisation for drug approval and Pharmaceutical production will be complete, consistent, and accurate.

What are the 5 principles of data integrity?

The 5 principles of data integrity include auditability, security, consistency, completeness, and accuracy. By Adopting these 5 principles, Businesses can guarantee their data management follows FDA guidelines and meets regulatory requirements.

Why is data integrity important according to FDA?

Data integrity is the continuity, accuracy, and dependability of data over its entire existence. For companies subject to FDA regulation, data integrity is essential since it serves as proof that the products meet all expectations. The consequences of inadequate data integrity can have a negative ripple effect on a Company’s’ production and reputation.

What is 21 CFR for data integrity?

Electronic record-keeping for pharmaceutical and medical product manufacturers is governed by FDA 21 CFR Part 11. Electronic records must be secure and transparent providing both sufficient detail and electronic signatures. This way, it’s easy for auditors to inspect and ask questions where needed. It’s important to leave behind a sufficient audit trail to prove the quality and reliability of a product.

david-deluca

AUTHOR:
DAVID DELUCA
Director, Engineering & Validation