Data corruption or quality issues

If critical regulatory data becomes corrupted due to a software glitch or storage failure, it can no longer be trusted for analysis or reporting. This could compromise literature reviews, incident surveillance or regulatory submissions.

Hazardous situation: Critical regulatory data becomes corrupted or unusable, leading to gaps in your evidence base and potentially undermining regulatory compliance.

• Validate AI outputs. Flinn’s AI extraction feature is validated to ensure accuracy and reliability , but you should still verify extracted data on a sampling basis. Checking random samples of the AI’s output against the original text helps catch anomalies early and maintain high data quality. You can learn more on how to avoid misinterpreting AI output here.
• Verify references. When using AI to draft report sections, every generated paragraph includes reference numbers to make it easy to verify sources . Cross‑check these references to ensure the data hasn’t been corrupted or misattributed.
• Monitor reliability features. Tools like Similar Reports Detection are designed to improve the reliability of your incident search by flagging duplicates and streamlining your review process. Use these features to avoid counting the same data twice and to maintain consistency across datasets.
• Maintain backups and contact support. Flinn’s infrastructure includes safeguards to prevent data loss, but maintaining your own backups of exported reports provides an additional layer of protection. If you notice any indication of a quality glitch or corrupted data, contact support immediately so we can investigate and restore the correct data.
• Check quality results regularly. Sampling results at regular intervals — for example, reviewing a subset of extracted data or incident reports each week — helps ensure that any corruption or glitches are detected and corrected promptly.

By combining built‑in reliability features with proactive quality checks, you can reduce the impact of data corruption and maintain confidence in your regulatory evidence base.