Inaccurate output

Even with upstream controls in place, the software could in principle produce an output that does not accurately represent the underlying data — through a bug, a configuration mismatch or an unforeseen edge case. If an inaccurate output is used directly in a regulatory submission or decision, the impact can be significant.

Hazardous situation: A flawed regulatory submission is produced because the software returned an inaccurate output.

• Validated processing logic. Every release goes through the documented Flinn Release & Validation Process, which exercises representative scenarios against expected results.
• Reference-backed reports. AI-assisted clinical writing always cites the source of every paragraph, so the user can confirm that the output reflects the underlying data — see AI Clinical Report Writing.
• Validated extraction. Structured data extraction is benchmarked against representative inputs; see the AI Extraction Guide.
• Cross-checks against the source. For high-stakes evaluations, results should be cross-checked against the official database; see Why do I find different results when I compare the official database and Flinn?.
• Export verification. Before relying on an exported report, sanity-check it against the on-screen results; see Export a Search and I would like to export additional data from the reports.
• Quality reporting. If an output looks wrong, Report a problem or a bug immediately. Related guidance: Misinterpretation of data and Data corruption or quality issues.

A combination of validated logic, traceable references, and user-side cross-checks keeps the residual likelihood of acting on an inaccurate output low.