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An alert that reveals no problem after investigation does not necessarily mean the monitoring system failed. Repeated identical explanations, however, may indicate that a definition or data flow needs attention. Reviewing false positives should reduce avoidable workload while preserving the visibility of important risks.
Scope of the decision
Define what a false positive means first. A genuine low-impact event, a delayed delivery, an incorrect match and a justified exception are different categories. A single percentage calculated without these distinctions provides a weak basis for improving the monitoring rule.
Worked review
Consider unscheduled laboratory samples that repeatedly trigger missing-visit alerts. Reviewers keep closing each case individually. The team traces several examples to their source and discovers that the data definition does not represent unscheduled samples correctly. It fixes the classification rather than merely suppressing the rule.
Practical checks
- Separate closure reasons into data problems, justified exceptions and real events.
- Trace samples of repeated explanations back to their source records.
- Check both unnecessary alerts and missed events after making a change.
Acceptance and limits
Accept an improvement when evidence shows lower avoidable workload and continued detection of important events. Also check that reviewers are not overusing an easy closure category. Finding no problem in an alert does not provide a general assurance that the source data are complete.
Frequently asked questions
- Should the target for false positives be zero?
- Not necessarily. A sensitive control can produce alerts explained by context; the balance depends on risk.
- Can closure reasons be free text?
- Yes. Comparable categories combined with an explanatory rationale generally make the learning more useful.