A county clinic queue dashboard breaks when wait-time metrics arrive as negative numbers. How should quality constraints treat those values?

Business-range rules are first-class quality constraints for civic metrics. County clinic wait-time dashboards assume non-negative durations; negative values usually indicate clock skew, sign errors, bad joins, or unit mistakes. Encoding a range rule that rejects or quarantines negative wait times before dashboard publish protects SLA boards, while a strict data-type check that accepts negative wait-time metrics as valid integers ignores the business range and lets impossible values light the board red.

Enforcing a strict data-type check that accepts negative wait-time metrics as valid integers fails by design: it validates the type while explicitly approving an impossible business value, so negative integers flow through and corrupt the dashboard. Configuring a freshness monitor that re-timestamps negative wait-time metrics on arrival fails by design: it only updates arrival time while leaving the negative duration intact, so invalid wait times still reach the dashboard while the upstream defect stays hidden behind a fresh timestamp. Applying a completeness threshold that counts negative wait-time metrics toward required coverage fails by design: it treats invalid values as good coverage, so negative wait times still reach the dashboard instead of being rejected or quarantined.

Exam caveat: a range rule can be warn/quarantine/fail depending on how critical the dashboard is; finance-style SLA boards often quarantine or fail, while exploratory silver may warn first. Operational check: inject negative and absurdly large wait times into a test batch, confirm they never reach the clinic queue dashboard, verify zero-and-positive values still aggregate correctly, and open a remediation ticket for the source system producing the negatives.