Did the Filter Find More Signal, or Just Remove Noise?
Ada re-derives this chapter’s own numbers step by step, at full precision
ADA · CALCULATION AUDIT
Did the Filter Find More Signal, or Just Remove Noise?
A hospital monitoring system pushes 350 alerts per shift at the nurses’ station, and only 18% deserve action — alert fatigue at the level where clinicians start tuning warnings out. The chapter’s multi-stage filter (patient-specific thresholds, multi-parameter fusion, a prediction model) cuts the flood to 145 alerts with 43% actionable. This audit asks the question those numbers invite: did the filter find more real signal, or just remove noise?
Companion to the chapter Healthcare IoT — every number here comes from that chapter.
Ada: The alert-fatigue pipeline makes two claims at once: alerts per shift fall from 350 to 145, and the actionable share climbs from 18% to 43%. It is easy to read that second number as “the system now finds more real emergencies.” Let me test that by counting actionable alerts instead of their percentage.
- Actionable before:
0.18 x 350 = 63.0real alerts inside the 350. - Actionable after:
0.43 x 145 = 62.35, about62real alerts inside the 145.
The count of real alerts barely moves — 63 before, 62 after. So the reduction did not come from finding more signal; it came from deleting noise. False alerts fell from 350 - 63 = 287 to 145 - 62 = 83, and of the 350 - 145 = 205 alerts removed, 287 - 83 = 204 were false.
The actionable percentage nearly triples, but not because the numerator grew: 62 / 145 = 0.428. The 43% is the arithmetic of a shrinking denominator over a preserved numerator, not new detection.
Design meaning: a rising “actionable %” is only trustworthy when the real-alert count holds steady — the safety test for a clinical alarm filter is that it strips false alerts without quietly dropping true ones, and 63 -> 62 is exactly the evidence that test demands.
Every number above is taken from the chapter’s own material and re-derived step by step.