The difference between periodic manual review and continuous AI monitoring is not the amount of data examined, but when it is examined. In batched review, data accumulates between scheduled checks, so the time to notice a problem is set by the interval. In continuous monitoring, each new record is compared against expected patterns as it arrives, so a deviation can be flagged within hours of appearing. The two timelines below run side by side: the same problem appears at the same moment in both, but the point at which it is detected — and therefore the point at which someone can act — falls far earlier in the continuous case.
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Monitoring Data and Catching Problems Early
Batched Review Against Continuous Review
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Watch the two timelines together. The problem appears at exactly the same moment on both — that part is identical. On the top timeline, review happens in scheduled batches, so the marker for detection sits far to the right, at the next check. On the bottom timeline, each new record is compared as it arrives, so detection lands almost immediately after the problem starts. The distance between those two detection points is the time the trial gains. Nothing about the data changed; only the moment someone looked at it.
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