Every automated process that affects money, members, patients, or records needs a point where a person decides. Most organizations agree with that sentence and then implement it as a policy: the system produces a recommendation, and staff are expected to review it before acting. Under normal load this works. Under deadline pressure it does not, because a policy can be skipped and a queue can be cleared by approving everything.
Build the approval into the system
The alternative is to make the review step part of the system's design. The recommendation cannot take effect until a named person approves it. The evidence the recommendation rests on is attached, so the reviewer decides from the source rather than from a summary. The approval is recorded with the reviewer's name, the evidence they saw, and the time. None of this can be switched off by a manager under pressure, because it is not a setting.
This is more work to build and less work to defend. When an auditor or a regulator asks how a decision was made, the record answers the question.
Corrections are data
A review step that is built into the system produces something a policy never does: a stream of corrections. Every time a reviewer changes a recommendation, the system learns where it was wrong. Over months, that stream is the most valuable data the organization has about its own process, and the reason the system improves with use instead of decaying.
The decision to build review into the architecture is made once, at the start. It is very expensive to make later.