What the work involves
You will spend most of your time reading AI outputs the way you'd read a colleague's authorisation packet: does the clinical justification actually map to the documentation, does it cite the right criteria set, would this survive a payer review or land in denial? Typical tasks include grading AI-drafted medical-necessity letters, checking whether a model correctly identified that a service requires prior auth under a given plan type, flagging hallucinated criteria or misapplied InterQual/MCG subsets, and writing rubric-anchored rationales explaining exactly where the output failed. Some batches ask you to produce gold-standard reference answers — a clean authorisation submission or appeal argument — that models are then scored against.
The distinction that matters here is between reviewing and explaining. A rating without a defensible clinical rationale is close to worthless to the lab. Reviewers who do well tend to write feedback that a utilisation management trainer could act on: which element of the criteria was unmet, what documentation would have satisfied it, and why the payer type changes the answer.
What the platform screens for
Mercor's screen is AI-led and pushes on specifics. Expect follow-ups on the payer mix you actually worked, which criteria set your organisation licensed, how you handled peer-to-peer escalations, and turnaround-time standards you were measured against. Vague answers about "managing the auth team" get probed until they resolve into concrete cases or fall apart. The screen also tests evaluation judgment directly — you may be shown a plausible-sounding AI justification with a subtle criteria error and asked what you'd do with it.
- 5+ years in prior authorisation, utilisation management, or clinical review, with real management exposure
- Fluency across commercial, Medicare Advantage, and Medicaid requirements — not just one book of business
- Willingness to write, at length, in clear English
Logistics
Fully remote and asynchronous. Work is distributed in batches with deadlines rather than fixed shifts; most contributors commit 10–20 hours per week, and availability windows are negotiated per project. Engagements are contract-based and can taper or extend depending on the lab's data needs. The $105/hr figure reflects rates observed on this listing and is not a guarantee — final rates depend on credentials, screen performance, and project tier.