prior_authorization · healthcare · workflow
How to build a custom AI review dashboard for LLM products — lessons from Anterior's Scalpel
Without metrics on AI performance, teams cannot know how their app is doing or how to improve it; performance could drop unnoticed until customers have already left.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · AI makes treatment decision
Clinical reasoning workflows check medical guidelines against medical evidence to decide whether a treatment should be approved.
Tools used
Scalpel
Outcome
Anterior's Scalpel dashboard enabled a small team of clinicians to review more than 100,000 medical decisions, providing a high-leverage bridge between production AI outputs and continuous product improvement.
What failed first
Spreadsheets and off-the-shelf tooling providers hit limits quickly — they restrict data views, struggle to expose intermediate LLM steps, and make it hard to translate review outputs directly into application improvements.
Results
Volume>100,000
Grounding & classification
Source type: technical build writeup
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agentic workflowknowledge basemedical recordpolicy documentfailure mode describedhuman review describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedhealthcareemployee productivitythroughput increasetechnical build writeupprior authorizationquality assuranceai draft human approvalhuman review queue