Diagnose a fill-rate move without blaming the wrong thing — by pinning the denominator, checking the seasonal boundary, splitting supply from order-quality, and pairing with time-to-fill including the credentialing clock. Reach for this when fill rate dropped and the cause is unclear.
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---
name: fill-rate-diagnostics
description: Diagnose a fill-rate move without blaming the wrong thing — by pinning the denominator, checking the seasonal boundary, splitting supply from order-quality, and pairing with time-to-fill including the credentialing clock. Reach for this when fill rate dropped and the cause is unclear.
---
# Skill: Fill-rate diagnostics
Fill rate is the most-quoted and most-misread staffing metric. A "fill rate fell" finding is, more often than not, a denominator shift or a seasonal artifact — not a recruiting failure. This skill runs the diagnosis in the order that prevents the expensive wrong-first-pick.
## Step 1 — Pin the denominator
Fill rate has at least four formulas (orders filled ÷ received / ÷ workable / placements ÷ submittals-accepted / ÷ open-in-period). **Confirm both periods use the same one.** If dead/on-hold/uncompetitive orders grew the base, fill "fell" without anything operational changing — that's an order-quality story (Step 3), not a fill story.
## Step 2 — Check the seasonal boundary (§3 #5)
Does the comparison cross a healthcare surge/summer peak or the education spring-recruit/fall-start cycle? If yes, re-cut **YoY same-period** before interpreting. Many Q-over-Q "declines" in school-based work are the hiring having happened in Q2–Q3.
## Step 3 — Split supply vs. order-quality (§3 #6)
Two diseases, identical symptom, opposite cures:
- **Supply:** submittals-per-workable-order is down → sourcing-channel + capacity work; check pay-rate competitiveness vs. market.
- **Order-quality / demand:** the workable orders themselves are uncompetitive (bill rate) or aged/un-workable → intake discipline + bill-rate competitiveness, not more recruiting.
Pull submittals-per-workable-order and the order-aging distribution to tell them apart.
## Step 4 — Pair with time-to-fill (§3 #2)
A 95% fill at 45 days can be losing to a competitor filling 80% at 9 days — speed wins the placement. Classify the disease: high-fill/slow-speed vs. low-fill/fast-speed have opposite fixes.
## Step 5 — Include the credentialing clock (§3 #7)
Measure time-to-**start**, not just accept. The accept→start interval (licensure, background, Joint Commission docs, district clearance) is where fall-off concentrates. A fast sales clock and a slow credentialing clock is a credentialing problem, not a recruiting one. See [`../../knowledge/credentialing-and-compliance.md`](../../knowledge/credentialing-and-compliance.md).
## Step 6 — Only now, look at recruiter execution
If supply is healthy, orders are workable and competitive, speed is fine, and credentialing is clean — *then* examine execution, normalized for reqs-per-recruiter and order difficulty (§3 #4).
## Step 7 — Rank the two likeliest causes
Name the top two with the evidence behind each and the data that would confirm. Resist a single-cause story; fill-rate moves are usually two things at once.
## Reference
Traverse the **`## Decision Tree: Fill rate has declined`** in [`../../knowledge/staffing-decision-trees.md`](../../knowledge/staffing-decision-trees.md) top-to-bottom. Definitions: [`../../knowledge/staffing-kpi-glossary.md`](../../knowledge/staffing-kpi-glossary.md).
