Problem-to-Action Leadership Playbook

Claim volume is up but cash is flat

Revenue was created but has not converted. Possible causes to investigate: cohort maturity or billing lag, payer mix shift, unposted or unlinked cash, stalled secondary billing, or collections follow-up.

Check in this order

1. Billing lag by cohort — is the new volume simply not old enough to have paid yet?
2. Payer mix shift — new volume in slower-paying or lower-yield payers.
3. Unposted cash — receipts sitting unlinked rather than missing.
4. Secondary billing lag — balances stalled after primary payment.

Common wrong move: Setting a collections push before confirming the cohort has matured.

High generation, low acceptance

Claims are being created and released but acceptance is not being evidenced. Possible causes to investigate: missing or delayed acknowledgments, acknowledgment being read as acceptance, payer-side processing delay, channel or enrollment changes, or corrections that did not actually resolve the original defect.

Check in this order

1. Are claim-level responses actually arriving, or is pending being read as accepted?
2. File and interface exceptions between billing, clearinghouse, and payer.
3. Payer-specific front-end rejection reasons, grouped by payer.
4. Companion-guide, enrollment, or channel changes on the affected payers.

Common wrong move: Treating it as a staff quality problem before ruling out a channel or response-capture failure.

High touches per claim and a growing edit backlog

Possible causes to investigate: handoff design, repeat upstream source errors, work blocked on another department, queue complexity, or genuine capacity shortfall.

Check in this order

1. Map the handoffs on the highest-touch claim types.
2. Rank blocking rules by affected claim volume and identify the repeat source error.
3. Check where work sits blocked on another department.
4. Confirm arrival rate versus capacity with the staffing calculator.

Common wrong move: Adding staff to a queue whose real constraint is a single upstream data error.

Low raw output on a complex queue

Possible causes to investigate: a standard that does not match the observed work, a harder complexity mix, externally blocked time, or individual performance.

Check in this order

1. Observed local task times for that complexity band.
2. QA accuracy on the same work — is the slower pace buying correctness?
3. Externally blocked elapsed time inside the period.
4. Weighted attainment rather than raw count.

Common wrong move: Opening a disciplinary conversation before validating task time and quality.

QA scores are high while denials rise

Possible causes to investigate: sampling gaps that miss the failing cohorts, timing or cohort differences between the audit period and the denial period, payer policy or edit changes, upstream causes outside the audited step, or a checklist that does not test what is being denied.

Check in this order

1. Sample size, selection method, and whether it covers the failing payer cohorts.
2. Whether the QA checklist tests the requirements that are actually being denied.
3. Upstream causes outside the audited step — authorization, coverage, documentation.
4. Critical error handling — are they visible or averaged away?

Common wrong move: Publishing the QA score as evidence that denials are not an internal issue.

Large recovered dollars reported

Recovery is real work. Possible causes to investigate: a recurring known defect being corrected repeatedly, a single payer or root cause dominating, face value being credited instead of posted cash, or genuine one-off variances.

Check in this order

1. Separate recurring correction of a known defect from genuine prevention.
2. Whether the same root cause and payer keep appearing.
3. Whether posted cash, not face value, is being credited.
4. Which department could stop the defect upstream.

Common wrong move: Celebrating recovery volume while the preventable cause stays open.