Revenue-cycle outcome diagnosis
Objective. Equip Sales and CSM to run the revenue-cycle diagnosis play: a revenue-cycle leader has a worrying outcome - denials up, AR ageing, a payer problem, a backlog - and needs to know which issue to address first and what evidence supports it. Show how RCM Operations, RCM Insights, Work Output and Quality and Call QA move from signal to evidence to owned action, and keep the four value definitions separate.
A revenue-cycle leader drowns in metrics but cannot prioritise. This play is a structured diagnosis: RCM Operations turns a signal into evidence, a proposed action and an outcome hypothesis; RCM Insights lets the leader interrogate the data and inspect the supporting evidence and scope; Work Output exposes whether an operational issue is really an output or cost problem; Quality and Call QA traces recurring defects that drive denials. The value is faster, evidence-led prioritisation of the issue that most protects revenue. The boundary is the discipline that recoverable value, billed value, collected cash and capacity value must never be conflated.
See it in the demo
The problem and who feels it
The revenue-cycle leader has signals everywhere - a denial rate ticking up, AR ageing in a bucket, a payer behaving badly, a backlog building - and no structured way to decide which one to tackle first or to defend that choice with evidence. Who feels it: a revenue-cycle or billing director accountable for cash and days-in-AR; an operations lead asked to act but unsure which action moves the outcome; a CSM whose customer is reacting to whichever metric shouted loudest this week rather than the one that matters most.
The problem is prioritisation under ambiguity. Without a way to move from a signal to its evidence, a proposed action and an expected outcome, the operation chases symptoms, and the same revenue problems recur because no one owned the right fix.
The business impact
Poor revenue-cycle prioritisation leaks cash and compounds. Delayed cash - the highest-impact denial or AR issue waits while effort goes to a louder but smaller one. Recurring denials - a root cause that is never owned keeps generating the same denials, so the backlog refills as fast as it is worked. Wasted effort - teams work the wrong queue because the diagnosis was signal-chasing, not evidence-led. And unmeasured outcomes - actions are taken but their effect on the revenue outcome is never confirmed, so the operation cannot learn.
The decision this play protects is 'what do we work first, and why' - the single most consequential daily judgement a revenue-cycle leader makes, and the one most exposed to being wrong.
Which applications address it and how they chain
The play chains the outcome and evidence applications in diagnostic order.
- RCM Operations (https://rcm-ops.demo.timechamp.io/#/mission) - Mission Control and the role-sensitive briefings take a signal (denials, SLA, backlog, a payer) to evidence, a proposed action and an outcome hypothesis. This is the spine of the diagnosis and where an operational owner and review period are agreed.
- RCM Insights (https://rcm-insights.demo.timechamp.io/) - lets the leader ask a specific question tied to the decision and inspect the supporting data, coverage and scope, so a prioritisation is backed by evidence rather than a fluent dashboard.
- Work Output (https://work-output.demo.timechamp.io/) - tests whether the issue is really an output or cost problem (is the team producing clean claims at acceptable cost) rather than purely a payer problem.
- Quality and Call QA (https://quality.demo.timechamp.io/) - traces a recurring documentation or coding defect that is generating denials, so the fix is upstream prevention, not just rework.
The chain: signal to evidence and proposed action (RCM Operations) - interrogate the data and scope (RCM Insights) - is it output/cost (Work Output) - is it a recurring defect (Quality and Call QA) - owned action with a review period.
The value story and how it is measured
The value is evidence-led prioritisation of the revenue issue that matters most, with an owner and a review period, so the operation works the highest-impact problem and can prove the action changed the outcome.
Measure against the customer's agreed definitions: agreed AR, denial and TAT measures and validated outcomes from RCM Operations; evidence inspection and data coverage/freshness from RCM Insights; cost per clean unit from Work Output; recurring errors from Quality and Call QA. Crucially, track the four value types separately - recoverable value (what could be recovered), billed value, collected cash (what actually arrived) and capacity value (freed time) - because conflating them produces a number that cannot be defended.
The honest boundary - what this does not solve
This play prioritises and evidences the revenue issue; it does not collect the cash by itself or prove an outcome in advance. Be explicit, because revenue claims are the most scrutinised.
- Keep the four value types separate: recoverable value, billed value, collected cash and capacity value are different things. Recoverable is an estimate, collected is fact, capacity is freed time - never present one as another, and never add capacity value to a cash figure.
- Outcome hypothesis is not an outcome: RCM Operations proposes an action and an expected outcome; the outcome is validated only after the action is taken and measured. A hypothesis is a direction, not a result.
- Evidence depends on scope and freshness: an RCM Insights answer is only as good as its data coverage, freshness, metric definitions and role scope - an answer without suitable coverage must not become a customer commitment.
- Definitions are the customer's: denial, AR bucket, TAT and 'clean claim' definitions are the customer's and must be validated with product before any figure is quoted.
- Diagnosis is not execution: the play points at the right fix and its owner; the operation still has to do the work and the customer's own systems close the loop.
Interpretation check
Recoverable value, billed value, collected cash and capacity value are four different things and must stay separate. An outcome hypothesis from a briefing is a direction to validate, not an achieved result, and an evidence answer is only valid within its data coverage, freshness and scope.
In the field
KnackGlobal RCM (largest customer, active ProHance displacement): the revenue-cycle leader needs to show which denial or AR issue to work first and prove the action moved cash - exactly what an activity-only incumbent cannot do. This play delivers the signal-to-evidence-to-owned-action diagnosis and the cost-per-clean-claim and validated-outcome measures, while keeping recoverable, billed, collected and capacity value rigorously separate.
Practical task
In the demo, run a revenue-cycle diagnosis: take one signal through RCM Operations to evidence, a proposed action and an outcome hypothesis; size it with an inspectable RCM Insights question; check whether Work Output or Quality and Call QA reveals an output or defect cause; and agree an owner and review period. Write the four value definitions on one line each and the sentence you would use to stop a recoverable figure being treated as collected cash.
Self-check
A leader points at a recoverable-value figure and asks 'is that cash we will collect?'. Answer honestly.
No. Recoverable value is an estimate of what could be recovered; collected cash is what actually arrives; billed value and capacity value are different again. Keep the four separate - quote collected cash only once it is realised, and never add freed capacity to a cash number.
Two revenue problems are loud this week. How does the play decide which to work first?
By evidence and impact, not volume of noise. RCM Operations moves each signal to its evidence, proposed action and outcome hypothesis; RCM Insights sizes each with inspectable data and scope. The one with the larger evidenced impact on the revenue outcome wins, with an owner and review period.
Why bring Work Output and Quality into a revenue-cycle diagnosis?
Because a 'payer problem' is often an output or defect problem in disguise. Work Output shows if the team is producing clean claims at acceptable cost; Quality and Call QA traces a recurring documentation or coding defect driving denials - so the fix is upstream prevention, not endless rework.
An RCM Insights answer looks confident but the data coverage is thin. What do you do?
Do not let it become a customer commitment. Inspect coverage, freshness, metric definitions and role scope first; a fluent answer on insufficient data is a liability. Refine the question or widen the data before acting on it.
The briefing proposes an action with an expected outcome. Can you promise that outcome to the customer?
No - it is an outcome hypothesis. Agree the owner and review period, take the action, then validate the actual outcome against the agreed AR/denial/TAT measure. The hypothesis sets direction; only the measured result is a claim.
Verified vs demo. The briefings, the ask-and-inspect flow, the output and quality views are demo-shown on seeded data. The customer's actual payer/client scope, denial/AR/TAT definitions, data sources, coverage and freshness, and any realised cash outcome must be validated with product and measured in the account; outcome hypotheses are directions to confirm, not results.