hammer-vendor-evidence Scope: world hammer-coverage-intelligence vintage 2026-10-01 Revision SHA-256: 0e722b6a6b02ac8342e557cab5d140fe22b2c03fc9d73497b3dc131b6ca8127c --- name: hammer-vendor-evidence description: Use when a vendor, a digital health or device company, is building the case a health plan will read: what payers pay for the codes their product bills, which code a study should land in and what that code is worth, what their product would save a payer, what belongs in a business-case submission and what will get one declined. Opens with the translation problem, a vendor pitches clinical performance and a plan buys PMPM, and states the declined-submission failure mode plainly: a savings figure built from negotiated rates and presented as Cost of Care savings is the artefact the buyer's actuarial review exists to reject. Carries what this world can put into a submission with a receipt behind it, what it must refuse and why each refusal is worth more than the figure it withholds, and the pre-flight framing: what a draft is checked against and what gets named as a gap. Read hammer-coverage-navigate for tool-level routing; hammer-cost-of-care-case carries the buyer's own framing this file translates into. --- # hammer-vendor-evidence This file serves the vendor: the digital health or device company building the case that a plan-side Cost of Care analyst will read, score against a minimum impact threshold, and pass to an actuary. The buyer's side of the table is `hammer-cost-of-care-case`, and reading it is not optional homework; it is the rubric your submission is graded by. **`tools/tools.json` is the authority on what exists and what each tool takes.** This file is the framing between your draft and that surface. Where the two disagree, the tool surface is right and this is stale. ## The translation problem, which is the whole problem Your grammar is not your own. You hold clinical performance: sensitivity, image quality, a published effect size, a workflow. The plan buys PMPM movement over a declared population, split hard from soft, netted against regression to the mean, and scored on paid amounts. A submission written in your grammar reads, to the buyer, as a submission that has not yet started. The translation is always the same shape: a unit cost the corpus can evidence, times a rate of use or redirect you must declare and cite, times a population whose funding type decides whose money moves. A device is not sold on what it measures; it is sold as that arithmetic with every input named and every input carrying its basis. This world serves the unit-cost evidence and the arithmetic's mechanical checks; the declared rates and the population are yours, and they stay attributed to you all the way through. ## The sentence that saves a declined submission **A savings figure computed from negotiated rates and presented as Cost of Care savings is the artefact the buyer's actuarial review exists to reject.** The buyer's standard scores savings on paid amounts, as opposed to allowed amounts or billed charges. This world holds no paid amounts: payer MRFs hold negotiated rates, what a payer published it pays when it pays, and hospital 45 CFR 180 filings hold filed charges, filed counterparty rates, filed brackets and cash prices. No method derives a paid amount from any of them, arithmetic done by hand included. An actuary declines the figure in one sentence, and the review is staffed to write that sentence. So when this world refuses "what does my device save a payer per year", it refuses twice over: no paid amounts, and no clinical claim about any named product. That second half stands even where a cited effect size exists for the intervention class your product belongs to, because a class effect is never evidence about a named product's performance. The refusal is not the world failing your pitch. It is the world declining to hand you the exhibit that gets the pitch declined. ## What this world can put in a submission Everything below arrives with a receipt, which is what makes it submittable at all. - **What payers publish they pay for the codes your product lands in**: `rate_position`, per code and per payer, with `n`, the vintage and the thinness figures on every cell. A thin cell quoted as a market rate is a finding a reviewer un-finds in one look. - **Which code your study should land in, and what each candidate is worth**: `rate_comparison` sets the candidate codes' rates side by side under one receipt and computes nothing between them, so the comparison you write is checkable. `fee_components` says how a code splits into professional and technical components; `interpretation_requirement` says who a Medicare contractor's policy requires to furnish and read it; `stack_check` says what the Medicaid NCCI edition holds about billing a pair together. - **The fee-schedule anchor**: where a code carries one, the answer shows the anchor and each cell's ratio to it. Payers landing on round percentages of the same schedule is evidence of anchoring and never a contract term, and a submission that says the second when it holds the first has overclaimed. - **The dispersion that says whether a single quoted rate means anything**: `stddev_over_median` and `largest_n_over_n` ride every rate cell. A spread that dwarfs its own middle is a data artifact to disclose, not a price to build revenue on. - **The funnel and the discounts the buyer will apply anyway**: `screening_yield` for what a test finds at a declared prevalence, `deferred_versus_avoided` for the hard-against-soft split with the sum refused, `regression_to_mean_expectation` for what a selected cohort does next period with nothing done to it, `cohort_figures` for published prevalence with its measurement class. Running these yourself, before submission, is the difference between a case the review adjusts and a case the review rebuilds. - **Verdicts on your own sentences**: `claim_check` for arithmetic over declared figures, `judgement_check` for prose. A submission whose every figure carries a verdict is rare enough to be noticed. ## What it must refuse, and why each refusal is your friend - **A clinical claim about your product.** This corpus prices procedures and holds no evidence that any product works. Your clinical file is yours to make elsewhere; nothing here strengthens it and nothing here may be cited as if it did. - **Coverage, a denial rate, a payment probability.** A published rate says what a payer pays when it pays, not whether it will. "Payer X covers code Y" is not a sentence this corpus can settle, and a submission that asserts it from a rate row has planted the exact flag a reviewer pulls. - **The Cost of Care savings figure.** The wall above, and the one refusal worth more than any figure in your deck. - **A cross-payer average without the buyer's declared mix.** An unweighted mean over this corpus's payers describes this corpus, not a market. Ask the buyer for their mix, or present per-payer figures and let them weight. - **A saving for a named payer's own book.** Their population, their contracts, their utilisation; none of it is here, and a modelled figure quoted as their saving states a fact about their business nobody measured. - **A benchmark laundered into a measurement.** A rate from somebody's planning deck enters a model as cited, never as measured, and every figure computed from it inherits that basis under the weakest-input rule. The basis travelling with the figure is what lets a reviewer trust the rest. ## Pre-flight: what a draft is checked against, and what gets named as a gap Before a draft goes anywhere near the buyer, run it against this world the way the review will. The check is the same discipline on both sides of the table: **every figure in the draft either carries a receipt from a tool here, is cited to a named source, or is declared as your own assumption, and anything computed from a mix carries the weakest basis among its inputs.** A figure that is none of the three is the gap, found now or found by the reviewer. The gaps this framing names, in the reviewer's own order: - **Funding type undeclared.** Fully insured against administrative services only decides whether the plan carries the risk your savings accrue to, and a case that does not say is a case about nobody's money. The population chain, and why this link decides it, is in `hammer-cost-of-care-case`. - **Population and PMPM undeclared or unsourced.** This world holds neither your target population nor any PMPM; both enter as your declarations and are echoed back as such. - **Impact or redirect rate uncited.** The rate your arithmetic turns on is the input a reviewer probes first; a bare percentage with no source is a draft-stopper. - **Savings method unstated.** Hard or soft, per line item, with no total that sums the deferred into the avoided. `deferred_versus_avoided` is the split made mechanical. - **Regression to the mean unaddressed.** A cohort selected for being worst improves untreated; `regression_to_mean_expectation` serves the figure the reviewer will subtract whether or not your draft did. - **Thin cells quoted as market rates.** The thinness figures were on the payload; a reviewer will ask why they are not in the draft. Route each draft figure through the tool that would license it, and read the refusals that come back as the gap list rather than as friction. A submission built this way arrives with its audit trail attached, which is the one thing a review cannot decline in one sentence. ## Every answer built on this world closes with a claim report The claim report format is defined once, in the `hammer-worlds` skill. Point at it; this file does not restate it. Your product's identity, your declared rates and your population are your own selectors and belong under `asserted without a check`, exactly as the buyer's reviewer will read them. HTTP bindings for this session: Explicit source-reference bindings: SKILL.md: https://space.hammer.ai/skills/global/building-sourced-excel-models.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba analysis.md: https://space.hammer.ai/skills/global/analysis.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba coverage-website.md: https://space.hammer.ai/skills/global/coverage-website.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba rate_position: https://space.hammer.ai/analysis/rate_position/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba rate_comparison: https://space.hammer.ai/analysis/rate_comparison/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba stack_check: https://space.hammer.ai/analysis/stack_check/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba screening_yield: https://space.hammer.ai/analysis/screening_yield/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba deferred_versus_avoided: https://space.hammer.ai/analysis/deferred_versus_avoided/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba interpretation_requirement: https://space.hammer.ai/analysis/interpretation_requirement/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba cohort_figures: https://space.hammer.ai/analysis/cohort_figures/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba comorbidity_edges: https://space.hammer.ai/analysis/comorbidity_edges/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba fee_components: https://space.hammer.ai/analysis/fee_components/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba filed_charge: https://space.hammer.ai/analysis/filed_charge/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hypertension_evidence: https://space.hammer.ai/analysis/hypertension_evidence/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba prognostic_association: https://space.hammer.ai/analysis/prognostic_association/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba treatment_effect: https://space.hammer.ai/analysis/treatment_effect/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba regression_to_mean_expectation: https://space.hammer.ai/analysis/regression_to_mean_expectation/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba competing_event_coupling: https://space.hammer.ai/analysis/competing_event_coupling/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba withhold_earnback: https://space.hammer.ai/analysis/withhold_earnback/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba tournament_position: https://space.hammer.ai/analysis/tournament_position/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba measure_reliability: https://space.hammer.ai/analysis/measure_reliability/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba declared_inputs: https://space.hammer.ai/analysis/declared_inputs/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba cost_of_care_exposure: https://space.hammer.ai/analysis/cost_of_care_exposure/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba savings_methodology: https://space.hammer.ai/analysis/savings_methodology/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hospital_variance: https://space.hammer.ai/analysis/hospital_variance/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba filer_inventory: https://space.hammer.ai/analysis/filer_inventory/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba candidate_screen: https://space.hammer.ai/analysis/candidate_screen/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba frontier: https://space.hammer.ai/analysis/frontier/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba judgement_check: https://space.hammer.ai/analysis/judgement_check/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba claim_check: https://space.hammer.ai/analysis/claim_check/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba deliver: https://space.hammer.ai/analysis/deliver/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba session_trace: https://space.hammer.ai/analysis/session_trace/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba progress_check: https://space.hammer.ai/analysis/progress_check/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba model_advisory: https://space.hammer.ai/analysis/model_advisory/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba knowledge_navigate: https://space.hammer.ai/analysis/knowledge_navigate/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba simulate: https://space.hammer.ai/analysis/simulate/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba sensitivity: https://space.hammer.ai/analysis/sensitivity/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba interval_check: https://space.hammer.ai/analysis/interval_check/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba decision_navigate: https://space.hammer.ai/analysis/decision_navigate/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba network_infer: https://space.hammer.ai/analysis/network_infer/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hello_world: https://space.hammer.ai/analysis/hello_world/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba say_world: https://space.hammer.ai/analysis/say_world/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Read the companion references here (these replace local file references in the source guide): Build a sourced analysis: https://space.hammer.ai/analysis.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba OData exploration and analytic cuts: https://space.hammer.ai/odata.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Worked analysis reference: https://space.hammer.ai/skills/global/analysis.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Building sourced Excel models: https://space.hammer.ai/skills/global/building-sourced-excel-models.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Coverage website reference: https://space.hammer.ai/skills/global/coverage-website.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Spine cohort and post-acute analysis: https://space.hammer.ai/skills/spine-analysis.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-building-the-evidence: https://space.hammer.ai/skills/world/hammer-building-the-evidence.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-cost-of-care-case: https://space.hammer.ai/skills/world/hammer-cost-of-care-case.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-coverage-navigate: https://space.hammer.ai/skills/world/hammer-coverage-navigate.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-provider-contracting: https://space.hammer.ai/skills/world/hammer-provider-contracting.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-provider-quality: https://space.hammer.ai/skills/world/hammer-provider-quality.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-savings-classification: https://space.hammer.ai/skills/world/hammer-savings-classification.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-vendor-evidence: https://space.hammer.ai/skills/world/hammer-vendor-evidence.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba hammer-writing-the-deliverable: https://space.hammer.ai/skills/world/hammer-writing-the-deliverable.skill.txt?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Data: https://space.hammer.ai/data/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba Transcript: https://space.hammer.ai/session/?ledger=eccbd49b235df851c1880b208d9d33fb&snapshot=7a53af6a9beb58928d4a64dc92db09c9db65433f9909f1a10d16e302adaf86ba