Contract analytics models what each payer agreement should pay, compares that to what was actually received, and quantifies variance by contract, service, and period. It calculates expected reimbursement from terms your team has entered and verified. It does not interpret a contract, determine that an underpayment is owed, or pursue recovery.
The difficult part is never the arithmetic. It is that contract terms live in signed documents written in language that resists modelling, and a modelled rate built on a misread term produces confident variance findings that waste months of staff effort chasing nothing. Taction builds contract analytics where every modelled term is attributable to a person who verified it against the document.
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Contract analytics turns agreement terms into a computable expected payment for each claim, then compares expected against actual remittance to produce variance. Doing that credibly requires modelled terms, clean claim and remittance data, and enough detail to explain any individual variance rather than only a total. It sits inside a wider healthcare data analytics practice, and it draws on the definition discipline our analytics consulting work brings to modelling questions before anything is calculated. Contract analytics reports variance. Deciding whether that variance is owed, and pursuing it, remains work for your people.
Rates, carve-outs, stop-loss provisions, escalators, and payment methodologies are entered as structured terms with the source clause referenced. Modelled terms carry the name of whoever verified each one against the signed document.
Each claim is priced against its governing contract, effective dates, and applicable methodology to produce an expected payment figure. Expected payment depends on the accuracy of the terms behind it rather than on calculation sophistication.
Actual remittance is compared to expected and differences classified by likely cause, whether contractual, coding, eligibility, or adjudication related. Variance classification directs work to whoever can actually resolve each category.
Payment accuracy, timeliness, and adherence to agreed methodology reported per payer, per contract, and per period over time. Compliance patterns are considerably more useful in negotiation than any single disputed claim.
Proposed rates and methodologies applied to historical volume so the financial effect of a change is visible before signature. Negotiation models show consequences under your real case mix rather than under an illustrative example.
It does not interpret contract language, determine legal entitlement, decide whether to dispute, or pursue recovery on your behalf. Those decisions belong to your revenue cycle leadership, your contracting team, and where relevant your counsel.
The recurring failure in this category is a variance report nobody trusts, usually because a term was modelled from a summary rather than the executed agreement and nobody can say who entered it. We build term attribution and clause referencing first, then calculation, then variance workflow. That order costs visible progress early and is the only route to findings your staff will actually work. Claim and remittance data quality sits underneath all of it, so our data quality practice is part of the build rather than an assumption about your source systems.
Structured term entry with clause references, effective dating, amendment history, and the verifier named against each modelled provision. Attribution is what makes a variance finding defensible when a payer disputes it.
Terms connected to executed documents held in your contract management system so the model and the agreement stay linked. Document linkage means a reviewer can check the clause in seconds.
Pricing logic covering fee schedules, percentage arrangements, case rates, per diems, outliers, and carve-outs with methodology recorded per calculation. Methodology records explain why a specific claim priced as it did.
Findings queued by classification, value, and payer with ownership, ageing, and outcome recorded through to resolution. Outcome recording is what turns variance reporting into a measurable recovery process. Outcomes feed the trend reporting as well.
Claim, remittance, and adjustment data assembled through our claims processing practice with denial and adjustment reasons preserved. Reason preservation distinguishes a contractual variance from an adjudication outcome. Adjustment codes are preserved rather than summarised away.
Variance, compliance, and yield reporting to claim level using our data visualisation practice. Claim-level drill-down is what lets a contracting director defend a figure in a payer meeting. Totals alone persuade nobody.
We publish no figures on recovery amounts, underpayment rates, or negotiation outcomes, because those depend entirely on your contracts, your payer mix, and how your team works the findings. What we deliver is instrumentation so your team measures impact against its own data. The honest framing is narrow: this work makes expected payment computable and variance visible with enough detail to act on. Whether anything is recovered depends on your staffing, your payer relationships, and decisions people make about which findings are worth pursuing at all. Findings still have to be worked by people.
Every modelled provision references its clause and names its verifier, so a disputed finding can be checked against the executed agreement rather than argued from memory. Auditable terms are the foundation of everything else here.
Findings arrive classified by likely cause rather than as an undifferentiated list of dollar differences requiring investigation from scratch. Classified variance routes work to the team that can resolve it.
Claim-level detail with contract reference means a specialist can act without reconstructing the calculation first. Actionable detail determines whether a variance queue gets worked or quietly ignored. Reconstruction time is what kills a variance queue.
Payment accuracy and timeliness trends per payer accumulate into evidence rather than anecdote, which is what changes a negotiation conversation. Pattern evidence outperforms individual disputed claims consistently. Individual disputes rarely change payer behaviour.
Proposed terms are tested against your historical volume and case mix before signature rather than modelled optimistically afterwards. Pre-signature modelling surfaces the effect of a methodology change. Illustrative examples flatter proposed terms.
A modelled term read wrongly produces confident findings that waste real staff effort. Model risk is why we build verification and attribution rather than only calculation speed. Verification costs time and prevents wasted pursuit.
We start with term modelling on a small number of contracts, because the honest test of this work is whether variance findings survive payer challenge, and that depends on term accuracy rather than platform features. Discovery is paid and time-boxed and produces an itemised fixed-scope estimate with a build or configure recommendation. Where your claims system or clearing house already produces reliable expected payment, we scope only the gap. Delivery runs in short increments with your contracting and revenue cycle staff working real findings from the first increment onward. That order is deliberate.
Executed agreements, amendments, effective dates, and current summaries inventoried so the model has an authoritative source. Inventory work frequently reveals amendments nobody had modelled anywhere. Summaries frequently differ from the executed agreement.
A small set of contracts modelled fully, with clause references and named verifiers, then tested against historical remittance. Pilot testing validates the approach before scale. Scale follows a pilot that survived remittance testing.
Claim, remittance, adjustment, and eligibility data assessed for completeness and the linkage needed to price accurately. Data gaps are reported honestly rather than modelled around. Unpriceable claim types are named rather than approximated.
Pricing logic, variance classification, and the working queue built in increments alongside our accounts receivable practice. Queue design is agreed with the people who will work it. Ownership and ageing are included.
Findings compared against variances your team already identified manually, with every difference investigated to cause. Known-case validation is the credibility test that matters. Averaging two figures would defeat the purpose entirely.
Contracts modelled progressively with term ownership transferred, then handover covering amendment handling and verification practice. Term ownership stays with your contracting team permanently. Amendments arrive continuously and need an owner who handles them.
We build modelling, calculation, and workflow on your data platform. Contract interpretation is legal and commercial work performed by your team, and we implement the terms they verify rather than reading agreements ourselves. Rate benchmark datasets, where used, are licensed directly by your organisation. Compliance covers HIPAA safeguards on claim data, access control appropriate to contractual and financial information, and audit sufficient to reconstruct any expected payment figure including the term version applied at the time. Our chargemaster practice covers the pricing side, where gross charges affect expected payment under some methodologies.
Contract language is interpreted by your contracting team and, where needed, your counsel. We model the terms they verify and record who verified each provision and when. We do not read agreements ourselves.
Amendments and corrections create new term versions with effective dates rather than overwriting the previous model. Version history explains why a claim priced differently in two periods. Corrections are additive rather than destructive.
A variance is an indication requiring human review before any dispute, appeal, or recovery action proceeds. We decline to build automated dispute submission from unreviewed variance findings. Review precedes any payer contact.
Comparative rate data belongs to whichever provider you subscribe to, and we integrate what you hold. No partnership or endorsement with any benchmarking organisation is claimed anywhere. Your subscription remains yours throughout.
Contract profitability informs organisational and negotiation decisions only. We decline to build anything that routes, prioritises, or restricts individual patients by contract yield. That line appears in our proposals rather than in conversation.
Terms, versions, verifiers, claim inputs, and calculated expectations are retained so any figure can be reproduced exactly. Reproducibility is what survives a payer challenge months later. Recollection is not evidence in a payer meeting.
We have been building healthcare software since 2013, which is over 12 years, and we have delivered more than 200 healthcare projects. We built our own EHR platform, Voyant Health, so charge capture, claim construction, and the relationship between clinical activity and billed amounts are working knowledge rather than assumptions from a specification. We are ISO 27001 certified, our leadership brings more than 20 years of personal experience in the field, and we work from four US offices in Chicago, Cheyenne, Austin, and Sacramento. Our payer claims work covers the adjudication side of the same problem.
We build clause referencing and named verification before pricing logic, because a variance nobody can trace back to an executed agreement will not survive its first payer conversation. Attribution is the whole credibility position.
Findings are reconciled against variances your team already identified manually, and every difference is investigated rather than averaged. Known-case validation is how we earn confidence. Confidence is earned rather than asserted in a demonstration.
Building Voyant Health means we understand charge capture, modifier behaviour, and how claims are actually constructed rather than how a pricing model assumes they are. Modifier behaviour in particular defeats simplistic pricing models.
Taction is ISO 27001 certified, with documented access control, encryption, and change control that stands up to a customer security review without improvisation on our part. Contractual data carries role-based access with logging.
Where your claims system or clearing house already prices contracts reliably, we recommend using it and scope only the gap. That advice reduces our revenue and your maintenance burden. considerably.
Four US offices in Chicago, Cheyenne, Austin, and Sacramento, with delivery overlapping your working hours through term modelling and validation cycles. Escalation reaches a named delivery lead rather than a shared support queue.
Pricing turns on how many contracts are modelled, how complex their methodologies are, and whether a data platform already exists. The tiers below cover engineering. Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly. Rate benchmark subscriptions are held directly by your organisation, and contract abstraction performed as a service, which we do not provide, sits outside our engineering estimate entirely at every tier. Where an existing pricing engine already covers most of your contracts, the honest scope narrows and we quote it that way rather than proposing a replacement.
$40,000 to $80,000 for term modelling and expected payment calculation across a limited contract set, with variance detection and a working queue. Negotiation modelling and compliance reporting follow in a later phase.
$80,000 to $200,000 for full contract modelling, pricing engine, variance classification and workflow, compliance reporting, and negotiation modelling. This tier covers most single-entity provider organisations that we are asked to scope.
Starting at $200,000 for multi-entity organisations with many contracts, complex carve-outs, several source systems, and consolidated governance across entities. Contract count and methodology complexity drive the figure more than claim volume.
A paid, time-boxed discovery phase produces a contract inventory, a modelled pilot tested against remittance, a data assessment, a build or configure recommendation, and an itemised estimate. The pilot model is yours regardless.
Contract count, methodology complexity, entity count, and claim data quality. Carve-outs and outlier provisions cost far more to model correctly than standard fee schedules. Standard fee schedules model quickly by comparison with them.
Budget annually for amendment modelling, term verification, payer methodology changes, and source system change handling. Amendment volume is the recurring driver rather than platform support. Payer methodology changes arrive without warning most of the time.
If your variance findings get challenged and nobody can point to the clause behind the expected payment, start with a modelled pilot. A paid discovery phase gives you a contract inventory including amendments nobody had modelled, a small set of contracts modelled fully with clause references and named verifiers, that model tested against your historical remittance, an honest data assessment, a build or configure recommendation, and an itemised fixed-scope estimate. You keep the pilot model regardless of what you decide.
These are the questions revenue cycle directors, contracting leaders, and decision support analysts raise before scoping contract analytics. Several concern the accuracy of modelled terms, which is where the credibility of everything downstream actually sits. One concerns a use we refuse. Where an answer depends on your contract methodologies or claim data quality, the modelled pilot in discovery settles it quickly and is worth having on its own regardless of what you build. We would rather tell you that your clearing house already prices your contracts reliably than build a second engine you then reconcile against it.
Extraction can propose terms from a document, and we treat every proposal as a draft requiring human verification against the executed agreement before it prices anything. Contract language resists reliable extraction, and a misread provision produces confident variance findings that waste staff effort. Every modelled term names the person who verified it.
It identifies variances with enough detail to work, classified by likely cause, and tracks outcomes through resolution. Whether anything is recovered depends on your staffing, your payer relationships, and which findings your team judges worth pursuing. We make no claim about recovery amounts and would treat any vendor figure here with real suspicion.
Clearing house reporting typically flags adjudication differences against loaded fee schedules. Contract analytics models the full agreement including carve-outs, stop-loss, outliers, and methodology, then explains variance by cause. If your clearing house already prices your contracts reliably, we will say so and scope only what it genuinely misses.
No. A variance is an indication that expected and actual payment differ under a modelled term. Entitlement depends on contract interpretation and sometimes on legal analysis, which belongs with your contracting team and counsel. We also decline to build automated dispute submission from findings nobody has reviewed.
Yes, and that is one of the more valuable uses. Proposed rates and methodologies are applied to your historical volume and case mix so the financial effect is visible before signature rather than discovered in the first quarter. Modelling assumptions are documented alongside the result.
No. Contract yield informs decisions about negotiation, service configuration, and network participation at an organisational level. Wiring it into anything that routes, prioritises, or restricts an individual patient by contract profitability is a line we will not cross, and we state that in our proposals rather than waiting to be asked about it.
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