Chronic disease registry integration identifies and maintains condition cohorts from clinical data, and submits records to the disease and procedure registries your programme participates in. It identifies candidates, assembles data, and validates submissions. It does not diagnose, assign a patient to a condition, or attest a submission.
Registry work in chronic disease has two failure modes. Internal cohorts are built from a query somebody wrote years ago that nobody has validated against a chart since, and external society registries consume abstraction hours that were never budgeted. Taction builds both properly: phenotypes validated and published, and submission work reduced to review rather than reconstruction.
Our experts are ready to understand your business goals.
The category covers internal population registries used to manage a condition and external registries operated by professional societies and public programmes, which impose their own data dictionaries and submission cycles. Both start from cohort identification, and both live or die on it. It sits inside a wider healthcare integration programme, and it shares foundations with our clinical registry development practice, which covers building registries rather than feeding ones that already exist. We scope internal and external registries separately, since their governance, their obligations, and the people who own them all differ.
Patients are identified as candidates for a condition cohort using coded diagnoses, medications, laboratory results, and procedures. Candidate identification is not a diagnosis and never becomes one automatically. Clinicians confirm anything reaching the chart.
A phenotype is an explicit, testable definition of who belongs in a cohort. Our clinical phenotyping work covers definition development, including how each definition is validated and versioned. Definitions are testable rather than assumed correct.
Cohorts used operationally for care management, outreach, and gap closure within your organisation. Internal registries answer to your clinical governance rather than to an external dictionary. Refresh cadence and clinician trust matter most.
Society and programme registries define their own elements, validation rules, and submission windows. External submission requires conformance to their current dictionary version rather than your internal model. Windows are fixed and dictionaries change.
Where external elements exceed what structured data holds, an abstractor completes the record. Abstraction support means assembled evidence rather than any attempt to infer missing elements. Missing elements are flagged, never inferred.
It does not diagnose, add a condition to a problem list, score risk that affects access, or attest a submission. Those actions belong to clinicians and your programme leadership. Those boundaries are contractual.
Everything here rests on phenotype quality, so we build definitions first, validate them against chart review, and publish the validation result rather than asserting accuracy. After that the work is cohort maintenance, external submission, and gap reporting. Population-level use of these cohorts overlaps with our population health analytics practice, and we scope the boundary explicitly so two teams are not building the same denominators twice. Gap reporting comes last, because a care gap calculated on an unvalidated cohort produces outreach to the wrong patients and destroys clinical trust quickly and permanently.
Explicit inclusion and exclusion logic built with clinicians, versioned, and documented in language a clinician can audit. Definition transparency is what makes a cohort defensible. Logic is readable without an analyst translating it.
Definitions tested against chart review on a sample, with performance measured and reported honestly. Validation results are published to your governance rather than assumed adequate. Revalidation follows any material change to source data.
Cohorts refreshed on a defined cadence with entries, exits, and reasons tracked over time. Cohort churn is reportable, since unexplained movement usually signals a data problem. Refresh cadence is agreed with clinical owners.
Element mapping, record generation, local validation, and submission per registry with acknowledgement processing. Dictionary versions are configuration, since registries revise them regularly. Acknowledgement processing distinguishes a submitted record from an accepted one.
Evidence assembled per case with structured capture of the abstractor’s entries and their sources. Assembled evidence is where abstraction hours are recovered, not in inference. Entries record their source document too.
Care gaps, follow-up intervals, and measure alignment reported alongside your HEDIS reporting definitions. Definition alignment prevents two systems publishing different denominators. Denominators are reconciled with your measure definitions rather than calculated independently.
We publish no figures on cohort accuracy, control rates, or submission completeness, because those depend entirely on your documentation practice, your coding quality, and each registry’s rules. What we deliver is instrumentation so your team measures impact against its own data. The benefits are a cohort your clinicians will accept, published validation you can point to, and external submissions that consume review time rather than reconstruction time. Clinical improvement remains the work of clinical teams. Read the items below as cohort trustworthiness and reduced duplication rather than clinical outcome claims.
Explicit, validated, versioned definitions survive the first challenge from a sceptical physician. Definition transparency is the difference between a registry used and one dismissed. A wrongly included patient discredits an entire registry quickly.
Phenotype performance is measured against chart review and reported rather than asserted. Published validation is what makes downstream analysis defensible to anyone reviewing it. Performance is reported by subgroup, not as one figure.
Entries and exits are tracked with reasons, so cohort size changes have explanations. Churn tracking exposes coding and interface problems that a headline count conceals. Sudden growth usually means an interface changed.
Abstractors complete assembled records rather than reconstructing each case from scratch. Assembled records are where the external registry burden is genuinely reduced. Evidence assembly is where the external registry burden genuinely falls.
Registry cohorts and quality measure denominators use reconciled definitions. Reconciled definitions stop the meeting where two teams present different numbers. One definition serves the registry, the measure, and the reporting pack.
A computable phenotype is an approximation, and more registries do not produce better care. Approximation is why we validate, publish results, and revalidate after data changes. We publish the approximation rather than concealing it.
We start with the phenotype and its validation, because every downstream use inherits its errors. Discovery is paid and time-boxed and produces an itemised fixed-scope estimate with an honest build or configure recommendation. Where your EHR’s registry or population tooling already maintains the cohort adequately and the gap is external submission, we scope only that. Delivery runs in short increments with clinicians and abstractors reviewing working output each time. Clinical owners keep authority over every definition throughout, because a phenotype is a clinical statement rather than a technical configuration choice.
We establish which conditions and which external registries are genuinely in scope and why. Scope discipline matters, since each registry adds real recurring obligation. We will argue against a registry you cannot staff.
Inclusion and exclusion logic drafted with clinical owners and signed off in writing. Clinical sign-off precedes any implementation, because this is a clinical definition. Ambiguity is resolved clinically rather than technically.
A sample is reviewed manually against the definition and performance measured, including by subgroup. Subgroup performance is a gate rather than a footnote. Sample size is agreed upfront so the result means something.
Cohort maintenance, element mapping, and submission generation built in increments using our clinical data integration practice. Terminology mapping is counted rather than estimated. Mapping volume is counted against your actual catalogue rather than estimated.
We work through each external registry’s test, validation, and submission process alongside your team. Their windows govern submission timing, and we sequence around them. Their submission windows are fixed and cannot be brought forward.
Live running with churn and validation monitoring, then handover covering definition versioning and dictionary updates. Revalidation cadence is agreed and documented. Definition versioning and revalidation cadence pass to a named clinical owner.
We build phenotypes as explicit, versioned, auditable definitions and treat validation as an obligation rather than a nicety. Clinical data arrives through our EHR and EMR integration services, with terminology handled consistently. Compliance covers HIPAA safeguards, secondary use governance, and clear allocation of every clinical determination to a person. External registry programmes are referenced as market context: we support your submissions and claim no partnership, certification, or endorsement from any society or programme. We also record which definition version produced each cohort snapshot, so a count from last year remains explicable when somebody asks about it.
Every phenotype carries its logic, version, author, and validation result. Version records explain why a cohort count differs between two reporting periods. Definitions are stored as documentation rather than buried inside query code.
Cohort inclusion is an analytic classification, not a clinical diagnosis. No condition is written to a problem list automatically, and clinicians confirm anything that reaches the chart. Suspected undiagnosed cases become clinician review items instead.
Phenotype performance is measured by subgroup before deployment, because definitions built on coding and utilisation patterns behave unevenly. Adverse findings stop deployment. Findings are reviewed with clinical governance before any deployment proceeds.
We decline to build cohort-derived scores that restrict access to care, coverage, or services. Registry membership informs outreach and review, never a denial. That refusal is stated in our proposals rather than only in conversation.
Conditions, medications, and results map to coded concepts including SNOMED CT so definitions survive catalogue change. Coded definitions outlast local identifiers. Local identifiers change, and coded definitions survive those changes intact.
Research reuse runs under separate approvals, access control, and logging, with validation results attached. Purpose separation is architectural rather than procedural. Access is granted per approval and logged against it individually.
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 coded clinical data, result structures, and problem list behaviour are working knowledge rather than assumptions. 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. We will also tell you when your existing population tooling already maintains the cohort.
We validate phenotypes against chart review and publish the result. Published validation costs project time and is the only honest basis for using a cohort. Unvalidated cohorts are assertions rather than evidence.
Logic is written so a clinician can read and challenge it without an analyst translating. Auditable definitions are what earn a registry clinical acceptance. A sceptical physician is the test every definition must pass.
Building Voyant Health means we understand how diagnoses, results, and medications are actually recorded rather than how a phenotype assumes they are. Problem lists, results, and medication records each carry their own reliability profile.
Taction is ISO 27001 certified, with documented access control, encryption, and change control that stands up to a customer security review. Registry and cohort access is role-based, logged, and reviewed on a cadence.
We will argue against adding a registry you cannot staff the abstraction for. That argument reduces our scope and protects your data quality. The argument appears in the discovery report rather than after signature.
Four US offices in Chicago, Cheyenne, Austin, and Sacramento, with delivery overlapping your hours during validation review and registry testing. Escalation reaches a named delivery lead rather than a support queue.
Chronic disease registry pricing turns on how many conditions and external registries are in scope, phenotype complexity, and abstraction volume. The tiers below cover engineering. Third-party licensing, cloud infrastructure, data subscriptions, and hardware are separate from engineering cost and itemised clearly. External registry participation fees, submission tooling, and terminology licensing are vendor and programme costs quoted as separate lines, and abstraction staffing is your operational cost rather than ours. Abstraction staffing for external registries is your recurring operational cost, and we size it honestly during discovery rather than omitting it.
$40,000 to $80,000 for one condition with phenotype development, validation against chart review, cohort maintenance, and gap reporting. One condition, internal use only, with external submission deferred to a later phase.
$80,000 to $200,000 for several conditions with validated phenotypes, external registry submission, abstraction workspace, churn tracking, and measure alignment. This tier covers most single-organisation registry programmes that we are asked to scope.
Starting at $200,000 for multi-site programmes with central definition governance, several external registries, research access controls, and site variation reporting. Registry count and governance requirements drive the figure more than cohort size.
A paid, time-boxed discovery phase produces a condition and registry scope review, phenotype draft, data availability assessment, build or configure recommendation, and an itemised estimate. The phenotype draft is yours whether or not we build.
Condition count, external registry count, phenotype complexity, and abstraction element volume. External registries add recurring obligation well beyond their initial build cost. Poorly coded diagnoses shift the burden onto abstraction and chart review.
Budget annually for dictionary version updates, phenotype revalidation, terminology maintenance, and EHR upgrade regression testing. Revalidation is recurring rather than one-off work. Revalidation is scheduled work rather than something triggered by a complaint.
If your diabetes registry runs on a query nobody has validated since it was written, start with the phenotype. A paid discovery phase gives you a drafted definition your clinicians can audit, a validation plan against chart review including subgroup performance, a data availability assessment for the elements each external registry requires, a build or configure recommendation, and an itemised fixed-scope estimate. If your existing population tooling maintains the cohort adequately, we scope only the submission gap.
These are the questions population health leaders, service line directors, and quality teams raise before scoping registry work. Several concern phenotype trustworthiness, which is the question everything else depends on. One concerns whether adding a registry is wise at all, where the honest answer is sometimes no. Where an answer depends on your coding quality or a specific registry’s dictionary, discovery resolves it quickly and cheaply. We would rather scope one registry properly than three inadequately, and we will say that before a statement of work rather than after the abstraction backlog appears.
By validating it. We test the phenotype against manual chart review on a sample, measure performance including by subgroup, and publish the result to your governance. A cohort presented without validation is an assertion, and the first clinician to find a wrongly included patient will treat the whole registry as unreliable.
No. Cohort inclusion is an analytic classification used for outreach, review, and reporting. Nothing is written to a problem list automatically. Where a cohort suggests a patient may have an undiagnosed condition, that becomes a clinician’s review item rather than a diagnosis the software has made.
Only if you can staff the abstraction, and we will say so plainly. External registries impose recurring obligation measured in abstractor hours per case, and a half-completed registry submission damages your standing more than not participating. We would rather scope one registry properly than three inadequately.
Population health analytics covers stratification, programme measurement, and intervention targeting across your population. This page covers registry mechanics: phenotype definition and validation, cohort maintenance, and external submission conformance. They share denominators, which is exactly why we reconcile the definitions rather than letting each build its own.
Not in anything we build. We decline to construct cohort-derived scoring that restricts access to care, coverage, or services. Registry membership exists to prompt outreach and clinical review, and using it to deny something would invert the purpose of building the registry in the first place.
It becomes scheduled reconfiguration rather than an incident. Element mappings and validation rules are versioned configuration with a named owner tracking releases, and historical submissions retain the version that produced them so year-on-year comparisons stay explicable to anyone reviewing your data. We budget that reconfiguration annually rather than treating it as unplanned.
Your email address will not be published. Required fields are marked *
Our expert reaches out shortly after receiving your request and analyzing your requirements.
If needed, we sign an NDA to protect your privacy.
We request additional information to better understand and analyze your project.
We schedule a call to discuss your project, goals. and priorities, and provide preliminary feedback.
If you're satisfied, we finalize the agreement and start your project.