Clarrio Platform · B2B
One longitudinal health record for care and research teams
A longitudinal health intelligence platform aligning clinical, drug and treatment, and behavioral information on one shared timeline. Dashboards, cohorts, research exports and APIs preserve the source context.
Engagement
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Data completeness
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Follow-ups
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Capabilities
Product capabilities for longitudinal health data
Dashboards
Program, clinic and population views.
Cohort views
Define cohorts and follow them over time.
RWE & research exports
Harmonized, analysis-ready datasets.
EHR integration
EHR-native but EHR-agnostic.
APIs
Bring Clarrio.ai data into your own tools.
Standardized at ingestion
FHIR, HL7, LOINC, SNOMED CT and more.
Overview
Longitudinal records, dashboards, and data exports
The Clarrio Platform is where organizations work with the longitudinal record. Data from apps, wearables, labs and EHRs is standardized at ingestion so it can be compared over time and across people.
From there, teams build cohorts, monitor programs in dashboards, export analysis-ready datasets for research and connect Clarrio.ai to their own tools through APIs and EHR integration.
Clarrio.ai / Program workspace
Clarrio Platform
Member cohort
Records
4
In review
2
Window
12 mo
| Record | Complete | Review context |
|---|---|---|
| 94% | Sleep context | |
| 87% | New lab result | |
| 91% | Training change | |
| 82% | Check-in due |
Illustrative · synthetic data
How it works
From source to insight in Clarrio Platform
- 1
Ingest
App, devices, labs, EHR and files via FHIR, HL7 and APIs.
- 2
Standardize
Mapped to LOINC, SNOMED CT and common units.
- 3
Link
One longitudinal timeline per consented person.
- 4
Analyze
Cohorts, dashboards and Intelligence on the same record.
- 5
Deliver
Exports, APIs and EHR write-back.
Data flow & integrations
Data sources, integration pathways, and outputs
Clarrio Platform sits between the sources your organization already has and the people who need to act on them. Select a node in the diagram to see the role it plays.
EHR (FHIR/HL7): Epic, Oracle Health and other FHIR R4 / HL7 systems.
Features in depth
Detailed product features and worked examples
Program dashboards
Views for clinics, programs and populations.
- Engagement and completeness by source
- Review queues for care teams
- Configurable per role
Illustrative example
A clinic lead sees which members have not synced data in 14 days.
Synthetic example for illustration; not a real person or measured outcome.
Cohort builder
Define a population once and follow it over time.
- Filters on profile, period, measures and events
- Saved, versioned definitions
- Shared between clinical and research teams
Illustrative example
“Members enrolled in Q1 with 8+ weeks of sleep data and a baseline lab panel.”
Synthetic example for illustration; not a real person or measured outcome.
RWE & research exports
Harmonized, analysis-ready datasets.
- Consented, de-identified or aggregated outputs
- Data dictionary included with every export
- CSV, Parquet and FHIR bundles
Illustrative example
A quarterly export with one row per person-week and documented variables.
Synthetic example for illustration; not a real person or measured outcome.
EHR integration
EHR-native but EHR-agnostic.
- Epic and Oracle Health connectivity
- Summaries available in clinician workflow
- No duplicate data entry
Illustrative example
A between-visit summary appears alongside the next appointment.
Synthetic example for illustration; not a real person or measured outcome.
APIs
Bring Clarrio.ai data into your own tools.
- FHIR R4 and REST endpoints
- Scoped keys and role-based access
- Webhooks for new observations
Illustrative example
Push weekly cohort metrics into an existing BI dashboard.
Synthetic example for illustration; not a real person or measured outcome.
Data
Standards used at ingestion
| Source | Examples | Cadence |
|---|---|---|
| Exchange | FHIR R4, HL7 v2 | EHR and partner systems |
| Labs | LOINC with UCUM units | Results and biomarkers |
| Clinical terms | SNOMED CT, ICD-10 | Conditions and findings |
| Medications | RxNorm / ATC | Prescriptions and logs |
Before and after
How the product supports team review workflows
Without Clarrio Platform
- Data spread across apps, devices, labs and EHR
- Each analysis starts with manual cleaning
- Different teams keep different versions of the truth
With Clarrio Platform
- One standardized longitudinal record
- Saved cohorts reused across teams
- Exports and APIs from the same source
Who uses it
Different roles, different answers
Clinic or program lead
“How is the program performing?”
Dashboards on engagement and completeness.
Data & research
“Can we analyze this without months of cleaning?”
Documented, analysis-ready exports.
IT & integration
“How does this fit our stack?”
FHIR/HL7 interfaces, APIs and access controls.
FAQ
Clarrio Platform questions
Longitudinal health intelligence
The platform aligns three dimensions of the patient journey
Align clinical observations, medication or intervention history, and daily-life context without losing source meaning. Available data and review boundaries depend on the program.
One person / one shared timeline
Illustrative research data · Synthetic demonstration
Health and disease trajectories
Reported headache days / week
Treatments and interventions
Exposure · dose · reported experience
Behavior and daily life
Average sleep
Week 4 / Disease4 days · Symptom diary
Week 4 / TreatmentDose change · 20 mg daily recorded
Week 4 / Behavior6.1 h · Lower activity reported
Separate measures and independently scaled channels; values appear in their own units. Temporal alignment does not establish causality.
Benefits in practice
How this product fits a care or evidence workflow
Pharma & biotech
Start with the evidence question a decision-maker actually needs answered
Routine records can describe encounters and prescriptions while leaving daily function, symptom burden and experience under-observed. Adding remote data can fill selected gaps, but a large dataset is not automatically decision-ready. Population selection, questionnaire suitability, exposure timing and missing observations can all change the interpretation of a longitudinal analysis.
01
Specify the evidence objective
Define the population, observation window, outcome concept and intended decision before selecting sources.
02
Collect and reconcile
Combine appropriately scoped Patient App reports with available clinical and device data in the Platform; keep reported experience separate from recorded events.
03
Review before analysis
Use source-aware review to inspect context and quality. Agree variable definitions, analysis methods and limitations with the study team.
For the professional team
Clinical development, medical affairs and HEOR teams can work from a common record while asking different questions. Clinical operations needs coverage by site and timepoint; an evidence analyst needs definitions and provenance; medical affairs needs an account of the population and study limitations. Those outputs should not be treated as interchangeable.
For patients, members and caregivers
Participants have a structured way to contribute the experience that encounters may miss. Questions should be meaningful, proportionate and appropriate to the population. Explain the study purpose, who reviews submissions and whether reporting receives an individual response; a research diary is not an emergency service.
For payers and program funders
A payer-facing evidence package should connect patient experience to a defined decision question and relevant population. Include baseline characteristics, follow-up, missingness and the analysis method. Patient-reported experience can complement other evidence, but it does not alone establish comparative effectiveness, economic value or a coverage recommendation.
- Population definition
- Eligibility, enrollment dates and relevant baseline characteristics
- Longitudinal variables
- Source, units, assessment window and missing-data conventions
- Evidence limitations
- Selection, confounding and generalisability to the decision population
Workflow illustration · not a customer result or live record
What to evaluate before making a claim
Assess suitability against the research question: provenance, completeness, accuracy and relevance. Predefine the analysis rather than choosing a favorable pattern after viewing the results. Publication and payer use require appropriate scientific review.
Explore evidence and evaluationCROs & research teams
Make remote collection operationally accountable across sites
A participant may be enrolled but not connected, connected but not reporting, or reporting outside the intended window. Treating all three situations as one completion measure hides different operational problems. Distributed studies need clear responsibility for connection support, participant contact and data queries, alongside a protocol that defines what should be collected.
01
Translate the protocol
Map each requested observation to a source, collection window and responsible study role.
02
Inspect site coverage
Review available Platform records by participant, site and timepoint; distinguish technical gaps from missed questionnaire windows.
03
Resolve and document
Route issues to the agreed study owner and preserve the context of any correction before producing sponsor-agreed outputs.
For the professional team
Study managers can discuss a specific missing source rather than a generic incomplete record. Data managers can reconcile dates and definitions before analysis. Site teams need a manageable contact process that fits the protocol, not an additional dashboard with no assigned owner. Integration and export requirements belong in the study scope.
For patients, members and caregivers
Participants benefit from a predictable reporting schedule and a clear contact for technical or study questions. Reduce duplicate questions where possible and review whether device access, language or caregiver support affects participation. Remote collection should supplement the protocol, not silently remove necessary visits.
For payers and program funders
When study outputs will later support HEOR or payer appraisal, preserve the link from collection methods to the evidence question. Transparent follow-up and source quality help reviewers understand the dataset, but improved operational completion does not prove that a study conclusion is unbiased.
- Collection plan
- Source and expected window for each protocol observation
- Issue review
- Missing source, affected window and responsible study role
- Sponsor handoff
- Defined variables with reconciliation notes and known limitations
Workflow illustration · not a customer result or live record
What to evaluate before making a claim
Separate enrollment, connection availability, valid observations and scheduled report completion. Compare sites only with denominators and follow-up windows that make the comparison meaningful. Document reasons for gaps where available rather than imputing normal values.
Explore evidence and evaluationPayers & health economics
Distinguish richer patient context from evidence of value
Claims and encounter datasets can describe recorded utilization without fully capturing function, experience or the practical burden of a program. Patient reports add a different perspective, but they can be incomplete or drawn from a population unlike the members covered by a payer. The task is to establish whether those observations are suitable for a specific appraisal question, not to turn engagement into a savings claim.
01
Define the appraisal question
Specify the relevant covered population, decision, comparator where needed and evaluation horizon.
02
Assess data suitability
Review provenance, participation, measurement windows and missingness before examining results.
03
Build the evidence account
Combine appropriately governed patient experience with relevant clinical or utilization evidence; document assumptions and limits for expert appraisal.
For the professional team
HEOR and population-health teams can examine how reported experience varies across the observation period and which groups are represented. A useful output explains the denominator behind a measure and the observations excluded from analysis. Governance must also define the permitted purpose of any linkage to payer-held data.
For patients, members and caregivers
The member perspective should remain visible in a program evaluation: burden, everyday function, access and what participants consider meaningful. Reporting should not become an unexplained condition of receiving services. Explain the collection purpose and permitted uses in a way members can understand.
For payers and program funders
Decision support means an inspectable evidence package, not an automatic reimbursement verdict. Include the target population, relevant baseline differences, data sources, follow-up and limitations. Cost and utilization conclusions need appropriate data and analysis; neither can be inferred from app activity or observation coverage.
- Decision context
- Population, appraisal question and evaluation horizon
- Suitability account
- Provenance, completeness, accuracy and relevance
- Interpretation boundary
- What the analysis can support and what remains uncertain
Workflow illustration · not a customer result or live record
What to evaluate before making a claim
Separate program participation, patient-reported experience, clinical outcomes and economic endpoints. Define each before collection, use an appropriate comparison where the question requires one and examine whether findings transfer to the covered population.
Explore evidence and evaluationIntegrations
Data sources and integration options
- Epic
- Oracle Health
- Mayo Clinic Platform
- Microsoft Azure Marketplace
- FHIR R4 / HL7
Security & trust
Health data access, consent, and security
- Sovereign-cloud compatible
- Role-based access controls
- Consented, aggregated data for research
Used in
Solutions using this product
Longevity & preventive health
Understand how health status, interventions and lifestyle behaviors evolve together over time. Clarrio.ai aligns those dimensions on a shared longitudinal member timeline.
See solutionPharma & biotech
Investigate disease trajectories, treatment exposure and patient experiences in real-world settings. Clarrio.ai aligns clinical, treatment and behavioral observations to support defined research questions.
See solutionHealth systems & hospitals
Connect clinical information with longitudinal treatment and patient-generated data for research and appropriate healthcare workflows. A shared timeline preserves source context for professional review.
See solutionThe platform
How the app, platform, and intelligence work together
Clarrio Intelligence
A health-specific language and reasoning layer over the harmonized record. It answers cohort and patient-level questions, summarizes trajectories and surfaces trends.
Explore this workflowClarrio Patient App
The Patient App combines patient engagement, structured reports, and connected observations in configurable disease and longevity profiles. Personal views help members revisit patterns without interpreting them as treatment advice.
Explore this workflowRequest a demo
Explore disease, treatment and behavior together
Explore how clinical, treatment and behavioral information align around your program and the research or review questions your team needs to answer.
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