Clarrio.ai / Longitudinal health intelligence

Understand how disease, treatment, and patient behavior evolve together

Clarrio connects clinical, treatment, biomarker, and behavioral data into longitudinal health intelligence for pharmaceutical research, healthcare systems, and longevity clinics.

Clarrio connects clinical records, biomarkers, treatment information, and real-world patient data to build a longitudinal understanding of human health. From pharmaceutical research and healthcare systems to longevity clinics, our platform helps organizations explore health trajectories, treatment experiences, and the factors shaping patient outcomes over time.

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One person / one shared timeline

Illustrative research data · Synthetic demonstration

Therapeutic example

Health and disease trajectories

Reported headache days / week

Treatments and interventions

Exposure · dose · reported experience

Behavior and daily life

Average sleep

Shared time axis / weeks

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.

Interactive

How Clarrio.ai supports your organization's workflow

From real-world evidence to healthcare interoperability and longevity intelligence, discover how Clarrio turns connected health data into meaningful outputs.

Your challenge

Clinical records alone may not capture the full patient experience between visits.

What Clarrio.ai delivers

Longitudinal real-world datasets
Patient-reported outcomes
Wearable and behavioral data integration
Cohort-level research analytics
Treatment experience analysis

Potential value

More complete longitudinal research information, greater visibility into treatment experiences, and opportunities to improve study efficiency.

Explore pharma solutions Discuss your RWE study

Clarrio.ai / Research workspace

Illustrative research view

Cohort trajectory

Therapeutic area

Records

128

Sites

Window

12 months

Synthetic cohort: the two indices move together in some months and not others — an association, not a cause.

Illustrative · synthetic data

Expected reports received82%

82%

Your challenge

Patient information is distributed across EHRs, laboratories, connected devices, and other systems.

What Clarrio.ai delivers

FHIR-based interoperability
Longitudinal patient timelines
Multimodal data integration
Research cohort exploration
Data harmonization workflows

Potential value

Reduced data fragmentation, more efficient research workflows, and richer longitudinal patient information.

Explore healthcare integration Discuss interoperability

Clarrio.ai / Patient timeline

Illustrative interoperability view

One person, four sources

Clinical events: Structured record entries, each with a date and an authoring source.
Laboratory: Results arrive with their own source and collection date.
Activity: Daily wearable observations, kept at their own granularity.
Patient-reported: Structured check-ins from the person, separate from device data.

Illustrative · synthetic data

Your challenge

Biomarkers, wearables, lifestyle measurements, and clinical assessments often remain disconnected.

What Clarrio.ai delivers

Integrated biomarker dashboards
Longitudinal health profiles
Sleep and activity trends
Cardiometabolic domain analysis
Clinic population analytics
Program engagement measurement

Potential value

A more integrated patient experience, less manual data consolidation, and improved visibility into program outcomes.

Explore longevity solutions Discuss a clinic demo

Clarrio.ai / Program workspace

Illustrative longevity view

Health domains

Domain

Triglyceride index

Baseline62

Follow-up48

Direction shown is the one considered favorable for this measure.

Baseline and follow-up come from the same synthetic member record. The change is illustrative, not a result.

Illustrative · synthetic data

Your challenge

Research teams need reliable longitudinal information without increasing unnecessary participant and site burden.

What Clarrio.ai delivers

Remote research data capture
Patient-reported outcome collection
Connected-device integration
Follow-up dashboards
Study data exports
Research data harmonization

Potential value

Improved research data availability, better visibility into follow-up completion, and opportunities to reduce manual data handling.

Explore CRO workflows Discuss your study

Clarrio.ai / Study operations

Illustrative study view

Reporting funnel

Study model

Invited420
Consent given318
Device connected262
Reporting214

Synthetic study operations view: each stage is a different milestone, not one interchangeable adherence figure.

Illustrative · synthetic data

Every figure shown is synthetic demonstration data. Nothing here represents a customer result.

Start here

Which team are you on?

I run a longevity or preventive clinic

Understand how health status, interventions, and lifestyle behaviors evolve together over time.

Explore longevity intelligence Benefits for my team

I work in pharma, biotech or a CRO

Investigate disease trajectories, treatment exposure, and patient experiences in real-world settings.

Explore real-world evidence Benefits for my team

I'm with a hospital or health system

Connect clinical information with longitudinal treatment and patient-generated data for research and appropriate healthcare workflows.

Explore healthcare interoperability Benefits for my team

Or go straight to a therapeutic area

Already know you're the right fit?

Book a call

Partnerships and platform participation

Mayo Clinic Platform

Solutions Studio participant

Microsoft

Technology partner

Oracle

Technology partner

Solutions Studio participation is distinct from Mayo Clinic endorsement or clinical validation. Deployment scope is agreed for each program.

GDPR compliantHIPAA compliant

Disease / Treatment / Behavior

Three dimensions of the patient journey on one timeline

Disease tells us what is happening clinically. Treatment tells us what interventions patients receive. Behavior helps us understand the context in which those experiences unfold.

Health and disease trajectories

What is happening clinically: diagnoses, symptoms, disease activity, clinical events, laboratory results, biomarkers and functional status.

Treatments and interventions

What interventions the person receives: medication exposure, treatment changes, adherence, therapeutic interventions and, where relevant, preventive-health programs.

Behavior and daily life

The context between encounters: sleep, activity, mobility, recovery, daily functioning, lifestyle and environmental observations.

Clarrio.ai aligns these dimensions on a shared longitudinal timeline so researchers and healthcare organizations can explore temporal relationships, identify patterns and investigate questions fragmented datasets cannot easily address. Alignment preserves source meaning; it does not establish causality.

The data foundation

Connect clinical, treatment, and daily-life sources without losing context

Wearables contribute one category of observations. Clinical records, medication records, labs and the person's reported experience provide equally important context.

Electronic health records
Clinical diagnoses & events
Laboratory results & biomarkers
Symptoms & patient-reported outcomes

Health and disease trajectories

Medication records
Treatment start & changes
Reported adherence
Reported tolerability & experience

Treatments and interventions

Wearables & connected devices
Daily functioning & lifestyle
Patient-generated information
Behavioral & environmental information

Behavior and daily life

Longitudinal health intelligence

Dated, source-linked observations for research questions

Available sources, units, collection methods and access boundaries remain visible.

Scientific interpretation

Keep the observation separate from the conclusion

Observed data

What a source recorded, with its date, method and coverage.

Temporal associations

Changes occurring in the same interval; timing is not evidence of causality.

Research hypotheses

Questions generated for investigation using an appropriate study design.

Validated clinical findings

Require suitable scientific methods and validation; not established by this demonstration.

Product demo · one record

Explore patient, evidence, intelligence, and interoperability workflows

A member's everyday experience, a program team's review and a research question should not require three disconnected records. The products serve different people while preserving a shared longitudinal context.

Patient App

A record that starts with the person

The patient or member contributes symptoms, treatment experience and everyday context through structured check-ins. These observations join clinical and medication information on a shared longitudinal timeline.

Explore the record behind the screen

A Personal Response Graph puts sleep, activity, daily routines and reported experience in the same context. What Works for Me gives members a way to revisit patterns over time, while their clinical team remains responsible for interpreting the record. Missing observations are visible rather than silently filled in.

Member check-ins → personal context → longitudinal record

Explore Patient App

Platform

From one person to the whole program

Teams review disease observations, treatment exposure and behavior on one dated, source-linked timeline, then explore the cohort or person behind a pattern. Data coverage remains visible.

Explore the record behind the screen

The same underlying record supports member follow-up, program review and research preparation. Clinical events, biomarker results, wearable signals and structured reports keep their dates and source context. Exports should answer a defined evidence question, with agreed variables and consent boundaries—not simply collect everything because it is available.

Cohort review → source timeline → evidence preparation

Explore Platform

Intelligence

Investigate questions with source-linked summaries

Clarrio Intelligence presents trajectory summaries and cohort questions alongside the observations behind them. An answer is a starting point for review, not a substitute for the underlying data or professional judgment.

Explore the record behind the screen

A useful summary states the period, the signals compared and the limits of the observation. Sleep and training changes can occur together without establishing that one caused the other. Teams should be able to inspect the contributing records, check completeness and distinguish association from evidence of an effect before acting.

Question → contextual summary → source review

Explore Intelligence

Interoperability

Every source keeps its date and origin

Clinical and medication records, laboratory results and patient-generated observations support three synchronized dimensions: disease, treatment and behavior. Each source keeps its meaning and date.

Explore the record behind the screen

Clinical exchange follows standards such as HL7 FHIR where the customer's systems support them. Each observation keeps its source, unit and collection date; layers are aligned, not merged or interpolated. Integration scope is agreed with each organization's IT team.

EHR + labs + devices + reports → one governed timeline

Explore Interoperability

Your next research question

Find the capabilities that fit your research goals

Describe your research context for AI-powered workflow recommendations, sample questions, and evidence considerations.

Explore your research goals

Scope, not step counts

Comparing health data sources and longitudinal context

A single wearable can be useful. Remote-monitoring programs can be useful. The question is which sources, time horizons and outputs your program needs—not whether one category is universally better than another.

Data-source scope comparison, not an audited vendor comparison
What to ask	Wearable / health app	Monitoring program	Clarrio.ai
Multi-source record	Varies	Varies	Clinical, labs, devices, reports
Clinical context	Source dependent	Program dependent	Linked longitudinal context
Structured reports	App dependent	Workflow dependent	Configured check-ins
Across-time patterns	Device / app dependent	Vendor dependent	Person and cohort views
EHR integration	Connector dependent	Vendor dependent	Epic / Oracle Health pathways
Research output	Export dependent	Contract dependent	Scoped cohort datasets

Category-level questions, not claims about every vendor. Availability depends on the connected sources, deployment and agreed scope.

Evidence & responsible use

Evidence review and responsible longitudinal analysis

A useful record keeps the observations and their limitations close together. Teams need to see the collection period, missingness, source definitions and consent context before drawing conclusions from a trend.

Observation

Review

Judgment

Clarrio.ai is a data and insight platform, not a medical device. Clinical decisions remain with qualified professionals. The sample screens on this website use synthetic observations and demonstrate workflows, not measured customer outcomes.

Methodology and review

Mayo Clinic Platform review during Solutions Studio due diligence is part of the stated methodology context. Specific evidence, scope and claims should be reviewed for each program.

People behind the deployment

Clinical advisors, interoperability engineers and data-science expertise support the collection and review workflow. The right expertise depends on the therapeutic area and the evidence question.

Consent and source boundaries

Research use should remain within agreed consent and access boundaries. Hosting and ownership terms are confirmed for each deployment.

Illustrative program stories

Illustrative workflows across longevity, research, and healthcare

These examples explain how a program could use the record. They are not verified customer case studies; named clients, outcome figures and quotations are not presented without confirmation.

Longevity program

Assessment

Member timeline