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Clarrio.ai

Clarrio Intelligence

Explore longitudinal health data through natural-language analytics

Clarrio Intelligence brings clinical, treatment, biomarker, and patient-generated data into an integrated research analytics experience. Explore cohorts, examine longitudinal trends, compare measurements, and investigate relationships across health and disease trajectories using natural-language questions and interactive visualizations.

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How did symptom burden change after treatment initiation?

Mean symptom score

Synthetic data

Dashed line: treatment initiation (week 0)

Cohort summary

Participants
212
Observation
16 weeks
PRO completeness
82%
Sites
4
Health & disease
Treatment
Behavior
EHRPRO diaryMedication recordsConnected device

Descriptive view with no comparison group; changes cannot be attributed to treatment.

What it is

A research analytics layer for connected longitudinal health data

Health information is often distributed across electronic health records, laboratory systems, connected devices, patient questionnaires, and research datasets. Even when these sources are available, examining them together can require significant preparation and technical expertise.

Clarrio Intelligence provides a research-oriented analytics experience built around harmonized longitudinal information. It helps authorized users investigate how clinical measurements, treatment histories, biomarkers, and patient-generated observations evolve over time.

Instead of examining isolated measurements, research teams can explore broader patterns within individuals and populations, generate visual summaries, and investigate questions across multiple data domains.

  1. 1 · Data collection

    Connected data

    Clinical records, biomarkers, medication information, symptoms, questionnaires, connected devices, and environmental observations.

    Collection brings authorized sources together; on its own it does not make them comparable.

  2. 2 · Harmonization

    Harmonized longitudinal data

    Aligned timestamps, normalized measurements, structured data categories, cohort definitions, and documented data provenance.

    Harmonization makes measurements comparable over time and keeps a record of where each value came from.

  3. 3 · Analytics

    Intelligence & analytics

    Natural-language exploration, interactive visualizations, cohort summaries, longitudinal comparisons, and research hypothesis exploration.

    Analytics helps people ask questions of the harmonized record; findings still require human and statistical review.

Core capabilities

From complex datasets to meaningful research questions

Six capabilities, each shown with an example question and the type of output a research team would review.

Capability 1

Explore research questions using natural language

Clarrio Intelligence provides a conversational interface for exploring connected longitudinal datasets. Authorized users can formulate research questions in familiar language and examine available information through structured summaries and visualizations.

  • Time-series charts
  • Cohort summaries
  • Descriptive statistics
  • Available-data summaries
  • Research-oriented visual explanations

Generated answers are exploratory summaries, not automatically validated scientific conclusions.

Symptoms over the observation period

n = 212 · 9 weekly reports · 14% missing

Example question

“How have patient-reported symptoms changed over the observation period?”

Example output

A weekly symptom chart for the selected cohort, with the number of participants reporting each week and a note on missing reports.

Capability 2

Examine how health measurements change over time

Clarrio Intelligence helps research teams examine longitudinal information rather than relying exclusively on isolated clinical encounters. Users can review changes in clinical measurements, symptoms, biomarkers, treatment history, and patient-generated information across a shared timeline.

  • Synchronized clinical, treatment and behavior tracks
  • Event markers for treatment starts and changes
  • Baseline and follow-up windows
  • Source labels on each observation
Symptoms
Exposure
Activity

Example question

“How did symptom burden and activity levels change around the treatment initiation date?”

Example output

Three aligned tracks centered on treatment initiation, showing symptom burden, exposure and daily activity before and after.

Capability 3

Explore patient cohorts across multiple data dimensions

Researchers can examine defined populations using available clinical, demographic, biomarker, treatment, and behavioral attributes. Cohort-level exploration helps teams understand the characteristics, data availability, and longitudinal patterns of selected populations.

  • Predefined cohort selection
  • Participant characteristics summary
  • Side-by-side cohort comparison
  • Completeness indicators per domain

Cohort exploration describes defined populations; it does not perform automated recruitment or eligibility assessment.

Cohort A · n=104Cohort B · n=97
Sleep data88%
Activity data79%
Labs94%

Example question

“How do longitudinal sleep and activity patterns differ between the two predefined research cohorts?”

Example output

Paired trend lines for both cohorts with participant counts and data completeness for each measurement.

Capability 4

Investigate relationships between clinical, treatment, and behavioral data

Health trajectories are shaped by multiple factors that may change simultaneously. Clarrio Intelligence enables exploratory examination of temporal relationships between clinical measurements, treatment exposure, patient-reported experiences, and everyday behaviors.

  • Exploratory scatterplots
  • Longitudinal overlays
  • Contextual event timelines
  • Interpretation notes on confounding and missing data

Observed associations may reflect confounding, measurement differences or missing data and do not establish causality.

Exploratory association · r = 0.41 (synthetic) · not causal

Example question

“Are changes in patient-reported fatigue associated with changes in sleep duration within the selected cohort?”

Example output

A scatterplot of week-over-week changes with a descriptive association measure and explicit interpretation notes.

Capability 5

Connect biomarker trends with broader health measurements

Clarrio Intelligence supports the exploration of longitudinal biomarkers alongside available physiological, behavioral, and clinical information. This is relevant to research into cardiometabolic health, preventive-health programs, chronic conditions, and healthy aging.

  • Multi-domain biomarker views
  • Individual and cohort-level trends
  • Baseline versus follow-up comparisons
  • Reference to measurement source and date

Clarrio Intelligence does not calculate biological age or clinically validated longevity scores.

ApoB

112 → 95 mg/dL

Baseline → month 12

HbA1c

5.8 → 5.5 %

Baseline → month 12

hs-CRP

2.1 → 1.4 mg/L

Baseline → month 12

Steps

6.1k → 8.0k /day

Baseline → month 12

Example question

“How have selected cardiometabolic biomarkers and activity measurements changed between baseline and follow-up?”

Example output

A biomarker panel showing baseline and follow-up values next to activity trends for the same period.

Capability 6

Turn exploratory analysis into interpretable research outputs

Clarrio Intelligence helps users organize analytical findings into visual summaries that can support research discussions, study planning, and further statistical investigation. Outputs should retain relevant context about the selected population, observation period, data sources, and analytical limitations.

  • Descriptive cohort reports
  • Longitudinal visualizations
  • Comparative summaries
  • Research question summaries
  • Data completeness assessments

Downloadable reports or exports depend on the configured deployment.

Research summary · draft
  • Population: synthetic cohort A, n = 212
  • Period: weeks −4 to +12
  • Sources: EHR, PRO diary, device
  • Limitations: no comparison group; 18% missing diary weeks

Requires human review before use

Example question

“Summarize what we know about follow-up completeness in this cohort.”

Example output

A summary that states the population, observation period, sources used and known limitations alongside the charts.

Explore the product

Illustrations on this page use synthetic data and preconfigured demonstrations. They show intended workflows and are not live analyses.

By industry

How Clarrio Intelligence supports different healthcare organizations

Each organization asks different questions of the same longitudinal record. Select a tab to see a dedicated example.

Investigate treatment experiences and disease trajectories in real-world settings

Pharmaceutical and biotechnology research increasingly requires a more comprehensive understanding of how patients experience disease and treatment outside scheduled study visits.

Clarrio Intelligence helps research teams explore longitudinal clinical information alongside medication exposure, symptoms, patient-reported outcomes, physiological measurements, and everyday behaviors.

By examining these dimensions together, teams can investigate patterns relevant to observational studies, natural-history research, treatment experience, and evidence-generation planning.

Key use cases

  • Longitudinal disease characterization
  • Treatment exposure and adherence exploration
  • Patient-reported outcome analysis
  • Natural-history research
  • Exploratory subgroup comparisons
  • Symptom and functional status trends
  • Real-world study planning

Treatment-aligned symptom trajectory

Synthetic data
Cohort A · drug XCohort B · standard care

Shaded: baseline observation window · dashed line: treatment start

82%

PRO weekly

86%

Adherence

21%

Missing device

Example questions

  • “How do patient-reported symptoms evolve before and after treatment initiation?”
  • “What longitudinal differences are visible between predefined treatment cohorts?”
  • “How does reported adherence vary across the observation period?”
  • “Which research variables have the highest missing-data rates?”

Example outputs

  • Treatment-aligned symptom timelines
  • Cohort-level descriptive comparisons
  • Adherence and observation summaries
  • Longitudinal research charts
  • Data quality and completeness views

Value by stakeholder

RWE teams
Explore richer longitudinal datasets.
Medical affairs
Investigate patient experience questions.
Clinical development
Examine relevant longitudinal research variables.
Biostatistics teams
Identify exploratory patterns and data quality considerations.
Explore pharma intelligence Discuss a research study

Connect clinical records with longitudinal information beyond the hospital

Hospitals and healthcare systems generate valuable information through clinical encounters, laboratory testing, and electronic health records. Yet many aspects of the patient journey take place between visits and are captured in separate systems.

Clarrio Intelligence helps authorized research and operational teams explore integrated longitudinal datasets combining available clinical information with patient-reported measurements and other relevant observations.

This supports a more comprehensive understanding of research populations, data continuity, and longitudinal patterns across care settings.

Clarrio Intelligence supports research and operational review; it does not make clinical care decisions.

Key use cases

  • Clinical research cohort exploration
  • Longitudinal patient research profiles
  • EHR and patient-generated data analysis
  • Research data harmonization
  • Follow-up data completeness
  • Population-level descriptive analytics
  • Research program evaluation

Research registry · 3 sites

Synthetic data
EHR events
Lab results
Patient reports

Patient-reported data fills the gaps between clinical encounters.

SiteParticipantsFollow-up complete
Site A20481%
Site B15866%
Site C12472%

Example questions

  • “Which participants have missing follow-up measurements?”
  • “How have selected laboratory measurements changed over the observation period?”
  • “What patient-reported information is available between clinical visits?”
  • “How does data completeness differ between participating sites?”

Example outputs

  • Longitudinal research timelines
  • Population summaries
  • Clinical and patient-generated data overlays
  • Research follow-up dashboards
  • Data completeness visualizations

Value by stakeholder

Hospital research leaders
Access a more integrated view of research information.
CIOs and interoperability teams
Understand how connected data supports research workflows.
Research coordinators
Examine follow-up data availability.
Population health researchers
Explore longitudinal cohort patterns.
Explore healthcare intelligence Discuss your integration needs

Understand health trajectories across biomarkers, interventions, and lifestyle

Longevity and preventive-health programs combine laboratory testing, clinical assessments, lifestyle information, and physiological measurements to understand how health changes over time.

However, these measurements are often distributed across disconnected tools and assessed at different intervals.

Clarrio Intelligence provides a longitudinal analytics experience for exploring biomarker, intervention, and lifestyle information together, so clinic teams can review how each member's program and the wider clinic population evolve between assessments.

Key use cases

  • Biomarker trend exploration
  • Sleep and activity trends
  • Preventive-health program evaluation
  • Lifestyle and intervention timelines
  • Clinic population analytics
  • Patient engagement and data completeness

Member health domains

Synthetic data
CardiometabolicSleep & recoveryActivityInflammation

ApoB

112 → 95 mg/dL

Lp(a)

38 → 37 mg/dL

Fasting glucose

99 → 92 mg/dL

Solid: sleep (h) · dashed: steps (k/day) · baseline → month 12

Nutrition planTraining programSleep coaching

Intervention timeline: nutrition, training, sleep coaching

Example questions

  • “How have selected biomarkers changed since baseline?”
  • “What differences are visible in sleep and activity measurements after program enrollment?”
  • “Which participants have completed their scheduled follow-up assessments?”
  • “How do biomarker trajectories vary across predefined clinic cohorts?”

Example outputs

  • Individual longitudinal health profiles
  • Biomarker trend charts
  • Health-domain dashboards
  • Baseline-versus-follow-up comparisons
  • Clinic-level cohort summaries
  • Program engagement dashboards

Value by stakeholder

Clinic directors
Explore longitudinal program information.
Physicians and scientific teams
Review integrated measurements for research and appropriately governed workflows.
Program managers
Monitor engagement and data completeness.
Patients
Support clearer communication of longitudinal measurements where patient-facing views are implemented and approved.
Explore longevity intelligence Request a longevity demo
Request an industry-specific demo

Interactive demonstration

See how Clarrio Intelligence explores connected health data

Choose an industry and a sample research question, then turn data layers on and off. These are preconfigured examples with synthetic data, not a live analysis.

Industry
Research question
Data layers
Cohort
Synthetic observational cohort A
Observation period
Weeks −4 to +12

Mean symptom score (0–10)

Synthetic data

Preconfigured sample output for: “How do patient-reported symptoms evolve before and after treatment initiation?”

Intervention start
ClinicalTreatmentBehavior

Each layer is scaled to its own range so trends can be compared on one timeline.

212

Participants

16

Time points

3

Sources

Key observations

  • Mean reported symptom score is lower in weeks +4 to +12 than in the four baseline weeks.
  • Reported adherence declines gradually after week +6.
  • Daily activity increases over the same window.

Data sources

  • Electronic PRO diary
  • Medication records
  • Connected activity device

Limitations

  • No comparison group in this view; changes cannot be attributed to treatment.
  • 18% of weekly diary entries are missing after week +8.
  • Self-reported adherence may overstate actual exposure.

Suggested follow-up questions

  • → How does symptom change differ by baseline severity?
  • → Which participants stopped reporting after week +8?
Pharma & biotech solutionRequest a demo
Discuss your research questions

How it works

From fragmented health information to longitudinal research intelligence

Stage 1: Connect

Integrate authorized information from clinical systems, laboratory sources, patient questionnaires, connected devices, and other supported data environments.

Trust, science and responsible AI

Research intelligence requires scientific context

Clarrio Intelligence is designed to support research-oriented data exploration. It is not an autonomous diagnostic system or a treatment recommendation engine, and a language model does not independently establish clinical validity.

Data provenance and traceability
Where supported, summaries link back to the source, date and type of each contributing observation so reviewers can check them.
Appropriate access controls
Users see only the data their role and the governing agreements allow.
Transparent research assumptions
Cohort definitions, observation windows and measurement choices are stated alongside each output.
Human review of analytical findings
Researchers and clinicians review every finding; the language model does not independently establish clinical validity.
Awareness of missing data and confounding
Outputs report data completeness and flag factors that could explain an observed pattern.
Association is not causation
Observed relationships are described as associations; causal questions need appropriate study designs.
Privacy and research governance
Use follows consent, ethics approval and data protection requirements for each program.

FAQ

Questions about Clarrio Intelligence

Learn more about the Clarrio Platform that harmonizes the data, and the Patient App that collects patient-reported information.

Discover what connected longitudinal health data can reveal

Whether your organization is investigating treatment experiences, connecting healthcare research data, or evaluating longevity programs, Clarrio can help you explore the information that matters.

Request a demo Contact our team

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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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