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Clarrio.ai

Customers · Pharma & biotech

Real-world data for pharma and biotech research teams

Continuous real-world evidence from Phase 2/3 through post-launch, deployed worldwide and alongside your CRO.

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Sound familiar?

  • Site visits miss most of the patient experience
  • Long-term follow-up is costly
  • Rare populations are dispersed
  • Payers want real-world outcomes

How Clarrio.ai helps

Longitudinal data workflows for pharma & biotech

Center-agnostic deployment

US, Middle East, Europe

Immediate collection

Partner sites start right away

No protocol change

Works alongside CROs

Harmonized exports

For data science and HEOR

Benefits for you

Pick your role and see what changes

The same record is shown differently to each person in your team.

Today

  • – Learns about missed diaries and site problems at monitoring visits
  • – Reconciles patient-reported data from several vendors
  • – Has limited sight of participant burden between visits

With Clarrio.ai

  • + Sees protocol completion by site and week
  • + Receives one harmonized record across app, devices and sites
  • + Spots rising burden before it becomes dropout

What you receive

  • Completion dashboard by site
  • Participant engagement list
  • Data completeness report

Clarrio.ai / Program workspace

Clarrio Platform

Member cohort

Records

4

In review

2

Window

12 mo

Illustrative member cohort
RecordCompleteReview context
94%Sleep context
87%New lab result
91%Training change
82%Check-in due
M-104 · longitudinal recordJan—Dec
M-104: dated observations retain their source context. Completeness varies by source.

Illustrative · synthetic data

Scenarios

How it plays out, step by step

Illustrative scenarios built from typical program workflows. They are not client case studies.

Illustrative scenario 1

Long-term follow-up after a gene therapy trial

A sponsor must follow participants for years after dosing, mostly between infrequent visits.

  1. 1

    Enroll

    Participants join a branded app with consent for follow-up.

  2. 2

    Collect

    Quarterly PROs plus passive activity data.

  3. 3

    Monitor

    Completion and engagement tracked by site and country.

  4. 4

    Flag

    Gaps in data surface for site follow-up.

  5. 5

    Report

    Documented exports for safety and evidence teams.

Outputs: Follow-up completeness report · Participant engagement view · Analysis-ready export

Illustrative scenario 2

Building a natural history baseline in a rare disease

Little is known about day-to-day progression, and patients are spread across regions.

  1. 1

    Recruit

    Patients join remotely through advocacy and clinic partners.

  2. 2

    Capture

    Symptom diaries, function measures and device data.

  3. 3

    Harmonize

    One coded record per consented participant.

  4. 4

    Describe

    Cohort-level trajectories by subtype and age.

  5. 5

    Share

    Aggregated outputs for protocol and endpoint design.

Outputs: Descriptive trajectory report · Cohort definitions · Data dictionary

Illustrative scenario 3

A patient support program after launch

A brand team wants to understand how patients manage between appointments once on therapy.

  1. 1

    Launch

    A white-label app in the program's name.

  2. 2

    Check in

    Short weekly questions on symptoms and routines.

  3. 3

    Support

    Program staff see who has gone quiet.

  4. 4

    Learn

    Aggregated patterns across consented members.

  5. 5

    Improve

    Program content adjusted from what is used.

Outputs: Program engagement dashboard · Aggregated burden summary · Outreach list

Benefits in practice

Benefits and evidence needs across stakeholder roles

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.

  1. 01

    Specify the evidence objective

    Define the population, observation window, outcome concept and intended decision before selecting sources.

  2. 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.

  3. 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.

Illustrative review brief
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 evaluation

Payers & 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.

  1. 01

    Define the appraisal question

    Specify the relevant covered population, decision, comparator where needed and evaluation horizon.

  2. 02

    Assess data suitability

    Review provenance, participation, measurement windows and missingness before examining results.

  3. 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.

Illustrative review brief
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 evaluation

Patients, members & caregivers

Give the person contributing data a meaningful place in the workflow

A person can complete many questions without understanding why they are being asked or who will read the answers. Device observations may look precise while missing personal context. A useful longitudinal journey starts with the person's priorities and makes the reporting purpose, burden and review arrangements understandable.

  1. 01

    Explain the purpose

    Agree what the care or research program needs to learn and what participation involves.

  2. 02

    Capture lived experience

    Use appropriate reports alongside available observations; distinguish the patient's own answers from caregiver observations.

  3. 03

    Bring context to review

    Discuss a dated interval with the responsible professional, including what is missing and what matters to the person.

For the professional team

Teams gain context for a more specific conversation, but should not assume that a missed report means a person disengaged. Accessibility, fatigue, language and connectivity may affect reporting. Define support and review arrangements before introducing new questions.

For patients, members and caregivers

Patients and members can keep a dated account of experiences they might otherwise forget. Caregivers can contribute observations when appropriately scoped, without replacing the person's own perspective. Personal patterns invite discussion; they are not instructions or proof that a particular routine produced an outcome.

For payers and program funders

A program's apparent reach can hide who could not participate. Payers and commissioners should examine access, reporting burden and representativeness alongside outcome evidence. Patient experience is an important perspective in appraisal, not a shortcut to an economic conclusion.

Illustrative review brief
Personal context
The person's own priorities and dated reports
Reporting perspective
Patient answer or appropriately identified caregiver observation
Review arrangement
Who reviews reports, when and through which contact pathway

Workflow illustration · not a customer result or live record

What to evaluate before making a claim

Ask whether reporting is understandable, manageable and meaningful. Examine completion alongside accessibility and support needs. A high completion rate alone does not show that people feel heard or that a program has improved their health.

Explore evidence and evaluation

What you receive

Data outputs for pharma & biotech teams

Clinical development

Between-visit response data

Medical affairs & HEOR

Real-world outcomes

Patients

A supportive app

The Pharma & biotech solution

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 the full solution

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Other customers

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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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