Therapeutic area · Incl. long-term follow-up after gene therapy
Rare disease populations are small, dispersed and followed for years. Clarrio.ai collects longitudinal data remotely and across centers so no observation is wasted. Align clinical, treatment and behavioral information on one shared longitudinal timeline, where available and appropriate to the program.
What Clarrio.ai captures
The evidence gap
In rare disease, a study may include tens of patients across many countries. Each missed visit or incomplete questionnaire is a meaningful loss of statistical power, and traveling to a specialist center is a real burden for patients and caregivers.
Natural history data is often scarce, and one-time or gene therapies may require long-term follow-up over many years. Sustaining that follow-up through site visits alone is costly and difficult for families.
Natural history
Disease progression data is limited and scattered across centers.
Gene therapy follow-up
Multi-year follow-up obligations strain sites and families.
Pediatric conditions
Caregivers report on behalf of children; their burden matters.
Functional decline
Mobility and function change slowly and need dense measurement.
The challenge
Rare disease patients are few and geographically dispersed, so every data point matters and site visits are costly for families. Natural-history data is often thin, and one-time and gene therapies require years of long-term follow-up. Relying on periodic site visits produces sparse records and high participant burden, with function and caregiver experience between visits largely unrecorded.
Toggle the chart to compare what a visit-based record shows with what the continuous record shows for the same synthetic patient.
Functional score (0–100) over 120 days
Observations2
Events in periodNot visible
Events with no nearby visit3 of 3
Illustrative · synthetic data
Dispersed
Travel burden
Families may travel long distances for each assessment, limiting how often data is collected.
Natural history
Thin baselines
Comparators depend on sparse, inconsistent historical data.
Follow-up
Years, not months
Long-term follow-up after gene therapy is hard to sustain through site visits alone.
How Clarrio.ai addresses the gap
Remote-first collection
PROs, caregiver reports and function measures completed at home.
Multicenter record
One longitudinal record across referral centers and countries.
Flexible schedules
Protocols that adapt to disease course and family needs.
Governed sharing
Consent-driven access for sponsors, registries and investigators.
Data captured
Select a layer. Exact sources depend on deployment configuration and data permissions.
Captured from patients or caregivers in the Patient App.
Product workflow
Select a step to see what happens. Sources are captured through the Patient App and integrations, harmonized on the Platform and summarized by Intelligence.
Sources
Clarrio.ai workflow
Outputs
Stakeholder outputs
Each role receives a different view of the same longitudinal record. Values are synthetic.
Clinical development
Status of every participant in long-term follow-up, with completeness by measure.
Caregiver
Caregivers report function and daily life in a few minutes, from home.
Medical affairs
Aggregated trajectories by genotype or age band, from consented participants.
Worked examples
Pediatric participant and caregiver
Year 0
Treatment; enrolled in remote follow-up.
Monthly
Caregiver check-ins and home functional tests.
Year 1
Site visit uses the year of remote data as context.
Years 2–5
Follow-up continues with fewer trips.
Illustrative · synthetic data
Patient organization and sponsor
Month 0
Families enroll across countries.
Months 1–12
Consistent PROs and functional measures.
Month 12
Harmonized dataset available for analysis.
Illustrative · synthetic data
FAQ
Research & operational outputs
Patient journey map
Diagnosis, treatments and outcomes across centers on one line.
Longitudinal evidence map
Which variables exist for which patients, and when.
Natural history cohort view
Progression trends across a dispersed cohort.
Follow-up compliance report
Long-term follow-up status for every participant.
Pharmaceutical & biotech
Natural history and long-term follow-up data with less site burden.
Specialist centers
Continuity across referral pathways.
CROs & registries
Remote collection across countries with completeness tracking.
Patients & caregivers
Fewer trips and a supportive app that respects their time.
Program evaluation
These are the KPIs we agree with customers before deployment, measured against their own baseline. Data coverage, patient experience and clinical outcomes answer different questions. Higher engagement alone does not demonstrate better health or lower utilization.
Explore benefits and evidence| Lever | How it is measured |
|---|---|
| Participant retention | Participants remaining in follow-up relative to enrolled |
| Data completeness | Expected timepoints with data |
| Visit burden | In-person visits per participant per year |
| Cost per complete record | Collection cost relative to complete longitudinal records |
Illustrative use case
Clinical development, illustrative
Year 0
Participants consent to remote follow-up at treatment.
Years 1–2
Quarterly PROs and function tasks at home; annual site visit.
Ongoing
Compliance report shows each participant's status.
Year 5
Longitudinal dataset supports regulatory reporting.
Illustrative · synthetic data
Clarrio.ai has joined Mayo Clinic Platform's Solutions Studio, supporting its mission to advance interoperable, data-driven healthcare innovation. This does not imply endorsement or clinical validation.
Design Your Rare Disease Data Strategy
Design Your Rare Disease Data StrategyEvidence interpretation
Align records to meaningful study milestones and distinguish patient reports from caregiver assessments. Small, dispersed cohorts need explicit eligibility, instrument suitability and gap documentation. Natural-history observations are not automatically a valid comparison group.
Benefits in practice
Pharma & biotech
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
Define the population, observation window, outcome concept and intended decision before selecting sources.
02
Combine appropriately scoped Patient App reports with available clinical and device data in the Platform; keep reported experience separate from recorded events.
03
Use source-aware review to inspect context and quality. Agree variable definitions, analysis methods and limitations with the study 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.
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.
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.
Workflow illustration · not a customer result or live record
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 evaluationPayers & health economics
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
Specify the relevant covered population, decision, comparator where needed and evaluation horizon.
02
Review provenance, participation, measurement windows and missingness before examining results.
03
Combine appropriately governed patient experience with relevant clinical or utilization evidence; document assumptions and limits for expert appraisal.
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.
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.
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.
Workflow illustration · not a customer result or live record
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 evaluationPatients, members & caregivers
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.
01
Agree what the care or research program needs to learn and what participation involves.
02
Use appropriate reports alongside available observations; distinguish the patient's own answers from caregiver observations.
03
Discuss a dated interval with the responsible professional, including what is missing and what matters to the person.
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.
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.
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.
Workflow illustration · not a customer result or live record
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 evaluationRelated solutions
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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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