Therapeutic area · RA, IBD, psoriasis, lupus
Flares, fatigue and pain come and go between scheduled visits Clarrio.ai records them as they happen and aligns them with treatment and biomarkers. 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
Rheumatoid arthritis, inflammatory bowel disease, psoriasis and lupus follow relapsing-remitting patterns. Disease activity scores captured at quarterly visits describe a moment, not the weeks of fluctuation around it.
Biologic and advanced therapies are evaluated on long-term control, persistence and quality of life. Without between-visit data, short flares, switching reasons and the impact of fatigue go largely unmeasured.
Rheumatoid arthritis
Morning stiffness and joint pain vary daily; visit scores show one point.
IBD
Symptom flares and their triggers occur between endoscopies and labs.
Psoriasis & atopic dermatitis
Itch, sleep loss and skin changes are best captured at home, with photos.
Lupus
Fatigue and multi-system symptoms are hard to recall accurately.
Symptom score (0–10) over 120 days
Observations2
Events in periodNot visible
Events with no nearby visit3 of 3
Illustrative · synthetic data
How Clarrio.ai addresses the gap
Flare logging
Patients record flares and symptoms in the app as they occur.
Scheduled PROs
Disease activity and fatigue instruments at protocol intervals.
Biomarker linkage
CRP, calprotectin and other labs integrated from records.
Therapy context
Start, switch and dose history aligned with symptoms.
Data captured
Select a layer. Exact sources depend on deployment configuration and data permissions.
Captured from patients or caregivers in the Patient App.
Research & operational outputs
Symptom and biomarker timeline
Flares overlaid with lab values and therapy changes.
Persistence view
Therapy continuation and switching across a cohort.
Flare pattern summary
Frequency and context, framed as associations.
PRO completion report
Coverage by site and timepoint.
Pharmaceutical & biotech
Real-world control, persistence and QoL evidence for advanced therapies.
Hospitals & specialists
Flares and symptoms since the last visit, in one view.
CROs
Remote PROs reducing visit-only data collection.
Patients
A record that reflects how they actually feel between appointments.
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 |
|---|---|
| Data completeness | Weeks with symptom data relative to expected |
| Retention | Participants active at study end |
| Manual follow-up time | Coordinator calls to chase questionnaires |
| Operational efficiency | Site visits replaced by remote assessments |
Illustrative use case
Medical affairs, illustrative
Month 0
Participants enroll at therapy start; baseline PROs.
Months 1–6
Flares logged in-app; monthly fatigue and activity scores.
Month 3
Timeline shows flare clusters aligned with dose intervals for review.
Month 9
Cohort persistence and QoL dataset exported.
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.
Explore Immunology Research Solutions
Explore Immunology Research SolutionsEvidence interpretation
Track how flare reports, fatigue, function and assessments were collected rather than treating them as one measure. Reporting burden may vary across difficult periods. For evidence appraisal, distinguish symptom experience from separately verified outcomes.
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
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 solutionConnect 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 solutionRequest a demo
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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