Patient, rare tumor
Escalating fatigue across cycles
Cycle 1
Fatigue peaks Day 3, resolves by Day 8.
Cycle 3
Peak higher and longer.
Cycle 4 visit
Nurse reviews the cycle-by-cycle view with the patient.
Illustrative · synthetic data
Therapeutic area · Between-cycle experience and outcomes
Oncology patient experience across treatment cycles
Fatigue, nausea, pain, sleep and function change between cycles, at home. Clarrio.ai captures that experience in a structured, longitudinal way and links it to treatment history. Align clinical, treatment and behavioral information on one shared longitudinal timeline, where available and appropriate to the program.
What Clarrio.ai captures
- Symptom and side-effect PROs (e.g. PRO-CTCAE-based items)
- Quality-of-life instruments
- Free-text experience notes
- Return-to-activity check-ins
- Regimen and cycle dates
- Labs (blood counts, chemistry)
Longitudinal health intelligence
Disease, treatment and behavior on one timeline
Align clinical observations, medication or intervention history, and daily-life context without losing source meaning. Available data and review boundaries depend on the program.
One person / one shared timeline
Illustrative research data · Synthetic demonstration
Health and disease trajectories
Reported fatigue burden
Treatments and interventions
Exposure · dose · reported experience
Behavior and daily life
Average daily activity
Shared time axis / weeks
Week 4 / Disease4 /10 · Structured experience report
Week 4 / TreatmentInterval · No administration recorded this week
Week 4 / Behavior25 min · Short walks resumed
Separate measures and independently scaled channels; values appear in their own units. Temporal alignment does not establish causality.
The evidence gap
The weeks between cycles are where much of the patient experience lives
Oncology visits are dense with clinical activity but short on time to explore how the last few weeks went. Symptom and toxicity information is often captured as a summary at the next infusion, not as it unfolded.
For evidence teams, quality-of-life and functional outcomes are increasingly important alongside survival and response, yet they are hard to collect consistently once patients leave trial settings or move between centers. Rare tumors compound the problem: few patients, many sites, little longitudinal data.
Symptom burden
Peak fatigue and nausea often occur days after a cycle, long before the next review.
Function
Daily activity can decline gradually; a single performance-status rating may not reflect it.
Quality of life
QoL instruments collected at visits only produce a few points per patient per year.
Rare tumors
Patients are distributed across centers, making consistent follow-up difficult.
The challenge
Most of the treatment experience happens between cycles
Patients typically see their oncology team at the start of each cycle. Side effects, fatigue and changes in function often peak in the days after treatment, at home, and are later recalled imperfectly. For rare tumors, real-world data on quality of life and tolerability is especially limited, making both care planning and evidence generation harder.
Toggle the chart to compare what a visit-based record shows with what the continuous record shows for the same synthetic patient.
Fatigue score (0–10) over 120 days
Observations5
Events in periodNot visible
Events with no nearby visit4 of 4
Illustrative · synthetic data
Timing
Peaks are missed
Assessments at cycle start miss symptoms that peak days later.
Recall
Severity drifts
Patients summarize weeks of symptoms in a short consultation.
Rare tumors
Little RWD
Small populations mean limited real-world quality-of-life evidence.
How Clarrio.ai addresses the gap
Collection, integration and analytics, designed for this area
- 01
Cycle-aware PRO schedules
Questionnaires timed relative to each treatment cycle, not the calendar.
- 02
Passive function
Step count and activity trends as context for functional change.
- 03
Treatment linkage
Regimen, cycle dates and labs integrated from oncology records.
- 04
Cross-site collection
One patient record regardless of which center the patient attends.
Data captured
Three layers of data, kept distinct
Select a layer. Exact sources depend on deployment configuration and data permissions.
Captured from patients or caregivers in the Patient App.
- Symptom and side-effect PROs (e.g. PRO-CTCAE-based items)
- Quality-of-life instruments
- Free-text experience notes
- Return-to-activity check-ins
Product workflow
How the record is built, step by step
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
- Symptom & side-effect PROs
- Quality-of-life measures
- Activity & function
- Treatment schedule
- Labs & records
Clarrio.ai workflow
Outputs
- Between-cycle summary
- Symptom-by-cycle view
- Quality-of-life evidence
Stakeholder outputs
Role-specific views of the longitudinal record
Each role receives a different view of the same longitudinal record. Values are synthetic.
Oncology nurse
Between-cycle summary
A short summary of symptoms and function since the last cycle, ready before the visit.
Clarrio.ai / Between-cycle summarySynthetic
- Fatigue peakDay 3–5Higher than previous cycle
- NauseaDays 1–3Resolved by Day 6
- Daily steps↓ 40% at peakReturned by Day 10
Patient
Daily check-in
Patients report how they feel in under a minute, with reminders around each cycle.
Clarrio.ai / Daily check-inSynthetic
- How tired are you today?0–10Single tap
- Any new symptoms?OptionalPick from list
- My cyclesViewSymptoms by cycle
Pharma / HEOR
Cohort QoL view
Quality-of-life trajectories by cycle across a consented cohort.
Clarrio.ai / Cohort QoL viewSynthetic
- Cohort148Rare tumor type
- QoL completion77%Per cycle
- ExportReadyCycle-aligned dataset
Worked examples
Two illustrative scenarios
Medical affairs
Quality-of-life evidence
Month 0
Cohort recruited across centers.
Months 1–6
Cycle-aligned PROs collected.
Month 6
Dataset supports a QoL analysis.
Illustrative · synthetic data
FAQ
Oncology questions
Research & operational outputs
Research and operational deliverables
Treatment timeline
Cycles overlaid with symptoms, activity and QoL.
Cycle comparison
How the experience of cycle 1 compares with cycle 4 across a cohort.
PRO completion dashboard
Who is overdue, by site and cycle.
Cohort QoL trends
Aggregated quality-of-life trajectories by regimen, for research use.
Value for each stakeholder
Pharmaceutical & biotech
Longitudinal PRO and QoL evidence to complement efficacy data in medical affairs and HEOR.
Oncology centers
A structured view of the inter-cycle period ahead of each review.
CROs
Remote, cycle-aware PRO collection with completion tracking across sites.
Patients
A simple way to report how they are doing, and to feel heard between scheduled visits
Program evaluation
Where Clarrio.ai can create measurable value
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 |
|---|---|
| PRO completion | Completed PRO timepoints relative to scheduled |
| Longitudinal follow-up | Patients with data beyond first treatment line |
| Manual data handling | Hours spent entering paper questionnaires |
| Usable data coverage | Share of cycles with complete symptom data |
Illustrative use case
QoL across a treatment line
HEOR team, illustrative
QoL across a treatment line
Cycle 1
Baseline QoL and symptom items; wearable connected.
Cycles 1–6
Short PROs at days 3, 7 and 14 of each cycle.
Cycle 4
Dashboard shows rising fatigue in a subgroup; coordinators check completion.
End of line
Cohort QoL trajectories exported for an observational report.
Illustrative · synthetic data
Solid tumorsHematologic cancersRare tumorsSurvivorship
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.
- Consent and data governance
- Privacy-by-design principles
- Role-based access
- Flexible deployment
- Data residency options
- FHIR-based interoperability
- Research-use positioning, not diagnosis
Build Richer Oncology Evidence
Build Richer Oncology EvidenceEvidence interpretation
Between-cycle experience must stay anchored to the collection window
Interpret reports alongside cycle timing and the question the instrument was designed to answer. Missing questionnaires may reflect burden as well as technical issues. Evidence of reported experience does not, by itself, establish comparative outcomes or payer value.
Benefits in practice
From oncology observations to a useful review
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.
01
Specify the evidence objective
Define the population, observation window, outcome concept and intended decision before selecting sources.
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.
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.
- 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 evaluationPayers & 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.
01
Define the appraisal question
Specify the relevant covered population, decision, comparator where needed and evaluation horizon.
02
Assess data suitability
Review provenance, participation, measurement windows and missingness before examining results.
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.
- 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 evaluationPatients, 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.
01
Explain the purpose
Agree what the care or research program needs to learn and what participation involves.
02
Capture lived experience
Use appropriate reports alongside available observations; distinguish the patient's own answers from caregiver observations.
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.
- 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 evaluationRelated solutions
Related deployment workflows
Pharma & biotech
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 solutionHealth systems & hospitals
Connect 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 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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