Therapeutic area · Cycles, fertility, pregnancy, menopause
Cycles, fertility treatment, pregnancy and menopause unfold over months and years. Clarrio.ai captures them in context, with strict consent and privacy controls. 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
Many aspects of women's health are cyclical or change across life stages. Symptoms, sleep and physiology vary across the menstrual cycle, yet research and care often rely on isolated measurements that ignore where a person is in that cycle.
Women's health has historically been under-researched, and the data that exists is fragmented between fertility clinics, apps and hospital records. Privacy expectations are, rightly, very high.
Menstrual health & endometriosis
Pain and symptoms follow cycle patterns that visits cannot show.
Fertility treatment
Treatment cycles generate dense data that rarely connects to outcomes over time.
Pregnancy
Sleep, activity and symptoms change week by week.
Menopause
Vasomotor symptoms, sleep and mood fluctuate over years.
Pain score (0–10) over 120 days
Observations2
Events in periodNot visible
Events with no nearby visit4 of 4
Illustrative · synthetic data
How Clarrio.ai addresses the gap
Cycle-aware check-ins
Symptom questions aligned to the participant's cycle or treatment phase.
Wearable physiology
Sleep, temperature trends and heart rate from consented devices.
Clinical linkage
Fertility treatment, labs and pregnancy records via integration.
Privacy by design
Granular consent, role-based access and minimized data sharing.
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
Longitudinal women's health timeline
Cycle, symptoms and physiology on one privacy-conscious view.
Cycle-phase analysis
Symptoms grouped by phase across a cohort.
Treatment-cycle summary
Fertility cycles with associated data, for research.
Consent and access audit
Who can see what, and why.
Pharmaceutical & biotech
Longitudinal real-world evidence in an under-studied therapeutic area.
Fertility clinics & hospitals
Structured histories across treatment cycles.
Research teams
Cycle-aware data collection with strong consent controls.
Patients
Control over their data and a clearer record of their experience.
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 |
|---|---|
| Follow-up efficiency | Remote follow-ups completed relative to due |
| History completeness | Participants with complete cycle records |
| Research data quality | Share of entries with cycle-phase context |
Illustrative use case
Research team, illustrative
Month 0
Participants consent with granular sharing choices.
Months 1–6
Daily pain and symptom check-ins aligned to cycle phase.
Month 3
Cycle-phase analysis reviewed by the research team.
Month 7
De-identified dataset prepared for analysis.
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.
Connect Women's Health Data
Connect Women's Health DataEvidence interpretation
Agree the purpose and sensitivity of each collected field. Keep patient priorities, cycle or life-stage context and clinical observations distinct. Payer or research use needs appropriate governance and should not presume that one population represents every life stage.
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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