Data Inventory & Insights: building the menopause data substrate

EHR clinical data

FHIR R4 via SMART-on-FHIR

• Volume / coverageStructured + unstructured visit notes for ~250M US patients across Epic, Cerner
• Representative signalsVitals, problem list, medications, lab panels (FSH, estradiol, TSH), encounter notes
• Why it mattersThe longitudinal clinical truth set. Where we measure outcomes and where guidelines are applied.

Claims data

X12 837/835, plus partner data lakes

• Volume / coverageVisit, procedure, prescription, and ER utilization across commercial + MA plans
• Representative signalsICD-10 N95.x, CPT for endometrial biopsy / DEXA, RX fills for HT/SSRIs/SNRIs
• Why it mattersQuantifies avoidable utilization and the economic case for the payer-side product.

Patient-generated wearables

HealthKit / Health Connect, Oura, Whoop, Garmin

• Volume / coverageContinuous HRV, sleep, heart rate, skin temperature, cycle data
• Representative signalsSleep fragmentation, resting HR drift, nocturnal heat events, HRV decline patterns
• Why it mattersEarliest and most sensitive signal for perimenopause onset — usually invisible to clinicians.

Patient-reported outcomes (PROs)

Validated instruments + adaptive intake

• Volume / coverageMRS (Menopause Rating Scale), GCS, PHQ-9, GAD-7, plus structured symptom diaries
• Representative signalsVasomotor severity, sleep quality, mood, cognition, sexual function, urogenital
• Why it mattersCaptures the symptoms that drive lived experience but rarely make it into the EHR.

Public research corpora

Open / licensed

• Volume / coverageSWAN, UK Biobank menopause cohort, NHS, PubMed, ClinicalTrials.gov
• Representative signalsTrajectory data, hormone-therapy outcomes, cardiovascular risk modeling
• Why it mattersPretraining and clinical evaluation; provides population-level priors.

Specialty guideline corpus

Structured + retrievable

• Volume / coverageNAMS, ACOG, IMS, AACE position statements; menopause hormone-therapy guidance
• Representative signalsEvidence levels, contraindications, dosing, monitoring intervals
• Why it mattersGrounding source for every recommendation — explainability requires it.

FHIR-native by default

All ingestion conforms to FHIR R4 resources. No bespoke data formats. SMART-on-FHIR auth means we plug into Epic and Cerner without bespoke integration projects.

Patient-side first, EHR-side second

We begin collection on the patient side (wearables + PROs) before the visit, then merge in EHR context. This produces a fuller picture than starting from the EHR alone.

De-identified product telemetry

Every AI recommendation is logged with inputs, outputs, clinician acceptance, and downstream outcome. This dataset compounds in value monthly.

Outcomes registry

With each design-partner contract, we co-build a clinical outcomes registry — diagnostic time, symptom resolution, utilization deltas, HT adoption and adherence.

Federated where required

For health systems unwilling to move data, our inference layer runs federated against an in-VPC deployment. Model weights leave; PHI does not.

Acceptance + outcomes telemetry

Every recommendation accepted, edited, or rejected by a clinician — paired with the eventual outcome — becomes training signal. Competitors without deployments can't replicate this.

Multi-modal patient timeline

Wearable + PRO + EHR merged on a per-patient timeline is rare and expensive to assemble. The longer a patient is on Pause, the more valuable this representation becomes.

Specialty guideline grounding library

A curated, regularly updated, retrievable corpus of menopause clinical guidelines mapped to structured concepts. Hard to build, harder to keep current.

Provider relationships and EHR install base

Each Epic / Cerner deployment takes time and trust. The N-th install is dramatically faster than the first, while remaining a meaningful barrier for newcomers.