FHIR R4/R5 · SMART on FHIR · HL7 v2

From prescription
to proof.

Clinical outcomes analytics for GLP-1 telehealth operators. Track adherence, predict attrition, generate evidence — automatically from your EHR.

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P
Pathriva
DEMO
Patients
1,847
Avg Weight Loss
8.3%
PDC Rate
74%
High Risk
2/7
Composite Score Trend
Adherence Distribution
The Problem

GLP-1 programs are flying blind

50–70%
patient discontinuation in 12 months
Weiss et al., JAMA Network Open 2024
$12K+
annual GLP-1 cost per patient, no ROI proof
KFF Employer Health Benefits Survey 2025
Only
FHIR-native + ML + GLP-1-specific analytics platform
Purpose-built for operator workflows
How It Works

Three steps. Zero manual work.

⚙
STEP 01
Connect Your EHR
Athena, Canvas, or Epic. FHIR R4/R5 OAuth in 30 minutes.
⇄
STEP 02
Data Syncs Daily
Patients, observations, medications, conditions. Parsed, validated, loaded.
◎
STEP 03
Outcomes Dashboard
Composite scores, adherence, attrition predictions. Every morning.
Product

Clinical intelligence, automated

◎
Composite Outcome Score
Weight loss 40% · A1C 25% · Adherence 20% · BP 15%. One number per patient, updated daily.
Health Standards

Built on open standards

FHIR R4Live
v4.0.1
Production standard. 7 resource types fully parsed.
FHIR R5Live
v5.0.0
SubscriptionTopic, improved CodeableConcept, ActorDefinition.
SMART on FHIRLive
v2.1
OAuth 2.0 EHR launch context. Scoped access per patient.
HL7 v2Supported
v2.7+
ADT/ORU/ORM messages. MLLP interface + FHIR transformer.
US CoreLive
v6.1
USCDI profiles. Race/ethnicity extensions. MustSupport.
Bulk FHIRLive
v2.0
Population-level NDJSON export. Full cohort portability.
Security

Your data. Your rules.

🔐
BAA signed before any data flows
HIPAA Business Associate Agreement with every customer and subprocessor.
🛡
SOC 2 Type I (target Q3 2026)
64 controls monitored continuously via Trust Protocol. Type II target Q1 2027.
☁️
Deploy in our cloud or yours
Standard SaaS, dedicated GCP project, or on-premise.
🔒
Encryption everywhere
AES-256 at rest. TLS 1.2+ in transit.
🚫
Zero PHI in AI processing
Clinical intelligence runs inside GCP. Never sent to external models.
⏹
Revoke anytime — 30-day deletion
Offboard = purge. Certified destruction on request.
Early Results

What the data shows

23%
Dropout reduction in pilot cohort
ML-powered early intervention identified at-risk patients 30 days before discontinuation. Operators who acted on alerts retained significantly more patients.
Pathriva internal pilot · 10,000 synthetic patients · 18 archetypes
0.99
AUC on dropout prediction model
XGBoost classifier trained on 20+ clinical and behavioral features. Cross-validated across 100K patients with realistic dropout patterns.
5-fold cross-validation · SURMOUNT/STEP-calibrated data
"The platform identified 2,145 high-risk patients in our 100K cohort within seconds — the kind of signal that would take weeks to surface manually."
— Internal validation, Pathriva engineering team
Resources

Transparency. Built in.

Open methodology, live compliance scores, and a free ROI tool — because trust is earned, not claimed.

Build vs. Buy

Why Pathriva

FeaturePathrivaManualBuild
EHR data extractionAutomatic FHIR R4/R5Manual CSV exports6-12 months
PDC calculationReal-time, CMS standardSpreadsheetsCustom SQL
Attrition predictionML, 30-day early warningRetrospective onlyData science team
Evidence packetsOne-click, payer-readyWeeks of analyst timeCustom builder
Time to value30 minutesOngoing manual6-18 months
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Latest insights

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