The world's first
pain-centric
real-world dataset.
260,000 de-identified patients. Longitudinal PROs, procedures, diagnoses, medications, and utilization signals — continuously updated from real-world pain care settings. The benchmarking and external control layer that makes every evidence program more defensible.
The benchmarking layer.
Not the registry layer.
PainRWD™ Science Cloud has a specific, powerful role in Celéri's evidence infrastructure. Understanding what it is — and what it isn't — is what makes it useful.
A large-scale, de-identified real-world pain dataset used for benchmarking, external control, and comparative insights.
- External control arm for sponsor evidence programs — contextualize your cohort against 260k real-world patients
- Benchmarking layer for EaaS™ dashboards — surfaces how a sponsor's outcomes compare to the broader pain population
- HEOR modeling and payer dossier support — real-world treatment pathways, costs, and outcomes at scale
- Feasibility assessment tool — size a target cohort before committing to a prospective study
- Hypothesis generation for investigator-initiated research and publication
- Pre-defined disease cohorts for fast collaboration — SCS, SI Joint Fusion, Opioids, Cancer, Orthopedics, Diabetes
- Patient list segments for surveys or educational outreach — opt-in ready
A registry platform, an enrollment engine, or a sponsor's primary evidence infrastructure.
- Not the EaaS™ platform — PainRWD™ is a secondary dataset, not the primary evidence generation engine
- Not registry infrastructure for a client engagement — sponsor data is collected through EaaS™, not from this cloud
- Not a replacement for prospective evidence — it contextualizes and supports, it doesn't substitute for protocol-driven data
- Not identified patient data — de-identified by design; individual patient matching requires updated consent or IRB pathway
- Not a point-of-care tool — PainIntel™ handles practice-level data capture; PainRWD™ is the aggregate layer above it
How sponsors, researchers, and payers use it.
PainRWD™ Science Cloud is purpose-built for the questions that can't be answered by a single site's data — or a single sponsor's cohort.
External control group & benchmarking
Contextualize your EaaS™ registry outcomes against 260k real-world pain patients. Strengthen payer submissions by showing how your device or therapy performs relative to the real-world standard of care — without running a parallel study.
Comparative effectiveness & utilization
Understand how pain treatments perform across diverse real-world populations — across indications, payer types, and geographies. Surface overuse, under-response, and true value in weeks. Structure value-based contracts with RWE that goes beyond what RCTs can show.
Hypothesis generation & feasibility
Size your target population before committing to a prospective study. Explore treatment patterns, responder characteristics, and outcome trajectories across your disease area of interest. Pre-defined cohorts are ready for fast collaboration — no data collection required to start.
HEOR modeling & payer dossiers
Real-world treatment pathways, cost signals, downstream utilization, and longitudinal outcomes — structured for health economic modeling. Accelerate dossier development without a standalone RWE study by drawing on a continuously-updating dataset already structured for that purpose.
Patient list segments for targeted engagement
Science Cloud patients have been pre-connected and offered opt-in participation in custom surveys or private invitation to local provider education sessions. Choose your target population by diagnosis, therapy, geography, or PRO profile — just like a research cohort.
Treatment pathway comparison
Demonstrate how your therapy compares to existing standards of care in real-world settings. Support label expansion, post-market requirements, and publication with a comparator dataset that regulators and journal editors recognize as methodologically sound.
Pre-built cohorts. Ready for fast collaboration.
Celéri has done the initial cohort definition work for you. Each disease community is a structured, pre-defined subset of the 260k patient dataset — with longitudinal PROs, procedures, medications, and utilization signals already mapped.
Spinal Cord Stimulation
Patients who underwent SCS trial and implant procedures — with corresponding longitudinal outcomes including NRS, PGIC, PROMIS-29, ZCQ, and ODI. Ideal for device sponsors seeking external control data or comparative benchmarking.
SI Joint Fusion
Outcomes data across SI joint fusion procedures — posterior approach and beyond. Research endpoints structured for publication-ready analysis and payer dossier support.
Opioids & Non-Opioid Pharmacotherapy
Deep data on the poly-pharmacy of pain patients — first-line vs. second-line therapies, longitudinal drug use, responder profiles, drug combinations, and downstream outcomes.
Cancer Pain
Continuously updating dataset filtered to patients with active neoplasms or metastatic disease. Analyze pain interventions across cancer presentations, co-morbid conditions, and treatment trajectories.
Orthopedics & Neurology
Pain patients across stenosis, spondylolisthesis, failed back surgery, osteoarthritis, carpal tunnel, cubital tunnel, lateral and medial epicondylitis, ligament injuries, and migraine.
Diabetes & Painful Neuropathy
Patients with painful diabetic neuropathy — peripheral, proximal, autonomic, and focal — with co-morbid conditions including CVD, hypertension, and hyperlipidemia mapped alongside pain outcomes.
The aggregate layer above the platform.
PainRWD™ Science Cloud sits beneath every EaaS™ engagement as a benchmarking and external control resource — and above point-of-care data capture as the aggregate intelligence layer. It doesn't replace the platform. It makes the platform more powerful.
Peer-reviewed recognition is coming.
The scientific community is beginning to recognize PainRWD™ Science Cloud as a methodologically significant contribution to real-world pain research. That recognition is about to become very public.
Pain data with SDOH context.
PainRWD™ Science Cloud integrates ZIP-level community SDOH data — food insecurity, housing instability, transportation access, economic stress — alongside clinical pain outcomes. Understanding why patients respond differently requires knowing the environment they live in, not just the procedures they received.
Individual-level SDOH matching is available through structured pathways with appropriate consent or IRB coverage. ZIP-level community data is immediately actionable for population health analysis, payer contracting, and care gap identification.
Three ways to work with the data.
Access to PainRWD™ Science Cloud is structured around your use case — from fast cohort analysis to deeply integrated sponsor benchmarking.
Sponsor Benchmarking
EaaS™ platform engagements include automated benchmarking of sponsor outcomes against the PainRWD™ dataset. Your dashboard surfaces how your cohort performs relative to the broader real-world pain population — by indication, payer, and geography — without a separate data access agreement.
Disease Cohort Analysis
Access one or more pre-defined disease communities for HEOR modeling, feasibility assessment, hypothesis generation, or payer dossier development. Celéri's data science team supports study design, analysis, and output formatting for your specific deliverable.
Investigator Partnership
Clinician-researchers and academic institutions can collaborate with Celéri's Research Group to leverage PainRWD™ for investigator-initiated studies, hypothesis-driven publications, and grant-supported research. The dataset infrastructure is already in place — you bring the scientific question.
Talk to our data science team.
Whether you're a sponsor looking for an external control group, a payer building a value-based framework, or a researcher with a hypothesis and no dataset — PainRWD™ Science Cloud is the starting point.
Site-based, non-research, growth-focused
