PainRWD™ Science Cloud
A pain-centric real-world dataset, built for benchmarking.
De-identified patients at scale. 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.
*De-identified patient records in the PainRWD™ Science Cloud as of Q3 2026. A formal data descriptor is in preparation for peer-reviewed publication; figures will be updated on publication.
The benchmarking layer. Not the registry layer.
PainRWD™ Science Cloud has a specific, powerful role in Celéri Health'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 real-world pain patients at scale
- Benchmarking layer for EaaS™ Command Centers — shows how a sponsor's outcomes compare to the broader pain population
- HEOR modeling and payer dossier support — real-world treatment pathways, costs, and outcomes
- Feasibility assessment — size a target cohort before committing to a prospective study
- Hypothesis generation for investigator-initiated research and publication
- Pre-defined disease cohorts — SCS, SI Joint Fusion, Opioids, Cancer, Orthopedics, Diabetes
- Opt-in-ready patient list segments for surveys or educational outreach
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 does not substitute for protocol-driven data
- Not identified patient data — de-identified by design; individual patient matching requires updated consent or an 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 and benchmarking
Contextualize your EaaS™ registry outcomes against real-world pain patients. Strengthen payer submissions by showing how your device or therapy performs relative to real-world standard of care — without running a parallel study.
Comparative effectiveness and utilization
Understand how pain treatments perform across diverse real-world populations — across indications, payer types, and geographies. Surface overuse, under-response, and true value. Structure value-based contracts with RWE that goes beyond what RCTs can show.
Hypothesis generation and feasibility
Size your target population before committing to a prospective study. Explore treatment patterns, responder characteristics, and outcome trajectories in your disease area. Pre-defined cohorts are ready for collaboration — no data collection required to start.
HEOR modeling and 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.
Patient segments for targeted engagement
Science Cloud patients can be offered opt-in participation in custom surveys or private invitations to local provider education sessions. Define the target population by diagnosis, therapy, geography, or PRO profile — just like a research cohort.
Treatment pathway comparison
Show 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 built for methodological scrutiny.
Pre-built cohorts. Ready for collaboration.
Celéri Health has done the initial cohort definition work. Each disease community is a structured, pre-defined subset of the dataset — with longitudinal PROs, procedures, medications, and utilization signals already mapped.
Spinal Cord Stimulation
Patients who underwent SCS trial and implant procedures — with longitudinal NRS, PGIC, PROMIS®-29, ZCQ, and ODI. Built for device sponsors seeking external control data or comparative benchmarking.
SI Joint Fusion
Outcomes across SI joint fusion procedures — posterior approach and beyond. Research endpoints structured for publication-ready analysis and payer dossier support.
Opioid and Non-Opioid Pharmacotherapy
The polypharmacy of pain patients — first-line vs. second-line therapies, longitudinal drug use, responder profiles, combinations, and downstream outcomes.
Cancer Pain
Continuously updating cohort of patients with active neoplasms or metastatic disease. Analyze pain interventions across cancer presentations, comorbid conditions, and treatment trajectories.
Orthopedics and Neurology
Stenosis, spondylolisthesis, failed back surgery, osteoarthritis, carpal and cubital tunnel, epicondylitis, ligament injuries, and migraine.
Diabetes and Painful Neuropathy
Painful diabetic neuropathy — peripheral, proximal, autonomic, and focal — with comorbid CVD, hypertension, and hyperlipidemia mapped alongside pain outcomes.
The aggregate layer above the platform.
PainRWD™ Science Cloud sits alongside 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.
Documented for methodological scrutiny.
A dataset is only as useful as its documentation. PainRWD™ Science Cloud is being described formally so that sponsors, payers, and reviewers can evaluate its provenance, structure, and limitations on the record.
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 usable 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™ engagements include benchmarking of sponsor outcomes against the PainRWD™ dataset. Your Command Center shows 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 Health's data science team supports study design, analysis, and output formatting for your deliverable.
Investigator Partnership
Clinician-researchers and academic institutions can collaborate with the Celéri Research Group to use PainRWD™ for investigator-initiated studies, hypothesis-driven publications, and grant-supported research. The dataset infrastructure is 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
