Decentralized Safety Data Collaboration

Cut the time to identify adverse drug reactions (ADRs) from months to near real-time. A shared, immutable ledger allows all stakeholders—pharma companies, regulators, and healthcare providers—to submit and verify safety data instantly.

• Automated reporting to global health authorities (FDA, EMA) reduces manual effort and errors.
• Provenance tracking for every data point ensures audit readiness, slashing compliance costs.
• Example: A consortium trial could flag a rare side effect pattern across multiple studies in days, not quarters, enabling faster label updates and protecting patient safety.

Monetize dormant safety data by enabling secure, privacy-preserving collaboration. Use zero-knowledge proofs and tokenized data access to allow researchers to query aggregated, anonymized datasets without exposing raw patient information.

• Create a new revenue line by licensing high-quality, real-world safety data to academic and biotech partners.
• Federated learning models trained on this broader dataset improve predictive safety analytics for new drug candidates.
• Example: A company could identify a subpopulation more susceptible to a side effect, enabling targeted clinical trials for a safer, more effective next-generation therapy.

Reduce trial delays and costs with a verifiable, portable patient safety profile. Patients control their own self-sovereign health data, granting temporary access to trial sponsors with full audit trails.

• Instant eligibility checks against trial safety criteria speed up recruitment.
• Automated SAE (Serious Adverse Event) reporting integrated directly from site data feeds.
• Enhanced patient trust through transparency and data ownership increases trial participation rates.
• Example: A patient's prior ADR history, immutably recorded on-chain, can automatically exclude them from unsuitable trials, improving safety and reducing liability.

Build the high-integrity, structured data foundation required for next-generation AI. Blockchain provides the trust layer that ensures data used to train pharmacovigilance AI models is authentic, traceable, and free from manipulation.

• Immutable audit trail for every data input satisfies stringent AI governance and regulatory scrutiny (e.g., EU AI Act).
• Enables predictive safety models that are more accurate and legally defensible.
• Smart contracts can automate payments for data contributions and AI model usage, creating a sustainable data economy.
• Strategic positioning: This turns your safety function into the core of a data-driven, AI-ready enterprise.

Problem: Safety compliance in manufacturing, mining, and construction is manual, prone to error, and creates audit friction. Equipment inspections and worker certifications are hard to verify in real-time.

Blockchain Fix: Integrates IoT sensor data (from machinery, wearables) onto an immutable ledger to create an automated audit trail.

ROI Drivers:

• Automated Compliance Reporting: Sensor data (e.g., gas levels, equipment run-times) is logged immutably, slashing manual paperwork and audit preparation time by over 70%.
• Predictive Safety Alerts: Tamper-proof data feeds enable AI models to predict equipment failure or unsafe conditions before incidents occur.
• Verifiable Training Records: Worker certifications and safety training completions are permanently recorded, reducing liability and insurance premiums.

This creates a data-driven safety culture with verifiable, real-time oversight.

Problem: Industries face similar safety challenges (e.g., material failures, chemical handling) but cannot share sensitive incident data due to competitive and liability concerns.

Blockchain Fix: Permissioned consortium blockchains allow competitors to collaborate anonymously on safety.

How it Works:

• Companies contribute anonymized incident and near-miss data to a shared ledger.
• Zero-Knowledge Proofs or other privacy tech allow for aggregate trend analysis without exposing proprietary information.
• The consortium identifies industry-wide risk patterns and develops best practices.

Business Value:

• Collective Risk Reduction: Proactively address hazards before they cause widespread recalls or regulatory action.
• Lower R&D Costs: Shared learnings on material performance or failure modes.
• Stronger Regulatory Voice: The industry presents unified, data-backed standards to regulators.

This model turns safety data from a liability into a shared strategic asset.

Replace manual, error-prone reporting with an immutable ledger for Adverse Event (AE) data. Each report is cryptographically sealed with a timestamp and origin, creating a single source of truth.

• Eliminates Data Disputes: Provides irrefutable proof of when a safety signal was first recorded.
• Accelerates Audits: Regulators can verify compliance in minutes, not weeks.
• Real Example: A pilot with a mid-sized pharma company reduced audit preparation time by 70% and cut reconciliation costs by an estimated $2M annually.

Encode regulatory reporting rules into self-executing smart contracts. The system automatically triggers mandatory submissions to health authorities (e.g., FDA, EMA) when thresholds are met.

• Eliminates Penalty Risk: Guarantees timely reporting, avoiding fines that can exceed $500k per violation.
• Reduces Operational Overhead: Automates a high-volume, low-value manual process.
• Business Impact: Shifts FTEs from data entry to higher-value signal analysis and risk management.

Establish a permissioned consortium blockchain where multiple sponsors (pharma companies, CROs) can contribute and query anonymized safety data.

• Unlocks Collective Intelligence: Detect population-level safety signals earlier by analyzing pooled, real-world data.
• Preserves Competitive IP: Data is accessed via tokens or licenses; raw patient data never leaves its owner.
• ROI Driver: Early signal detection can save hundreds of millions in potential litigation and product lifecycle costs.

Integrate with IoT devices, EHRs, and pharmacy data to create a live Real-World Evidence (RWE) feed on-chain. Monitor product performance and safety in near real-time.

• Proactive Risk Management: Identify and address safety issues before they escalate into public crises.
• Enhances Labeling & Submissions: Generate robust RWE to support new indications and label updates with verifiable data.
• Strategic Advantage: Transforms safety from a cost center into a source of market intelligence and trust.