Real-World Evidence & Post-Market Surveillance Center

A real-world evidence platform for Pharma and Medtech that continuously captures, validates, and activates post-market data — reducing risk, accelerating insight, and strengthening product performance.

Discuss Your PMS & RWE Strategy

WHO THIS IS FOR

Built for real-world evidence teams in pharma

Regulatory Affairs, Clinical Operations, Medical Affairs, and Data leaders responsible for safety, outcomes, and real-world evidence.

Clean flat vector illustration of a real-world evidence platform for pharma showing distinct professional roles aligned around a shared evidence foundation, representing coordinated collaboration across regulatory, clinical, medical, and data functions.
Real-World Evidence Platform for Pharma – Fragmented Post-Market Evidence

THE CHALLENGE

Evidence is collected — but not connected

Post-market data exists across registries, hospitals, and vendors.

Execution remains fragmented, manual, delayed, and difficult to aggregate.

Safety signals emerge late.
Performance is hard to compare.
Regulatory reporting is reactive.

The structural limitation: Traditional PMS and RWE systems are study-based, vendor-dependent and episodic

Data is collected in cycles.

Insight is produced afterwards.
Decisions lag behind reality.

THE SOLUTION

Continuous, connected evidence generation

Meplis provides a real-world evidence platform for pharma that structures post-market data within a single connected environment.

Data is captured directly from:

  • HCP-reported input
  • hospital systems
  • patient-reported outcomes

Not as isolated datasets, but as a continuously evolving evidence system.

Evidence is generated during clinical practice — not reconstructed later.

Clean flat vector illustration of a real-world evidence platform for pharma showing previously separate evidence inputs aligned within one connected environment, with continuous relationships between data sources enabling integrated and operational evidence generation.
Clean flat vector illustration of a real-world evidence platform for pharma showing a continuous closed-loop system where evidence is captured, structured, and refined within a stable, self-reinforcing environment without interruption or reset.

HOW IT WORKS

A continuous evidence loop

Data is captured at source, structured immediately, and monitored as it evolves.

There is no separation between collection and analysis.

Signal detection

Structured data across sites enables continuous comparison.

Patterns, deviations, and safety signals are identified as they emerge.

Traceability and compliance

Every data point remains linked to source, context, and timeline.

Consent is captured at source.
Audit trails are continuously maintained.

Regulatory readiness is built into the system.

EXECUTION MODELS

Workspace-based evidence environment

Delivered as a dedicated workspace:

  • structured studies and registries
  • controlled collaboration across manufacturer, CRO, and sites
  • role-based dashboards and outputs

Evidence is captured where it happens:

  • hospital environments
  • HCP interaction
  • ongoing clinical use

Data flows directly from practice into evidence.

Clean flat vector illustration of a real-world evidence platform for pharma showing a bounded evidence environment receiving structured inputs from multiple real-world data sources, maintaining coherence, control, and continuous evidence capture across connected domains.
real-world-evidence-platform-for-pharma-delayed-to-continuous-intelligence

FROM GAPS TO OPPORTUNITIES

From episodic studies to continuous evidence

Post-market evidence is generated in cycles. Collected, aggregated, analyzed after the fact.
Each phase resets, signals appear late, and decisions follow outdated data.

Meplis removes the cycle.

Data is captured as it is generated, structured immediately, and evaluated continuously.
No separation between collection and analysis. No reset between phases.

Evidence evolves in real time across sites and populations.
Earlier signals, continuous visibility, direct linkage to action.

Evidence becomes operational, not retrospective.

WHY THIS APPROACH IS DIFFERENT

From registries to connected evidence systems

Traditional PMS and RWE rely on externalized data — registries, vendors, delayed aggregation outside clinical workflows.
Data is fragmented, ownership is unclear, integration remains complex.

Meplis captures evidence at the source — within HCP interaction, hospital systems, and patient-reported outcomes.
Data is structured immediately and remains connected across sites, stakeholders, and lifecycle stages.

Evidence is not extracted from the field. It is generated within it.

Clean flat vector illustration of a real-world evidence platform for pharma showing evidence generated directly within real-world activity, with data embedded in ongoing interactions and continuously captured and used without separation or delay.
Clean flat vector illustration of a real-world evidence platform for pharma showing dispersed evidence elements consolidating into fewer, stronger and more structured outcomes, representing reduced waste and increased strategic value.

COMMERCIAL & STRATEGIC IMPACT

Real world evidence & PMS solution for business impact

  • faster regulatory response reduces risk exposure
  • earlier insight supports label expansion
  • reduced dependency on external registries lowers cost
  • stronger evidence improves market positioning

Data becomes unified.
Duplication is reduced.
Insights are reusable across lifecycle stages

Post-market evidence becomes a strategic asset.

EXECUTION FLOW

Continuous evidence loop

Post-market evidence does not follow a fixed sequence.

Data is captured, monitored, validated, and acted on as it evolves.
There is no defined start or end point — only continuous progression.

Each interaction contributes to the next.
Each signal feeds further analysis.
Each insight informs immediate action.

Evidence is not processed in steps.
It compounds over time.

Clean flat vector illustration of a real-world evidence platform for pharma showing a continuous, self-reinforcing evidence cycle where elements circulate within a stable loop and gradually become more refined and connected over time.
Clean flat vector illustration of a real-world evidence platform for pharma showing post-market evidence evolving from local operational activity into a stable, higher-level decision layer, representing reusable organizational intelligence and structured decision-making.

REAL INTELLIGENCE LAYER

Intelligence Layer

Post-market evidence is not just stored. It is structured into a reusable decision layer.

Signals, outcomes, and site-level patterns are connected into a company-specific view of product performance.

It gives teams one place to see what is changing, where it is changing, and what requires action.

Not more reports.
Better decisions from connected evidence.

LIFECYCLE CONNECTION

Closing the lifecycle loop

Post-market evidence should not stop at surveillance.

It should improve product design, sharpen study strategy, support expert review, and strengthen evidence-based market execution.

That is where the loop closes: post-launch evidence informs pre-launch, clinical, medical, and commercial decisions.

One evidence stream.
Multiple business uses.
One connected lifecycle.

Real-World Evidence Platform for Pharma – Reusable Evidence Across Lifecycle Contexts
Clean flat vector illustration of a real-world evidence platform for pharma showing multiple existing external systems remaining intact while being connected into one continuous evidence environment, enabling interoperability and unified evidence use without replacing original systems.

INTEGRATION

A real-world evidence platform for pharma that connects, not replaces

Meplis integrates with EHR and EMR environments, safety databases, regulatory systems, and analytics tools through standard interfaces such as FHIR and HL7.

The objective is not to create another isolated system, but to turn existing data flows into a connected evidence environment.

START SMALL

Start with a focused scope

Deploy around one product, selected sites, and a defined population.

Prove the model in a focused scope. Then extend it across products, indications, and evidence streams.

Minimalist flat vector illustration of a Clinical Trial Recruitment and Site Selection Platform showing a focused, early-stage setup with a central concept connected to key clinical trial elements, representing structured site matching and efficient study initiation.

Turn post-market evidence into action

Move from fragmented reporting to a connected evidence system that supports faster decisions, stronger compliance, and better product performance.

Discuss Your Use Case

Frequently Asked Questions

A: Traditional solutions are usually study-based, vendor-driven, and episodic. Meplis structures evidence as a continuous system, with data captured closer to clinical practice and connected across sites, stakeholders, and lifecycle stages.

A: No. It supports regulatory and quality obligations, but the value goes beyond compliance. The same evidence can support product improvement, study refinement, advisory validation, and evidence-based commercial and medical execution.

A: It can complement or replace them, depending on the use case. The core advantage is that evidence no longer has to remain fragmented across separate registries, vendors, and reporting cycles.

A: Evidence is captured with source, context, and timeline preserved. Consent, data lineage, and auditability are built into the system, so regulatory readiness is continuous rather than reconstructed later.

A: The system connects structured and ongoing evidence from HCP input, hospital systems, and patient-reported outcomes. It can also integrate with existing clinical, safety, and analytics environments.

A: Post-market evidence should not end in reporting. In Meplis, it can feed product improvement in R&D, protocol refinement in trials, expert review in advisory, and evidence-based communication after launch.

A: Yes. A focused start is possible with one product, a limited number of sites, and a defined evidence objective. That allows teams to validate data flows, reporting logic, and signal monitoring before scaling further.