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Compliance Management

Compliance in FlaskTrack allows laboratories to define regulatory requirements once and have them automatically enforced throughout laboratory operations.

Instead of relying on manual interpretation of regulations, FlaskTrack evaluates every sample, batch, workflow, protocol, and operational record against the organization's configured compliance policies.

Overview

The compliance engine is built from six concepts.

Framework
    ↓
Compliance Levels
    ↓
Regulatory Tags
    ↓
Authorization Rules
    ↓
Applicability Scopes
    ↓
Compliance Checklists

Each layer builds upon the previous one to determine:

Compliance Frameworks

A Compliance Framework represents an external regulation or an internal organizational policy.

Examples include:

Frameworks define the overall compliance model for a particular regulation.

Each framework contains:

Frameworks do not directly control laboratory operations.

Instead, they provide the policy definitions used throughout the system.

Compliance Levels

Compliance Levels define the severity or operational significance of regulated work.

Examples might include:

Compliance levels are entirely customizable.

A level may require:

The compliance engine evaluates the highest applicable level whenever multiple regulations apply.

Regulatory Tags

Regulatory Tags classify laboratory entities.

Tags describe the work being performed rather than the regulation itself.

Examples include:

Tags may be attached to:

Tags automatically propagate through the laboratory where appropriate.

For example:

Workflow
        ↓
Batch
        ↓
Samples
        ↓
Protocol Execution

This prevents repeated manual classification.

Mapping Regulatory Tags

Regulatory Tags alone do not enforce compliance.

Each tag must be mapped into one or more Compliance Frameworks.

Example:

Tag:
    GMO

Framework:
    NIH Recombinant DNA

Compliance Level:
    High

Another framework could map the same tag differently.

Tag:
    GMO

Framework:
    Internal SOP

Compliance Level:
    Moderate

Multiple frameworks may apply simultaneously.

Authorization Rules

Authorization Rules determine whether work may proceed.

Rules evaluate:

Examples:

Samples
    Read
        Allowed

Samples
    Update
        Requires Approval

Samples
    Delete
        Blocked

Rules may produce one of three outcomes.

Allowed

The operation proceeds immediately.

Requires Approval

The operation requires approval from an authorized individual before continuing.

Blocked

The operation is prohibited.

Users cannot override blocked actions through the user interface.

Applicability Scopes

Not every checklist or authorization rule applies everywhere.

Scopes define where a rule becomes active.

Scopes may evaluate:

For example:

Entity:
    Batch

Domain:
    Tissue Culture

Required Tags:
    GMO

Excluded Tags:
    Archived

Only entities matching every requirement activate the associated compliance requirements.

Compliance Checklists

Checklists define the operational evidence required for compliance.

Examples:

Checklist items become the laboratory's documented evidence during audits.

Reference Documents

Frameworks may include supporting documentation.

Examples:

These documents provide supporting evidence during audits but do not directly control authorization.

Compliance Evaluation

Whenever a user performs an operation, FlaskTrack evaluates the entity.

The evaluation process is:

Load Regulatory Surface
            ↓
Resolve Framework Mappings
            ↓
Determine Highest Compliance Level
            ↓
Evaluate Authorization Rules
            ↓
Determine Applicable Checklists
            ↓
Return Final Decision

This occurs automatically throughout the application.

Regulatory Surface

A Regulatory Surface is the complete collection of regulatory tags affecting an entity.

A Batch may inherit tags from:

The compliance engine combines all applicable tags before evaluating policies.

This prevents accidental omission of inherited regulatory requirements.

Multiple Frameworks

Laboratories often operate under multiple regulatory standards simultaneously.

For example:

NIH Guidelines
        +
BMBL
        +
Internal SOP
        +
ISO 9001

FlaskTrack evaluates every applicable framework independently before combining the results.

The most restrictive applicable authorization always takes precedence.

Approval Workflow

Some work requires management approval before continuing.

When a rule requires approval:

  1. User initiates the operation.
  2. FlaskTrack pauses execution.
  3. Authorized personnel review the request.
  4. Approval or rejection is recorded.
  5. Audit records are generated automatically.

Audit Evidence

Every compliance decision is fully traceable.

Audit history records:

System Policies

The System Policies page provides a read-only explanation of how your compliance system is configured.

It displays:

This view is intended for administrators, auditors, and quality managers who need to understand why a compliance decision was made.

Compliance Dashboard

The Compliance Dashboard provides the operational overview of your organization's compliance program.

From the dashboard you can:

It serves as the central location for managing laboratory compliance.

Best Practices

For most organizations, the recommended implementation order is:

  1. Create Compliance Frameworks.
  2. Define Compliance Levels.
  3. Map Regulatory Tags.
  4. Configure Authorization Rules.
  5. Define Applicability Scopes.
  6. Create Compliance Checklists.
  7. Upload Reference Documents.
  8. Review the System Policies page.
  9. Test compliance using representative laboratory records.
  10. Begin recording compliance events and audits.

Following this order results in a clear, maintainable compliance program that can evolve alongside your laboratory while remaining fully auditable.