Glossary

Predetermined Change Control Plan (PCCP)

An FDA-authorized plan detailing the types of modifications a manufacturer intends to make to a machine learning-enabled device without requiring a new marketing submission.

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FDA REGULATORY MECHANISM

What is Predetermined Change Control Plan (PCCP)?

A Predetermined Change Control Plan (PCCP) is an FDA-authorized document detailing the specific types of modifications a manufacturer intends to make to a machine learning-enabled medical device without requiring a new marketing submission.

A Predetermined Change Control Plan (PCCP) is a regulatory mechanism that allows manufacturers to prospectively define and seek pre-authorization for planned modifications to Software as a Medical Device (SaMD). This framework is specifically designed for adaptive algorithms and machine learning models that are expected to improve over time. By agreeing on the scope, methodology, and acceptance criteria of future changes upfront, the PCCP eliminates the need for repeated 510(k) submissions for every iterative update, enabling continuous learning while maintaining safety and effectiveness.

The PCCP must rigorously detail the data management and re-training practices, the specific performance metrics used for analytical validation, and the statistical thresholds that trigger a modification. It serves as a binding agreement between the manufacturer and the FDA, ensuring that locked algorithms can evolve into adaptive systems under strict, transparent guardrails. This plan is a critical component of a robust Quality Management System (QMS) and directly supports compliance with IEC 62304 and ISO 14971 risk management standards.

ANATOMY OF A CHANGE CONTROL PLAN

Core Components of a PCCP

A Predetermined Change Control Plan (PCCP) is a regulatory mechanism that allows manufacturers to specify planned modifications to a machine learning-enabled device without requiring a new marketing submission. The following components define its structure.

Description of Modifications

A detailed, granular specification of the intended changes to the device. This section must clearly define what will be modified, including the specific algorithmic parameters, input data types, or performance characteristics that are subject to change. It must distinguish between modifications that are within the scope of the plan and those that would require a new submission. The description must be sufficiently precise to allow the FDA to evaluate the safety and effectiveness implications of the planned changes before they are implemented.

Modification Protocol

The standardized, repeatable process by which the manufacturer will develop, validate, and implement the planned modifications. This protocol must include:

Data management procedures for training, tuning, and test sets
Verification and validation (V&V) activities to confirm the modified device meets specifications
Performance assessment methodologies to compare pre- and post-modification outputs
Acceptance criteria that must be satisfied before the modification is deployed The protocol ensures that every change follows a controlled, auditable, and reproducible workflow.

Impact Assessment

A prospective analysis of the benefit-risk profile for each planned modification. This component evaluates how the change could affect the device's safety, effectiveness, and clinical performance. It must address:

Potential failure modes introduced by the modification
Hazard analysis and risk mitigation strategies
The anticipated impact on diagnostic accuracy, sensitivity, and specificity
Any changes to the intended use or indications for use The impact assessment demonstrates that the manufacturer has proactively considered and mitigated risks before implementation.

Methodology for Change Implementation

The engineering and operational procedures governing how modifications are deployed into the production device. This includes:

Version control mechanisms to track all software iterations
Rollback procedures in case of unexpected performance degradation
Configuration management to ensure the correct model is deployed to the correct device
Cybersecurity considerations for software updates, including Software Bill of Materials (SBOM) updates This section bridges the gap between the regulatory plan and the DevOps pipeline for SaMD.

Post-Market Performance Monitoring

A continuous surveillance strategy to detect drift, degradation, or unintended behavior after a modification is deployed. This component specifies:

Real-world performance metrics to be tracked (e.g., sensitivity, specificity, PPV)
Data collection mechanisms from clinical use
Thresholds that trigger a review or corrective action
Reporting procedures for adverse events via Medical Device Reporting (MDR) This ongoing monitoring is essential for maintaining the safety and effectiveness of an adaptive algorithm throughout its total product lifecycle.

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PREDETERMINED CHANGE CONTROL PLAN

Frequently Asked Questions

Clear answers to the most common regulatory and technical questions about the FDA's Predetermined Change Control Plan (PCCP) for machine learning-enabled medical devices.

A Predetermined Change Control Plan (PCCP) is an FDA-authorized document that describes the specific types of modifications a manufacturer intends to make to a machine learning-enabled device without requiring a new marketing submission. The PCCP works by defining the scope of planned changes, the methodology for implementing those changes, and the protocol for validating that the modified device remains safe and effective. This mechanism acknowledges that AI/ML-based Software as a Medical Device (SaMD) is designed to learn and improve over time, allowing manufacturers to iterate on algorithms within pre-approved boundaries while maintaining regulatory compliance. The PCCP is submitted as part of a 510(k), De Novo, or PMA submission and, once accepted, becomes a binding part of the device's clearance or approval.

REGULATORY ECOSYSTEM

Related Terms

A Predetermined Change Control Plan (PCCP) does not exist in isolation. It is a critical component of a broader regulatory and quality management framework. The following concepts define the ecosystem in which a PCCP operates.

Adaptive Algorithm

A machine learning model whose behavior changes over time based on new data inputs. Unlike a locked algorithm, an adaptive algorithm continues to learn after deployment. A PCCP is the primary regulatory mechanism for gaining FDA clearance for an adaptive algorithm, as it pre-specifies the bounds of acceptable change without requiring a new 510(k) submission for each update.

PCCP-Required

Regulatory Pathway

Locked Algorithm

A machine learning model whose learned parameters and decision-making logic are fixed at the point of deployment. Any modification to a locked algorithm—such as retraining on new data or changing a threshold—requires a new marketing submission. The PCCP was specifically created to provide a controlled alternative to this rigid, submission-heavy paradigm for Software as a Medical Device (SaMD).

New Submission

Required for Any Change

Software as a Medical Device (SaMD)

Software intended to be used for one or more medical purposes that performs those purposes without being part of a hardware medical device. A PCCP is exclusively applicable to SaMD that incorporates machine learning. The FDA's guidance on PCCPs is a cornerstone of the agency's broader digital health innovation strategy, acknowledging that software's iterative nature requires a different regulatory approach than traditional hardware.

IEC 62304

Governing Lifecycle Standard

Verification and Validation (V&V)

The combined processes of confirming that design outputs meet design inputs (verification) and that the final device meets user needs and intended uses (validation). A robust PCCP must detail the V&V activities that will be performed on each planned modification to ensure that the updated algorithm remains safe and effective. This includes pre-defined performance metrics and acceptance criteria.

Design Inputs

Verification Focus

User Needs

Validation Focus

Post-Market Surveillance (PMS)

The proactive, systematic process of collecting and analyzing real-world data on a device's performance after market release. A PCCP is intrinsically linked to PMS, as the plan must define how real-world performance data will be monitored to detect drift, bias, or degradation. This monitoring loop is the evidence base that confirms the algorithm is changing within its authorized boundaries.

Real-World Data

Monitoring Source

Quality Management System (QMS)

A formalized system documenting processes, procedures, and responsibilities for achieving quality objectives, such as ISO 13485. A PCCP is not a standalone document; it is a controlled output of a manufacturer's QMS. The QMS governs the change control procedures, documentation practices, and risk management activities that operationalize the PCCP throughout the product lifecycle.

ISO 13485

Core Standard

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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