Predetermined change control plans for machine learning-enabled medical devices: Guiding principles

On this page

• Background
• Guiding principles
• Contact us

Background

In 2021, the U.S. Food and Drug Administration (FDA), Health Canada and the U.K.'s Medicines and Healthcare products Regulatory Agency (MHRA) jointly identified 10 guiding principles that can inform the development of good machine learning practice (GMLP). GMLP supports the development of safe, effective and high-quality artificial intelligence/machine learning technologies that can learn from real-world use and, in some cases, improve device performance.

In this document, FDA, Health Canada and MHRA jointly identified 5 guiding principles for predetermined change control plans. These principles draw upon the overarching GMLP guiding principles, in particular principle 10, which states that deployed models are monitored for performance and re-training risks are managed.

Advancements in digital health technologies include artificial intelligence/machine learning-enabled medical devices (MLMDs). Regulatory expectations that are aligned with best practices for development and change management, such as those described in the GMLP guiding principles, can help to support the quality of such devices. Ultimately, this can lead to patient benefits such as earlier access to innovative technologies or more accurate diagnoses.

The change management process helps to ensure the ongoing safety and effectiveness of devices in the face of change throughout the device's total product lifecycle (TPLC). However, certain changes to MLMDs, such as changes to a model or algorithm, may be substantive or significant. For this reason, they can require regulatory oversight, such as additional premarket review. Such regulatory expectations may not always coincide with the rapid pace of MLMD development.

Internationally, the medical device community is discussing the use of predetermined change control plans (PCCPs) as a way of managing certain device changes where regulatory authorization before marketing is typically required. PCCPs can be used to help:

• align regulatory processes with the rapid and ongoing approach to change management in MLMDs
• manage risks in a timely and ongoing fashion through monitoring, maintaining and/or improving device performance
• uphold high regulatory standards to ensure device safety and effectiveness

For this document, the term PCCP describes a plan, proposed by a manufacturer, that specifies:

• certain planned modifications to a device
• the protocol for implementing and controlling those modifications and
• the assessment of impacts from modifications

PCCPs may be developed and implemented in different ways in different regulatory jurisdictions.

One key objective of the 5 guiding principles for PCCPs for MLMDs is to provide foundational considerations that highlight the characteristics of robust PCCPs. Another objective of this document is to facilitate and foster ongoing engagement and collaboration among stakeholders on the PCCP concept for MLMDs. As with the GMLP guiding principles, this document lays a foundation for PCCPs and encourages international harmonization.

International harmonization and stakeholder consensus on the core concepts of PCCPs will help support the advancement of responsible innovations in the digital health space.

Related links:

• Good machine learning practice for medical device development: Guiding principles

Guiding principles

1. Focused and bounded

A PCCP describes specific changes that a manufacturer intends to implement. Such changes are limited to modifications within the intended use or intended purpose of the original MLMD. This characterization can include:

• the extent of planned changes and scope of the MLMD with changes implemented
• plans in place to safely modify the device within the bounds of the PCCP, including methods for verifying and validating the changes and mechanisms to detect and revert or stop implementation of a change that fails to meet specified performance criteria
• the impacts of the planned changes

2. Risk-based

The value and reliability of a PCCP is strengthened when the intent, design and implementation of a PCCP are driven by a risk-based approach that adheres to the principles of risk management. This risk-informed perspective is relevant:

• throughout the TPLC, from inception, through implementation and to use
• to ensure that individual and cumulative changes remain appropriate over time for the device and its use environment

3. Evidence-based

Evidence generated throughout the TPLC of the device is important to:

• ensure the ongoing safety and effectiveness of the device with a PCCP
• demonstrate that the benefits outweigh the associated risks and
• establish that the risks are adequately managed and controlled

Considerations for evidence supporting a PCCP include:

• methods and metrics used to measure device performance are scientifically and clinically justified, in proportion to the risk and consistent with other evidence gathered throughout the TPLC
• methods to generate evidence are specified that demonstrate the benefits and risks of the device before and after changes specified in the PCCP are implemented

4. Transparent

For PCCPs, the best practice is to provide clear and appropriate information and detailed plans for ongoing transparency to users and other stakeholders. This helps ensure that stakeholders stay aware of the device's performance and use before and after changes are implemented. Consider, for example:

• how the data used to develop and modify the device is characterized and demonstrated to reflect the intended population
• comprehensive testing for planned changes
• how the device is characterized before and after the changes are implemented
• monitoring, detecting and responding to deviations in device performance

5. Total product lifecycle perspective

Creating and using a PCCP from a TPLC perspective can:

• elevate the quality and integrity of a PCCP by continually considering the perspectives of all stakeholders as well as risk management practices throughout the TPLC
• use and support existing regulatory, quality and risk management measures throughout the TPLC to ensure device safety by monitoring, reporting and responding to safety concerns

Contact us

This work is being spearheaded by the:

• Digital Health Center of Excellence for the FDA
• Medical Devices Directorate Digital Health Division at Health Canada
• software and AI team at the MHRA

We welcome your continued feedback through the FDA public docket (FDA-2019-N-1185). We look forward to engaging with you on these efforts.

You may also email us directly:

• Health Canada: mddpolicy-politiquesdim@hc-sc.gc.ca
• FDA: Digitalhealth@fda.hhs.gov
• MHRA: software@mhra.gov.uk

Related links

• Good Machine Learning Practice for Medical Device Development: Guiding Principles
• Transparency for machine learning-enabled medical devices: Guiding principles
• Draft guidance: Pre-market guidance for machine learning-enabled medical devices
• Notice: Health Canada's Approach to Digital Health Technologies
• Scientific Advisory Committee on Digital Health Technologies (SAC-DHT)