FDA Predetermined Change Control Plan (PCCP) Template White Paper - Ketryx Compliance Framework

FDA Predetermined Change Control Plan (PCCP) Template

This template is designed to assist organizations in developing a comprehensive plan to manage and assess modifications to Machine Learning Device Software Functions (ML-DSFs) in medical devices.

Reasons to Use This Predetermined Change Control Plan Template

The Predetermined Change Control Plan (PCCP) is a document that outlines the planned changes to the device, the methods for evaluating these changes, and the impact assessment to ensure the continued safety and effectiveness of the device. A comprehensive PCCP for ML-DSFs will incorporate processes outlined in the following standards and Practices:

How to Use This Predetermined Change Control Plan Template

To use this template effectively, please note the following color-coded guidance:

Follow these steps to complete the PCCP template:

  1. Read through the template: Understand the contents of each section and how it could apply to your product.
  2. Customize the Template: Replace all red text with specific information about your medical device and organization.
  3. Describe the ML-DSF: Provide details about the ML-DSF units in your device, including an architecture diagram and a description of how they fulfill the intended use of the product.
  4. Outline Modifications: Clearly describe each planned modification, ensuring they are specific, verifiable, and appropriate for a PCCP.
  5. Develop the Modification Protocol: Use the sections on Data Management Practices, Re-Training Practices, Performance Evaluation, and Update Procedures to develop a protocol for implementing and assessing modifications.
  6. Assess the Impact: Evaluate the impact of each modification and the collective impact of all modifications on the safety and effectiveness of the device.

Example Medical Device Description

The example provided in this template is a wearable medical device that incorporates a Machine Learning Device Software Function (ML-DSF). The ML-DSF is capable of predicting and diagnosing healthcare conditions using sensor data. It features a core algorithm that can be updated remotely and a personal ML-DSF algorithm that continuously learns from the user's real-world data. The modifications outlined in this template include weekly core algorithm re-training.

Disclaimer

The example provided in this template is for illustrative purposes only and is intended to demonstrate how to apply the template to a hypothetical medical device. Users should replace the example with details specific to their own medical device and organization.