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Updated September 30, 2021

Template: Post-Market Clinical Follow-Up Plan (PMCFP)

Sven Piechottka
ISO 13485

This is a free template, provided by OpenRegulatory.

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The following templates are Documents or SOPs related to this template. That means that they mention this template somewhere and (most likely) contain instructions on how and when to fill it out.
Beginning of template

Post-Market Clinical Follow-Up Plan (PMCFP)

This document is used to plan all post-market clinical follow-up activities for the medical device.

Product

Product Name Version
(your product name) (version)

Context

For your orientation, here is guidance documents that may further help you to fill out the template:

  • MEDDEV Guidance 2.7/1 Rev. 4 on Clinical Evaluation
  • MEDDEV Guidance 2.12/2 Rev. 2 on Post-Market Clinical Follow-Up

The post-market clinical follow-up plan is compiled along with concluding the clinical evaluation and is based on the clinical evaluation report. Following Annex XIV MDR, it specifies the methods used to collect and evaluate clinical data with the aim of:

Product Post-Market Clinical Follow-Up

References

The clinical evaluation report outlined the following risk-benefit-profile for the product:

Risk ID Residual Risk Benefit ID Product Benefit
       

PMCF Objectives

In this section, translate each risk and benefit ID from your report above into a specific objective that can be measured. Based on MEDDEV 2.12./2 guidance, you hereby want to express a ‘formal hypothesis’.

For example: if your product is a software to support diagnosis, one of your aims would be to measure a higher sensitivity / specificity of physicians that use your device. The clinical data you collect will support your claimed product benefits at the end of the PMCF interval.

PMCF Methods

In this section, describe the methods you will use to collect the data you specified above. Generally: if you claim that your product will somehow improve the patient’s well-being - how will you measure that?

For example: if your product is based on machine learning, you may want to calibrate and evaluate your model on customer-specific data before they use it in their clinical setting. Results from such evaluation will give an idea of your model’s generalizability (your objective).

Other examples are: clinical investigations, analysis of retrospective data, structured user feedback analysis (e.g. surveys), etc.

PMCF Schedule

PMCF Activity Responsible Role Due Date
     

Template Copyright openregulatory.com. See template license.

Please don’t remove this notice even if you’ve modified contents of this template.

End of template

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