Post-Market Clinical Follow-Up Plan (PMCFP)
This document is used to plan all post-market clinical follow-up activities for the medical device.
|(your product name)||(version)|
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:
- confirming the safety and performance of the device throughout its product life cycle,
- identifying previously unknown side-effects and monitoring the identified side-effects and contraindications,
- identifying and analyzing emergent risks on the basis of factual evidence,
- ensuring the continued acceptability of the benefit-risk ratio referred to in sections 1 and 9 of annex I MDR, and
- identifying possible systematic misuse or off-label use of the device, with a view to verifying that the intended purpose is correct.
Product Post-Market Clinical Follow-Up
The clinical evaluation report outlined the following risk-benefit-profile for the product:
|Risk ID||Residual Risk||Benefit ID||Product Benefit|
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.
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 Activity||Responsible Role||Due Date|
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