JOURNAL ARTICLE
A review of consent policies in dermatological surgery in the UK and the impact of leaner pathways and teledermatology on consent.
Published In: Clinical & Experimental Dermatology, 2025, v. 50, n. 5. P. 911 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Potluru, Aparna; Sokol, Daniel; Wernham, Aaron 3 of 3
Abstract
This article examines the current challenges and developments in obtaining valid informed consent in UK dermatological surgery, particularly following the 2015 Montgomery ruling, which requires clinicians to ensure patients are aware of all material risks and alternatives related to their treatment. It highlights the increasing use of streamlined care pathways—such as one-stop clinics, direct booking to surgery (DBS), and teledermatology—that may limit face-to-face interactions and reduce patients’ time for reflection, potentially compromising the quality and legality of consent. The two-stage consent process, involving initial information provision followed by confirmation at a later time, is advocated to enhance patient understanding and autonomy. To address consent challenges, the article discusses the benefits of multimedia educational tools and standardized procedure-specific consent forms (PSCFs) in improving patient comprehension and satisfaction. Overall, maintaining meaningful, individualized dialogue throughout the care pathway remains essential to uphold ethical and legal standards in dermatological surgery consent.
Additional Information
- Source:Clinical & Experimental Dermatology. 2025/05, Vol. 50, Issue 5, p911
- Document Type:Article
- Subject Area:Law
- Publication Date:2025
- ISSN:0307-6938
- DOI:10.1093/ced/llae500
- Accession Number:185321485
- Copyright Statement:Copyright of Clinical & Experimental Dermatology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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