Improve Consent: Patient Risk Perception Analysis

Fast, portable ways to turn interviews and surveys into clearer patient communications are now essential after the MHRA’s Patient Perception of Risk research (published 26 August 2025). The study found widespread recall gaps about implant risks and recommends better verbal and written risk tools. In this post for researchers, UX teams and policy analysts you’ll get: a concise read of the findings (source: www.gov.uk/guidance/breast-implants-patient-perception-of-risk-research), direct implications for qualitative research methods, and a step-by-step AI-enabled workflow using www.evidano.com to operationalize clearer consent and risk communication.

Fast take: what the MHRA study means

The MHRA partnered with a research organisation to interview and survey people who had or planned breast implant surgery, focusing on awareness of Breast Implant Associated–Anaplastic Large Cell Lymphoma (BIA-ALCL). The main finding: many patients either don’t recall receiving risk information or prioritise recovery and outcomes over long-term device risks.

Published: 26 August 2025 (www.gov.uk/guidance/breast-implants-patient-perception-of-risk-research).
Primary concern: limited recall and reliance on social media for information.
MHRA action: new verbal/written tools, implant cards in future regulation, and a Cosmetic Breast Augmentation Risk Awareness Tool.

Findings snapshot

Date / Item	Metric / Element	Value / Note	Implication
26 Aug 2025	Publication	MHRA Patient Perception of Risk research	Source for policy & comms changes
Methods	Data types	In-depth interviews + surveys	Qualitative-heavy; no single n reported in summary
Focus	Clinical risk	BIA-ALCL and implant risks	High-impact, low-recall topic
Patient cohorts	Surgery types	Cosmetic, revision, reconstruction	Diverse motivations; similar recall gaps
Action plan	Comm improvements	Verbal tools + better written info + implant cards	Design testing & implementation needed

What happened: methods and limits

The study used qualitative interviews and surveys with people who had or planned breast implant surgery (cosmetic, revision, reconstructive). Researchers explored what information patients received, how they processed it, and competing priorities that shape risk awareness.

Finding: many participants sourced info from social media and focused on surgery/recovery rather than long-term device risks.
Recall gap: patients often could not remember risk information even when it may have been provided.
Limitations: the public summary does not report sample sizes or full demographic breakdowns, useful to check the full report if you need subgroup analysis.

What this means for researchers, UX teams and policy analysts

For qualitative researchers

Design interview guides to probe recall versus receipt of information (ask for examples, documents, timestamps).

Compare channels (clinic leaflet vs. online vs. social media) when coding sources of knowledge.

Use cross-segment analysis to spot who remembers risks (by surgery type, age, referral path).

For UX & patient-communication teams

Prioritise micro-moments: deliver written risk tools at first consultation and as persistent links (MHRA recommends giving the Risk Awareness Tool at first contact).

Test readability and retention: not just whether a leaflet exists, but whether patients can recall its content after decision points.

Design for friction: small reminders (implant cards, digital follow-ups) increase long-term awareness.

For policy & clinical governance

Embed device identity (implant cards) in patient records as MHRA outlines in future regulatory changes.

Monitor private clinics and stakeholder compliance with improved risk communication guidance.

Use evidence from qualitative studies to inform mandatory information delivery points in care pathways.

How Evidano helps with patient risk perception analysis

Problem: scattered qualitative inputs

Transcripts, clinic leaflets, social posts and survey text live in different silos, making synthesis slow.

Evidano solution: ingest documents and spreadsheets (interview transcripts, surveys, PDFs, scraped web/social content) and normalise them for combined analysis.

Problem: missed recall vs. receipt distinction

Researchers need to code whether patients recall information or only received it.

Evidano solution: AI-assisted thematic coding that identifies mentions of 'recall', 'remember', and document references, and cross-tabulates them against patient cohorts to show where gaps concentrate.

Problem: multilingual or messy audio data

Clinics may have non-standard names, jargon or PII in audio.

Evidano solution: built-in transcription with custom dictionary and PII redaction, plus translation with custom terms to preserve clinical meaning.

Problem: communicating findings to clinicians and patients

Stakeholders need short, actionable outputs (e.g., which leaflet lines aren’t recalled).

Evidano solution: generate thematic summaries, quote collections, co-occurrence networks, and exportable visual reports tied to segments (surgery type, age, referral source).

Security & compliance

Sensitive health data requires strong safeguards.

Evidano solution: encrypted storage and a policy that customer data is never used to train third-party models, suitable for health research workflows.

A practical 7-step workflow to operationalize the MHRA recommendations with AI

Use this checklist to move from raw interviews to tested patient materials in two to four weeks (pilot scale).

1) Import: Gather transcripts, clinic leaflets, the MHRA Risk Awareness Tool and survey responses into Evidano.
2) Transcribe & normalise: Run audio through Evidano transcription with a custom dictionary for implant/clinical terms and PII redaction.
3) Rapid codebook: Seed an initial codebook (recall, receipt, social media, anxiety, primary motivator) and run AI-assisted coding across the corpus.
4) Thematic & cross-segment analysis: Produce themes and compare by cohort (cosmetic / revision / reconstruction) to identify where recall is lowest.
5) Evidence packs: Generate quote bundles and visual co-occurrence maps to show which messages are ignored or misunderstood.
6) Iterate materials: Use findings to revise written wording and script short verbal cues; A/B test delivery timing (first consult vs. follow-up).
7) Monitor & scale: Scrape social channels and incoming surveys to detect shifts; schedule periodic reanalyses to check retention.
Ethics note: this is research-focused guidance and not clinical advice. Ensure informed consent when collecting patient data and follow local governance.

Wrapping up: next steps and CTA

The MHRA’s 26 Aug 2025 report makes the case: having information available is not enough, patients need communication designed for recall and the right delivery points.

If you run qualitative studies, embed cross-segment analyses to find where recall fails; use AI to speed coding and surface evidence-driven wording changes.
Ready to operationalise this? Run a pilot of the 7-step workflow above with your implant study materials, start at www.evidano.com to import transcripts, code faster, and produce clinician-ready evidence packs.

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