Portable video education for informed consent: the shape of things to come?
Bibliographic record
Abstract
ORIGINAL ARTICLE, p 1014 Guidance surrounding consent in the U.K. is provided by the General Medical Council.1 Working in partnership with patients to discuss their condition and treatment in a way they understand is recommended. However, with increasing numbers of patients in time-pressured clinics needing to give consent for diagnostic biopsies, procedures or complex treatment, many practitioners agree that the current process for fully informed consent is less than optimal. One way to reconcile this and to enhance the process of consent in the future will undoubtedly be with the use of touch screen video technology. For example, patients could pause and replay information about procedures, risks, aftercare, complications and alternatives in an unhurried manner before providing informed consent. Drop-down menus dealing with ‘frequently asked questions’ could improve comprehension. In this month’s issue of the Journal Armstrong et al.2 show increased knowledge of shave or punch biopsies and postoperative care by use of portable video technology. For patients undergoing procedures, the authors compared video-based education using two short videos against verbal education for informed consent and wound care. As oral education varies between individuals and can be inconsistent, it is unsurprising that portable video media appeared to be more effective for education and resulted in higher satisfaction than traditional oral education. The authors have generously donated their videos.3-6 These can be accessed via the BJD website (http://www.wiley.com/bw/journal.asp?ref=0007-0963&site=1). It is the authors’ conclusion that video education appears to be a promising alternative to traditional oral instruction. However, my feeling is that rather than replace verbal information, portable video media will augment oral and written education and facilitate fully informed consent. Potentially, this could reduce complications and litigation requests and enhance the patient experience. Rarely does a paper promise to change clinical practice immediately. One reviewer was so impressed that he may get an iPad® (Apple Inc., Cupertino, CA, U.S.A.) to educate his patients. Standardized, reproducible and consistent video information could be used for supplementing oral education for complex treatments such as Mohs micrographic surgery, phototherapy or treatment with biologics which perhaps require a more detailed understanding before fully informed comment can be said to have been obtained. One can envision an entire industry dedicated to producing video education libraries for patients prior to consent akin to safety videos before an airline flight. Elsewhere, other researchers have reported that video information can reduce pain and anxiety in those proposing to undergo nondermatological procedures. Video education preconsent may become the future gold standard. In my view it is the shape of things to come and I recommend the paper and videos to you.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".