WhatsApp guidelines – what guidelines? A literature review
Bibliographic record
Abstract
INTRODUCTION: Instant messaging (IM) is pervasive in modern society, including healthcare. WhatsApp, the most cited IM application in healthcare, is used to share sensitive patient information between clinicians. Its use raises legal, regulatory and ethical concerns. Are there guidelines for the clinical use of WhatsApp? Can generic guidelines be developed for the use of IM, for one-to-one and one-to-many healthcare professional communication using WhatsApp as an example? AIM: We aimed to investigate if there are guidelines for using WhatsApp in clinical practice. METHOD: Nine electronic databases were searched in January 2019 for articles on WhatsApp in clinical service. Inclusion criteria: paper was in English, reported on WhatsApp use or potential use in clinical practice, addressed legal, regulatory or ethical issues and presented some form of guideline or guidance for WhatsApp use. RESULTS: In total, 590 unique articles were found and 167 titles and abstracts met the inclusion criteria. Twenty-one articles identified the need for general guidelines. Twelve articles provided some form of guidance for using WhatsApp. Issues addressed were confidentiality, identification and privacy (eight articles), security (seven), record keeping (four) and storage (three). Mandatory national guidelines for the use of IM for patient-sensitive information do not appear to exist, only advisories that counsel against its use. CONCLUSION: The literature showed clinicians use IM because of its simplicity, timeliness and cost effectiveness. No suitable guidelines exist. Generic guidelines are required for the use of IM for healthcare delivery which can be adapted to local circumstance and messaging service used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.046 | 0.034 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".