WhatsApp in Clinical Practice—The Challenges of Record Keeping and Storage. A Scoping Review
Bibliographic record
Abstract
The use of WhatsApp in health care has increased, especially since the COVID-19 pandemic, but there is a need to safeguard electronic patient information when incorporating it into a medical record, be it electronic or paper based. The aim of this study was to review the literature on how clinicians who use WhatsApp in clinical practice keep medical records of the content of WhatsApp messages and how they store WhatsApp messages and/or attachments. A scoping review of nine databases sought evidence of record keeping or data storage related to use of WhatsApp in clinical practice up to 31 December 2020. Sixteen of 346 papers met study criteria. Most clinicians were aware that they must comply with statutory reporting requirements in keeping medical records of all electronic communications. However, this study showed a general lack of awareness or concern about flaunting existing privacy and security legislation. No clear mechanisms for record keeping or data storage of WhatsApp content were provided. In the absence of clear guidelines, problematic practices and workarounds have been created, increasing legal, regulatory and ethical concerns. There is a need to raise awareness of the problems clinicians face in meeting these obligations and to urgently provide viable guidance.
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 | 0.052 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".