WhatsApp Messenger as an Adjunctive Tool for Telemedicine: An Overview
Bibliographic record
Abstract
BACKGROUND: The advent of telemedicine has allowed physicians to deliver medical treatment to patients from a distance. Mobile apps such as WhatsApp Messenger, an instant messaging service, came as a novel concept in all fields of social life, including medicine. The use of instant messaging services has been shown to improve communication within medical teams by providing means for quick teleconsultation, information sharing, and starting treatment as soon as possible. OBJECTIVE: The aim of this study was to perform a comprehensive systematic review of present literature on the use of the WhatsApp Messenger app as an adjunctive health care tool for medical doctors. METHODS: Searches were performed in PubMed, EMBASE, and the Cochrane Library using the term "whatsapp*" in articles published before January 2016. A bibliography of all relevant original articles that used the WhatsApp Messenger app was created. The level of evidence of each study was determined according to the Oxford Levels of Evidence ranking system produced by the Oxford Centre for Evidence-Based Medicine. The impact and the indications of WhatsApp Messenger are discussed in order to understand the extent to which this app currently functions as an adjunctive tool for telemedicine. RESULTS: The database search identified a total of 30 studies in which the term "whatsapp*" was used. Each article's list of references was evaluated item-by-item. After literature reviews, letters to the editor, and low-quality studies were excluded, a total of 10 studies were found to be eligible for inclusion. Of these studies, 9 had been published in the English language and 1 had been published in Spanish. Five were published by medical doctors. CONCLUSIONS: The pooled data presents compelling evidence that the WhatsApp Messenger app is a promising system, whether used as a communication tool between health care professionals, as a means of communication between health care professionals and the general public, or as a learning tool for providing health care information to professionals or to the general population. However, high-quality and properly evaluated research is needed, as are improvements in descriptions of the methodology and the study processes. These improvements will allow WhatsApp Messenger to be categorically defined as an effective telemedicine tool in many different fields of health care.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".