Smartphone Usage Among Doctors in the Clinical Setting in Two Culturally Distinct Countries: Cross-sectional Comparative Study
Bibliographic record
Abstract
BACKGROUND: Smartphones and mobile applications have seen a surge in popularity in recent years, a pattern that has also been reflected in the health care system. Despite increased reliance among clinicians however, limited research has been conducted on the uptake and impact of smartphone usage in medical practice, especially outside the Western world. OBJECTIVE: This study aimed to identify the usage of smartphones and medical apps by doctors in the clinical setting in 2 culturally distinct countries: King Hamad University Hospital (KHUH), Bahrain and Queen Mary Hospital (QMH), Hong Kong. METHODS: A cross-sectional, comparative study was conducted where doctors in both hospitals were asked to take part in a 15-item online survey. The questions were categorized into the following groups: demographics of the study population, ownership and main use of smartphones, number and names of medical apps currently owned, rating usage of smartphones for medical purposes, time spent on a smartphone related to clinical use, clinical reliance on smartphones, and views on further integration of smartphones. The results were then tabulated and analyzed using SPSS Statistics 25 for Mac (IBM Corp Inc, Armonk, NY). RESULTS: A total of 200 doctors were surveyed, with a total of 99.0% (99/100) of the doctors owning a smartphone in both KHUH and QMH; 58% (57/99) and 55% (54/99) of the doctors from KHUH and QMH, respectively, identified communication as their main use of smartphones in the clinical setting (P=.004). Doctors from KHUH were likely to spend more time on medical apps than doctors from QMH (P=.002). According to the overall results of both hospitals, 48% (32/67) of the junior doctors claimed high reliance on smartphones, whereas only 32.3% (41/127) of the senior doctors said the same (P=.03). Of doctors in KHUH and QMH, 78.0% (78/100) and 69.0% (69/100), respectively, either strongly agreed or agreed that smartphones need to be integrated into the clinical setting. In terms of preferences for future apps, 48% (48/100) and 56% (56/100) of the doctors in KHUH and QMH, respectively, agreed that more medical applications need to be created in order to support smartphone use in the clinical setting. CONCLUSIONS: These results suggest a substantial acceptance of smartphones by doctors in the clinical setting. It also elicits the need to establish policies to officially integrate smartphone technology into health care in accordance with ethical guidelines. More emphasis should be placed on creating medical applications that aid health care professionals in attaining their information from accurate sources and also regulate a system to monitor the usage of mobile devices within hospitals to prevent a breach of patient privacy and confidentiality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".