Smartphone and App Usage in Orthopedics and Trauma Surgery: Survey Study of Physicians Regarding Acceptance, Risks, and Future Prospects in Germany
Bibliographic record
Abstract
BACKGROUND: In the course of digitization, smartphones are affecting an increasing number of areas of users' lives, giving them almost ubiquitous access to the internet and other web applications. Mobile health (mHealth) has become an integral part of some areas of patient care. In contrast to other disciplines, routine integration of mobile devices in orthopedics and trauma surgery in Germany is still in its infancy. OBJECTIVE: This study aimed to investigate physicians' current state of opinion regarding acceptance, future prospects, and risks of medical apps in the field of orthopedics and trauma surgery in Germany. METHODS: A web-based survey among orthopedics and trauma surgeons in German university hospitals on the use of medical apps in everyday clinical practice was conducted between September 2018 and February 2019. The survey consisted of 13 open- and closed-ended or multiple-choice questions. A logistic regression analysis was performed to ascertain the effects of interindividual characteristics on the likelihood of participants' app and smartphone usage behavior. RESULTS: A total of 206 physicians participated in the survey. All of the participants (206/206, 100%) owned a smartphone, and 79.1% (159/201) used the device, while 64.7% (130/201) used apps regularly in everyday clinical practice. Medical apps were perceived as beneficial, given their substantial future promise, by 90.1% (181/201) of the participants. However, 62.5% (120/192) of the participants were not satisfied with the current supply of medical apps in app stores. Desired specifications for future apps were "intuitive usability" (167/201, 83.1%), "no advertising" (145/201, 72.1%), and "free apps" (92/201, 45.8%). The attributes "transparent app development and app sponsoring" (75/201, 37.3%) and the existence of an "easy-to-understand privacy statement" (50/201, 24.9%) were of minor relevance. The majority of the participants (162/194, 83.5%) considered that future apps in the field of "medical research" would provide the greatest benefit. The greatest predicted risks were "data misuse" (147/189, 77.8%), "usage of untrustworthy apps" (135/189, 71.4%), and "alienation from patients" (51/189, 27.0%). Increasing age was significantly associated with a reduction in the likelihood of regular smartphone (odds ratio [OR] 0.91, 95% CI 0.86-0.97; P=.002) and app (OR 0.90, 95% CI 0.85-0.96; P=.001) usage, while the medical profession grade had no significant impact on the usage behavior. CONCLUSIONS: The study demonstrates that young German doctors in orthopedics and trauma surgery already use smartphones and apps in everyday clinical practice. Medical apps are considered to play an important role in the future. However, a significant discrepancy exists between the supply and demand of mHealth applications, which creates a legal and ethical vacuum with regard to data protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".