A Mobile Medical Knowledge Dissemination Platform (HeadToToe): Mixed Methods Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Finding readily accessible, high-quality medical references can be a challenging task. HeadToToe is a mobile platform designed to allow easy and quick access to sound, up-to-date, and validated medical knowledge and guidance. It provides easy access to essential clinical medical content in the form of documents, videos, clinical scores, and other formats for the day-to-day access and use by medical students and physicians during their pre- and postgraduate education. OBJECTIVE The aim of this paper is to describe the architecture, user interface, and potential strengths and limitations of an innovative knowledge dissemination platform developed at the University of Geneva, Switzerland. We also report preliminary results from a user-experience survey and usage statistics over a selected period. METHODS The dissemination platform consists of a smartphone app. Through an administration interface, content is managed by senior university and hospital staff. The app includes the following sections: (1) main section of medical guidance, organized by clinical field; (2) checklists for history-taking and clinical examination, organized by body systems; (3) laboratory section with frequently used lab values; and (4) favorites section. Each content item is programmed to be available for a given duration as defined by the content’s author. Automatic notifications signal the author when the content is about to expire, hence, promoting its timely updating and reducing the risk of using obsolete content. In the background, a third-party statistical collecting tool records anonymous utilization statistics. RESULTS We launched the final version of the platform in March 2019, both at the Faculty of Medicine at the University of Geneva and at the University Hospital of Geneva in Switzerland. A total of 622 students at the university and 613 health professionals at the hospital downloaded the app. Two-thirds of users at both institutions had an iOS device. During the practical examination period (ie, May 2019) there was a significant increase in the number of active users (P=.003), user activity (P<.001), and daily usage time (P<.001) among medical students. In addition, there were 1086 clinical skills video views during this period compared to a total of 484 in the preceding months (ie, a 108% increase). On a 10-point Likert scale, students and physicians rated the app with mean scores of 8.2 (SD 1.9) for user experience, 8.1 (SD 2.0) for usefulness, and 8.5 (SD 1.8) for relevance of content. In parallel, postgraduate trainees viewed more than 6000 documents during the first 3 months after the implementation in the Division of Neurology at our institution. CONCLUSIONS HeadToToe is an educator-driven, mobile dissemination platform, which provides rapid and user-friendly access to up-to-date medical content and guidance. The platform was given high ratings for user experience, usefulness, and content quality and was used more often during the exam period. This suggests that the platform could be used as tool for exam preparation.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".