Development of Open Backend Structures for Health Care Professionals to Improve Participation in App Developments: Pilot Usability Study of a Medical App
Notice bibliographique
Résumé
BACKGROUND: Efficient digitization in medicine still is in its infancy but undeniably has great potential for current and future challenges in health care. Thus far, the rollout of medical apps has not resulted in widespread use of smartphones in the German health care sector-the reasons for this have not been clarified so far. Nevertheless, the lack of user involvement in the development process and content creation might contribute to low acceptance of these products. OBJECTIVE: This study aims to outline an approach to involve medical expertise without any coding knowledge for developing medical app content and functions. METHODS: An end user-operable backend was built. Its usability was evaluated using a usability evaluation test protocol. The results of the usability tests were evaluated by the app development team, and the usability test was repeated for optimizing backend usability. In total, 40 criteria to measure the ease of app usage were defined a priori. The usability test comprised 20 tasks that had to be fulfilled. Usability tasks were analyzed for completion, dropout, and test duration. Due to the COVID-19 pandemic, digital videoconferencing platforms (Zoom and QuickTime Player) were used to complete usability questionnaires. Finally, several backend-based apps for several specialties (infectiology, plastic and reconstructive surgery, and orthopedics) were developed by health care professionals as prototypes. RESULTS: Initial usability testing was conducted with 5 participants (4 men and 1 woman; mean age 39.2, SD 5.97 years). All of them could complete the assigned backend tasks with only a few workflow interruptions and some minor errors. After usability optimization, the workflow completion time decreased from 5.03 minutes to 3.50 minutes, indicating a time saving. The basic backend structure was clear to all test users and the handling was intuitive to learn. Some minor errors in the backend occurred during the test rounds. The apps developed using the aforementioned approach are in clinical use as a proof of concept. CONCLUSIONS: Backends offering operability for medical professionals might have great potential for app development in the mobile health sector. Sophisticated and time-saving usability are pivotal for the acceptance of medical software, as illustrated by the backend-based apps presented herein, which are in clinical use as a proof of concept. Basic interventions are essential and sufficient for adequate usability optimization. Practicable, well-structured software usability evaluation is possible based on the usability evaluation test protocol.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».