Evaluating the Prototype of a Clinical Decision Support System in Primary Care: Qualitative Study
Notice bibliographique
Résumé
Background: General practitioners are confronted with a wide variety of diseases and sometimes diagnostic uncertainty. Clinical decision support systems could be valuable to improve diagnosis, but existing tools are not adapted to the requirements and workflow in the primary setting. In project SATURN (Smart physician portal for patients with unclear disease), the prototype of a clinical decision support system based on artificial intelligence is being developed together with users specifically for primary care in Germany. It aims to reduce diagnostic uncertainty in cases of unclear and rare diseases and focuses on 3 medical fields. A user-centered design approach is applied for prototype development and evaluation. Objective: This study evaluates the usability of a high-fidelity prototype and explores aspects of user experience like the subjective impression, satisfaction, and areas of improvement. Methods: A total of 5 general practitioners participated in the evaluation, which consisted of (1) a remote think-aloud test, (2) a postsession interview, and (3) a survey with the System Usability Scale. During the think-aloud tests, the participants verbalized their thoughts and actions and solved several vignette-based tasks. Remarkable observations were logged, transcribed with quotes, and analyzed for usability problems and positive findings. All observations and interview responses were deductively assigned to the following categories: (1) content, (2) comprehensibility, (3) user-friendliness, (4) layout, (5) feedback, and (6) navigation. Usability problems were described in detail and solutions for improvement proposed. Median and total scores were calculated for all questionnaire items. Results: The evaluation detected both strengths and areas for improvement. The participants particularly liked the clear and well-structured layout of the prototype. Key issues identified were content-related limitations, such as the inability to enter unlisted symptoms, medications, and examination findings. Also, participants found the terminology for laboratory not suitable to their needs. Another key issue was a lack of user-friendliness concerning the time required to input medication plans and lab values. Participants expressed a need for faster data entry, potentially through direct imports from practice management systems or laboratory files. Adding symptom duration, weighting symptoms, and incorporating hereditary factors were suggestions made for improvement. Overall, the SATURN prototype was deemed useful and promising for future clinical use, despite the need for further refinements, particularly in the areas of data entry, as this is a key obstacle to its use. Conclusions: The usability evaluation methods combined proved to be location independent and easy to use. They provided important findings on usability issues and improvements that will be implemented in a second high-fidelity prototype, which will also be tested by users. Technically demanding user requirements, such as direct data transfer from the practice management system and entry options that require complex data models, were beyond the scope of this project, but should be considered in future development projects.
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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 | 0,035 | 0,055 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».