Improving the User Interface and Guiding the Development of Effective Training Material for a Clinical Research Recruitment and Retention Dashboard: Usability Testing Study
Notice bibliographique
Résumé
Background: Participant recruitment and retention are critical to the success of clinical trials, yet challenges such as low enrollment rates and high attrition remain ongoing obstacles. RecruitGPS is a scalable dashboard with integrated control charts to address these issues by providing real-time data monitoring and analysis, enabling researchers to better track and improve recruitment and retention. Objective: This study aims to identify the challenges and inefficiencies users encounter when interacting with the RecruitGPS dashboard. By identifying these issues, the study aims to inform strategies for improving the dashboard's user interface and create targeted, effective instructional materials that address user needs. Methods: Twelve clinical researchers from the Midwest region of the United States provided feedback through a 10-minute, video-recorded usability test session, during which participants were instructed to explore the various tabs of the dashboard, identify challenges, and note features that worked well while thinking aloud. Following the video session, participants took a survey on which they answered System Usability Scale (SUS) questions, ease of navigation questions, and a Net Promoter Score (NPS) question. Results: A quantitative analysis of survey responses revealed an average SUS score of 61.46 (SD 23.80; median 66.25) points, indicating a need for improvement in the user interface. The NPS was 8, with 4 of 12 (33%) respondents classified as promoters and 3 of 12 (25%) as detractors, indicating a slightly positive satisfaction. When participants compared RecruitGPS to other recruitment and study management tools they had used, 8 of 12 (67%) of participants rated RecruitGPS as better or much better. Only 1 of 12 (8%) participants rated RecruitGPS as worse but not much worse. A qualitative analysis of participants' interactions with the dashboard diagnosed a confusing part of the dashboard that could be eliminated or made optional and provided valuable insight for the development of instructional videos and documentation. Participants liked the dashboard's data visualization capabilities, including intuitive graphs and trend tracking; progress indicators, such as color-coded status indicators and comparison metrics; and the overall dashboard's layout and design, which consolidated relevant data on a single page. Users also valued the accuracy and real-time updates of data, especially the integration with external sources like Research Electronic Data Capture (REDCap). Conclusions: RecruitGPS demonstrates significant potential to improve the efficiency of clinical trials by providing researchers with real-time insights into participant recruitment and retention. This study offers valuable recommendations for targeted refinements to enhance the user experience and maximize the dashboard's effectiveness. Additionally, it highlights navigation challenges that can be addressed through the development of clear and focused instructional videos.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,037 | 0,098 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».