Improving the User Interface and Guiding the Development of Effective Training Material for a Clinical Research Recruitment and Retention Dashboard: Usability Testing Study
Bibliographic record
Abstract
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.
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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 | 0.037 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".