Leveraging Predictive AI and LLM-Powered Trial Matching to Improve Clinical Trial Recruitment: A Usability Assessment of Trialshub
Blankson, P.-K.; Hussien, S.; Idris, F.; Trevillion, G.; Aslam, A.; Afani, A.; Dunlap, P.; Chepkorir, J.; Melgarejo, P.; Idris, M.
Show abstract
BackgroundRecruitment remains a major barrier to timely clinical trial completion. Trialshub is an LLM-powered, chat-based platform intended to help users identify relevant trials and connect with coordinators to streamline recruitment workflows. ObjectiveTo evaluate the perceived usability and operational value of Trialshub, and identify implementation considerations for real-world deployment. MethodsA usability test was conducted at Morehouse School of Medicine for the Trialshub application. Purposively selected participants included clinical research coordinators and individuals with and without clinical trial search experience. Participants completed a pre-test survey assessing demographics, digital health information behaviors, and familiarity with AI tools, followed by a moderated usability session using a Trialshub prototype. Users completed scenario-based tasks (locating a breast cancer trial, reviewing results, and initiating coordinator contact) using a think-aloud protocol. Task ratings, screen recordings, and transcribed feedback were analyzed descriptively and thematically, and reported. ResultsParticipants reported high comfort with using digital tools and moderate-to-high familiarity with AI. Trialshubs chat-first design, guided prompts, and checklist-style eligibility display were perceived as intuitive and reduced cognitive load. Fast access to trials and the coordinator-contact workflow were viewed positively. Key usability issues included uncertainty at step transitions, insufficient cues for selecting results and next actions, and inconsistent system reliability (loading delays, errors, and broken trial detail pages). Participants also noted redundant questioning due to limited conversational memory, requested improved filtering/sorting, and clearer calls-to-action. All participants indicated that Trialshub has strong potential to meaningfully improve clinical trial processes. ConclusionsTrialshub shows promise for improving trial discovery and recruitment workflows, with identified design implications for real-world deployment.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Ethical review of clinical research with generative AI: Evaluating ChatGPT’s accuracy and reproducibility 94%
- Feasibility characteristics of wrist-worn fitness trackers in health status monitoring for post-COVID patients in remote and rural areas 93%
- Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study 93%
Similar papers in this journal
- Validating a Clinical Decision Support System for Palliative Care using healthcare professionals’ insights 95%
- A digital self-care intervention for Ugandan patients with heart failure and their clinicians: User-centred design and usability study 95%
- User Experience Evaluation of Cogscreen for Screening Mild Cognitive Impairment: Formative and Summative Evaluation 92%
Similar papers in this journal
- Improving Patient Engagement in Phase 2 Clinical Trials with a Trial-specific Patient Decision Aid (tPDA): A Development and Usability Study 98%
- Understanding how the design and implementation of Online Consultations influence primary care outcomes: Systematic review of evidence with recommendations for designers, providers, and researchers 94%
- Design and implementation of a system for automated monitoring of adherence to evidenced-based clinical guideline recommendations 93%
Similar papers in this journal
- Design and Formative Evaluation of a Voice-based Virtual Coach for Problem-Solving Treatment 95%
- Applications and barriers to use of an mHealth iPhone application for self-management of chronic recurrent medical conditions: A Pilot Study 94%
- A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.