Abstract Body (Do not enter title and authors here): Artificial Intelligence (AI)-driven ECG methods are transforming clinical workflows, signaling a shift in ECG analysis research and clinical practice. Adoption in routine care remains limited—largely due to challenges with explainability and poor integration into existing workflows. Combining human expertise with AI has potential to enhance research translation to improve clinical decision-making and patient outcomes. We applied human-computer interaction principles to develop ECG-SMART, a clinician-facing AI dashboard designed to estimate the likelihood of occlusion myocardial infarction (OMI) while intuitively communicating the model’s reasoning.
We aimed to refine an ECG-SMART dashboard based on feedback from practicing clinicians through a series of qualitative focus groups (FG) and clinical usability sessions.
Guided by user-centered design principles, we conducted 2 rounds of virtual FGs moderated by a FG methodologist. We asked open-ended questions to elicit feedback from clinical end-users on the scope and presentation of information provided by the dashboard prototype. Round 1 consisted of 3 separate FG sessions (paramedics, ED physicians, and cardiologists) who reviewed the prototype. Feedback was used to derive a modified version of the dashboard. Round 2 combined FGs with all available clinicians who participated in Round 1 to further refine the dashboard. All FGs were recorded and transcribed. Transcripts were coded by 3 research team members to identify recurrent patterns until thematic saturation was reached. After resolution of coding discrepancies, codes coalesced into emerging themes resulting in clinician-recommended dashboard changes.
Twelve clinicians participated (mean age = 40 years, 25% female, mean clinical experience = 14 years). Five themes emerged: interpretation and clinical context, trust and explainability, AI recommendations and labels, risk stratification and AI scoring, and workflow and dashboard design. Dashboard design changes included, simplified color coding, improved OMI risk score display, enhanced AI prediction explainability and functionality, access to historical 12-lead ECGs, and revision of clinical action recommendation language. Figure 1 details the dashboard evolution.
Engaging multidisciplinary clinicians in the design and refinement of an intelligent AI-ECG dashboard bridges the gap between AI developers and target end-users, improving clinical utility and acceptability for widespread clinical implementation.