Abstract 18508: Usability of Novel Automated Software for Guiding Remote Titration of Antiarrhythmic Drugs

Abstract

Introduction: We tested the first novel software application to accurately measure QTc using a machine learning algorithm from a mobile ECG and recommend patient-specific antiarrhythmic drug (AAD) dosing. The software was developed to address disparities in AAD hospitalizations that disproportionately affect minority patients. We evaluated the usability of the software interface using the validated Post-Study System Usability Questionnaire (PSSUQ) and mobile Health App Usability Questionnaire (MAUQ). Methods: Ten medical providers, 16 clinic staff, and 16 patients tested the novel software interface for remote titration of Sotalol and Dofetilide medications, followed by the validated PSSUQ and MAUQ surveys. An observer also evaluated each step of the software for completion and noted any challenges. Results: Providers rated the software interface highly, with an overall mean PSSUQ of 6.9 ± 0.2 and MAUQ of 6.8 ± 0.2, and 100% strongly agree on information quality (PSSUQ questions 7-12). Clinic staff and patients provided the highest ratings for system usefulness, with an overall PSSUQ mean of 6.4 ± 1.2 and MAUQ mean of 6.0 ± 1.2, ease of app use and user satisfaction, mean of 6.3 ± 1.1 and 5.9 ± 1.3 respectively. Eighty percent of users found the system easy to use and were satisfied, with 76% rating the app as an acceptable way to receive healthcare services and felt comfortable communicating with their providers using the app. Conclusion: The novel automated software application, designed to improve access to antiarrhythmic drugs and reduce healthcare disparities, received high usability ratings, indicating feasibility for remote administration of antiarrhythmic drugs.