Abstract 18508: Usability of Novel Automated Software for Guiding Remote Titration of Antiarrhythmic Drugs
Bibliographic record
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.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".