A Web-Based Tool to Report Adverse Drug Reactions by Community Pharmacists in Australia: Usability Testing Study
Bibliographic record
Abstract
BACKGROUND: Adverse drug reactions (ADRs) are unintended and harmful events associated with medication use. Despite their significance in postmarketing surveillance, quality improvement, and drug safety research, ADRs are vastly underreported. Enhanced digital-based communication of ADR information to regulators and among care providers could significantly improve patient safety. OBJECTIVE: This paper presents a usability evaluation of the commercially available GuildCare Adverse Event Recording system, a web-based ADR reporting system widely used by community pharmacists (CPs) in Australia. METHODS: We developed a structured interview protocol encompassing remote observation, think-aloud moderating techniques, and retrospective questioning to gauge the overall user experience, complemented by the System Usability Scale (SUS) assessment. Thematic analysis was used to analyze field notes from the interviews. RESULTS: A total of 7 CPs participated in the study, who perceived the system to have above-average usability (SUS score of 68.57). Nonetheless, the structured approach to usability testing unveiled specific functional and user interpretation issues, such as unnecessary information, lack of system clarity, and redundant data fields-critical insights not captured by the SUS results. Design elements like drop-down menus, free-text entry, checkboxes, and prefilled or auto-populated data fields were perceived as useful for enhancing system navigation and facilitating ADR reporting. CONCLUSIONS: The user-centric design of technology solutions, like the one discussed herein, is crucial to meeting CPs' information needs and ensuring effective ADR reporting. Developers should adopt a structured approach to usability testing during the developmental phase to address identified issues comprehensively. Such a methodological approach may promote the adoption of ADR reporting systems by CPs and ultimately enhance patient safety.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".