Linking Activity Theory Within User-Centered Design: Novel Framework to Inform Design and Evaluation of Adverse Drug Reaction Reporting Systems in Pharmacy
Bibliographic record
Abstract
BACKGROUND: Adverse drug reactions (ADRs) may cause serious injuries including death. Timely reporting of ADRs may play a significant role in patient safety; however, underreporting exists. Enhancing the electronic communication of ADR information to regulators and between health care providers has the potential to reduce recurrent ADRs and improve patient safety. OBJECTIVE: The main objectives were to explore the low rate of ADR reporting by community pharmacists (CPs) in Australia, evaluate the usability of an existing reporting system, and how this knowledge may influence the design of subsequent electronic ADR reporting systems. METHODS: The study was carried out in 2 stages. Stage 1 involved qualitative semistructured interviews to identify CPs' perceived barriers and facilitators to ADR reporting. Data were analyzed by thematic analysis, and identified themes were subsequently aligned to the task-technology fit (TTF) framework. The second stage involved a usability evaluation of a commercial web-based ADR reporting system. A structured interview protocol that combined virtual observation, think-aloud moderating techniques, retrospective questioning of the overall user experience, and a System Usability Scale (SUS). The field notes from the interviews were subjected to thematic analysis. RESULTS: In total, 12 CPs were interviewed in stage 1, and 7 CPs participated in stage 2. The interview findings show that CPs are willing to report ADRs but face barriers from environmental, organizational, and IT infrastructures. Increasing ADR awareness, improving workplace practices, and implementing user-focused electronic reporting systems were seen as facilitators of ADR reporting. User testing of an existing system resulted in above average usability (SUS 68.57); however, functional and user interpretation issues were identified. Design elements such as a drop-down menu, free-text entry, checkbox, and prefilled data fields were perceived to be extremely useful for navigating the system and facilitating ADR reporting. CONCLUSIONS: Existing reporting systems are not suited to report ADRs, or adapted to workflow, and are rarely used by CPs. Our study uncovered important contextual information for the design of future ADR reporting interventions. Based on our study, a multifaceted, theory-guided, user-centered, and best practice approach to design, implementation, and evaluation may be critical for the successful adoption of ADR reporting electronic interventions and patient safety. Future studies are needed to evaluate the effectiveness of theory-driven frameworks used in the design and implementation of ADR reporting systems.
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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 | 0.111 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.004 |
| 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".