Exploring the Use of Persuasive System Design Principles to Enhance Medication Incident Reporting and Learning Systems: Scoping Reviews and Persuasive Design Assessment
Bibliographic record
Abstract
BACKGROUND: Medication incidents (MIs) causing harm to patients have far-reaching consequences for patients, pharmacists, public health, business practice, and governance policy. Medication Incident Reporting and Learning Systems (MIRLS) have been implemented to mitigate such incidents and promote continuous quality improvement in community pharmacies in Canada. They aim to collect and analyze MIs for the implementation of incident preventive strategies to increase safety in community pharmacy practice. However, this goal remains inhibited owing to the persistent barriers that pharmacies face when using these systems. OBJECTIVE: This study aims to investigate the harms caused by medication incidents and technological barriers to reporting and identify opportunities to incorporate persuasive design strategies in MIRLS to motivate reporting. METHODS: We conducted 2 scoping reviews to provide insights on the relationship between medication errors and patient harm and the information system-based barriers militating against reporting. Seven databases were searched in each scoping review, including PubMed, Public Health Database, ProQuest, Scopus, ACM Library, Global Health, and Google Scholar. Next, we analyzed one of the most widely used MIRLS in Canada using the Persuasive System Design (PSD) taxonomy-a framework for analyzing, designing, and evaluating persuasive systems. This framework applies behavioral theories from social psychology in the design of technology-based systems to motivate behavior change. Independent assessors familiar with MIRLS reported the degree of persuasion built into the system using the 4 categories of PSD strategies: primary task, dialogue, social, and credibility support. RESULTS: Overall, 17 articles were included in the first scoping review, and 1 article was included in the second scoping review. In the first review, significant or serious harm was the most frequent harm (11/17, 65%), followed by death or fatal harm (7/17, 41%). In the second review, the authors found that iterative design could improve the usability of an MIRLS; however, data security and validation of reports remained an issue to be addressed. Regarding the MIRLS that we assessed, participants considered most of the primary task, dialogue, and credibility support strategies in the PSD taxonomy as important and useful; however, they were not comfortable with some of the social strategies such as cooperation. We found that the assessed system supported a number of persuasive strategies from the PSD taxonomy; however, we identified additional strategies such as tunneling, simulation, suggestion, praise, reward, reminder, authority, and verifiability that could further enhance the perceived persuasiveness and value of the system. CONCLUSIONS: MIRLS, equipped with persuasive features, can become powerful motivational tools to promote safer medication practices in community pharmacies. They have the potential to highlight the value of MI reporting and increase the readiness of pharmacists to report incidents. The proposed persuasive design guidelines can help system developers and community pharmacy managers realize more effective MIRLS.
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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 | 0.071 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.027 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".