Using Clinical and Computer Simulations to Reason About the Impact of Context on System Safety and Technology-Induced Error
Bibliographic record
Abstract
This paper describes how simulations can be used to reason about the impact of user interface design features in exploring the effect of different contexts of use on the occurrence of technology-induced errors. The paper describes our approach in several phases, using an example from the analysis of technology-induced errors in medication administration. In the initial phase a clinical simulation is conducted to gather baseline data on the occurrence of technology-induced error using the technology under study. In this phase of the study, data arising from the clinical simulation are collected and then analyzed using qualitative and quantitative approaches to assess the relationship between aspects of interface design (i.e. usability problems) and rates of technology-induced error. In the next phase, the base rates for error associated with specific types of usability problems (from the initial phase) form the input into computer-based mathematical simulations. This approach links clinical simulations with computer-based simulations and demonstrates the potential impact of aspects of interface design and contextual factors upon medical error along with the implications for correcting interface design issues.
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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 | 0.011 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".