Combining two forms of simulation to predict the potential impact of interface design on technology-induced error in healthcare
Bibliographic record
Abstract
This paper will describe how simulations of clinical activity (involving human subjects carrying out clinical tasks) and mathematical computer-based simulations can be linked to forecast the impact of interface design features upon medical errors in healthcare information technology. The paper describes our approach in two phases. In Phase 1 a clinical simulation was conducted involving 10 physicians who were asked to use a hand-held prescription writing application to enter and record medications administered during a simulated clinical interaction. In this phase of the study data arising from the clinical simulation was collected and then analyzed using qualitative approaches to assess the relationship between aspects of interface design (i.e. usability problems) and medication error prescribing rates. In Phase 2, the base rates for error associated with specific types of usability problems (from Phase 1) formed the input into a computer-based mathematical simulation. Using this approach, comparative graphs of total mistakes and slips from Phase 1 were forecasted over time. The work described in this paper is unique in health care as it directly connects two distinct forms of simulations: (1) clinical simulations of user behavior and (2) mathematical simulation to forecast error rates over time. This approach links clinical simulations with computer simulations and demonstrates the impact of aspects of interface design upon medical error.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".