Preventing Technology-Induced Errors in Healthcare: The Role of Simulation
Bibliographic record
Abstract
We describe a novel approach to the study and prediction of technology-induced error in healthcare. The objective of our approach is to identify and reduce the potential for error so that the benefits of introducing information technology, such as Computerized Physician Order Entry (CPOE) or Electronic Health Records (EHRs), are maximized. The approach involves four phases. In Phase 1, we typically conduct small scale clinical simulations to assess whether or not the use of a new information technology can introduce error. (Human subjects are involved and user-system interactions are recorded.) In Phase 2, we analyze the results from Phase 1 to identify statistically significant relationships between usability issues and the occurrence of error (e.g., medication error). In Phase 3, we enter the results from Phase 2 into computer-based simulation models to explore the potential impact of the technology over time and across user populations. In Phase 4, we conduct naturalistic studies to examine whether or not the predictions made in Phases 2 and 3 apply to the real world. In closing, we discuss how the approach can be used to increase the safety of health information 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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".