Toward an Integrated Simulation Approach for Predicting and Preventing Technology-Induced Errors in Healthcare: Implications for Healthcare Decision-Makers
Bibliographic record
Abstract
Research has indicated that health information technology has the potential to reduce medical error and the chances of an adverse event occurring. However, research has indicated that poorly designed systems may inadvertently lead to error (technology-induced error). In this paper, we describe our most recent work in developing a new framework for the integration of multiple forms of simulation to ensure that systems are safe by predicting and preventing technology-induced error in healthcare. The approach taken involves the integration of "clinical simulations" with computer-based simulations. In a case study, the combination of clinical simulations (i.e., involving video analysis of health professionals interacting with computerized physician order entry) and the use of computer modelling and simulation tools is described. In our work, we first employ clinical simulation to obtain baseline error rates. Next, we input data from the clinical simulations into a computer-based simulation and modelling tool to assess the impact of specific aspects of system and interface design upon error rates. The practical implications of combining the advantages of clinical simulation with "in the box" computer-based simulation to predict the impact of healthcare information systems (HIS) are discussed. Implications of this work for healthcare institutions and policy decision-making are explored.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".