Reducing Technology-Induced Errors: Organizational and Health Systems Approaches
Bibliographic record
Abstract
Technology-induced errors are a growing concern for health care organizations. Such errors arise from the interaction between healthcare and information technology deployed in complex settings and contexts. As the number of health information technologies that are used to provide patient care rises so will the need to develop ways to improve the quality and safety of the technology that we use. The objective of the panel is to describe varying approaches to improving software safety from and organizational and health systems perspective. We define what a technology-induced error is. Then, we discuss how software design and testing can be used to improve health information technologies. This discussion is followed by work in the area of monitoring and reporting at a health district and national level. Lastly, we draw on the quality, safety and resilience literature. The target audience for this work are nursing and health informatics researchers, practitioners, administrators, policy makers and students.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".