A Framework for Diagnosing and Identifying Where Technology-Induced Errors Come From
Bibliographic record
Abstract
Health information systems have the ability to reduce medical errors but they can also introduce new types of errors. In the cognitive and human factors literature there is a recognition that many of the high profile accidents that have occurred in other industries outside of healthcare have had their origins in the complexities of organizational work and how work is structured. The authors propose that in order to have a fully robust framework for diagnosing technology-induced errors one must understand the development and implementation of a technology and the influences of policy using a multi-organizational model. The authors propose that technology-induced errors may have their origins in up to four or more organizational structures that make up complex health care systems in addition to the health care provider: governments, model organizations, software development organizations, and local healthcare organizations. In this paper a framework for considering the origins of technology-induced error in healthcare is presented, along with our experiences to date in the application of the framework.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.014 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".