Technology-induced errors: where do they come from and what can we do about them?
Bibliographic record
Abstract
The introduction of health information technology (HIT) has been associated with a decrease in medical error and this has been one of the main reasons for international efforts at increasing adoption of systems such as electronic health records, computerized physician order entry and clinical decision support systems. However, in recent years there is growing evidence that if not designed and tested properly such HIT can also lead to new categories of errors that were previously unseen in healthcare. These errors are known as technology-induced errors and they typically manifest themselves in the complex interaction between healthcare providers and HIT during real clinical use. In this paper the author explores the concept of technology-induced error in healthcare and discusses a range of strategies for detecting and mitigating such errors. Strategies include creating new organizations whose focus is to reduce technology-induced errors, develop and deploy new ways to detect such errors before systems are released, as well as approaches to reporting such errors after they occur. Other strategies include the development of regulation and policy to reduce such errors. It is argued that a multi-faceted approach to dealing with technology-induced error is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".