Where do Technology-Induced Errors Come From? Towards a Model for Conceptualizing and Diagnosing Errors Caused by Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Borycki, Elizabeth M.; Kushniruk, Andre W. Health information technology has the potential to greatly improve healthcare delivery. Indeed, in recent years many have argued that introduction of information technology will be essential in order to decrease medical error and increase healthcare safety. In this chapter we review some of the evidence that has accumulated indicating the positive benefits of health information technology for improving safety in healthcare. However, a number of recent studies have indicated that if systems are not designed and implemented properly health information technology may actual inadvertently result in new types of medical errors—technology-induced errors. In this chapter we discuss where such error may arise and propose a model for conceptualizing and diagnosing technology-induced error so that the benefits of technology can be achieved while the likelihood of the occurrence of technology-induced medical error is reduced.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| 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 it