The human-computer user interface and patient safety: Introducing new technologies in healthcare effectively and safely
Bibliographic record
Abstract
Health information technology (HIT) promises to modernize healthcare, lead to efficiencies and to reduce medical error. However, the literature has shown that if HIT is not designed and tested properly, systems such as electronic health records (EHRs) and personal health records (PHRs) may actually inadvertently introduce new types of errors that are termed technology-induced errors. Such error may result from the complex interplay of health professional, patients and the HIT deployed in the healthcare system and currently they may not be easily detectable until systems are deployed. In order to deal with this, in this paper we describe a framework for detecting and rectifying technology-induced errors before they are propagated in the healthcare system and for implementing systems in a safe and effective manner. It is argued that in order to do this, it will require a layered approach to system design and testing involving application of usability testing methods along with application of clinical simulations. The paper describes a promising approach that involves multiple methods for ensuring system safety in healthcare.
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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".