Ensuring the Safety of Health Information Systems: Using Heuristics for Patient Safety
Bibliographic record
Abstract
Health information systems (HISs) are typically seen as a mechanism for reducing medical errors. However, there is evidence to suggest that technology can facilitate or induce medical errors. Therefore, it is crucial that we fully test systems prior to their implementation in real-world settings. Presently, evidence-based evaluation heuristics that are specific to HISs do not exist for assessing aspects of interface design that may facilitate errors. A three-phase study was conducted to determine the utility of evidence-based heuristics in evaluating a human-technology interface (i.e., the Veterans Affairs Computerized Patient Record System [VA CPRS]). Phase one consisted of a systematic review of the health informatics literature involving technology-facilitated or technology-induced error. Phase two involved reviewing the literature and generating a comprehensive list of 38 heuristics that could be used to evaluate an HIS for technology-induced errors. Lastly, phase three involved conducting a heuristic evaluation of the VA CPRS system using evidence-based heuristics. Results from this work are discussed.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".