Twenty Years of Cognitive Work Analysis in Health Care
Bibliographic record
Abstract
Improving patient safety, within the context of a complex system, forms one of the key challenges in health care today. Cognitive work analysis (CWA) is one way to analyze complex systems, and although it has been applied to health care for 20 years, little is known about its effectiveness or future research needs. This article presents a review of CWA studies in health care, addressing questions of use, usefulness, challenges, and opportunities. Results of the review make clear that the research agenda is largely confined to acute care. Of the 39 articles reviewed, 28 relate to this setting. There appears to be a growing interest in medical informatics, error investigation, and decision support. Conversely, work in physiological monitoring has slowed, associated with the uncertainties of modeling “biological” systems. Studies related to “organic” social systems are similarly challenged, although there is a recognition that important opportunities exist, such as studying work flow processes between teams. Other opportunities relate to new methods to enhance CWA; new technologies, such as auditory displays; and new applications, such as requests for proposals and incident investigation. Ultimately, the capacity to foster an understanding into the deep structures of a system may prove to be the greatest contribution of CWA to health care today.
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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| 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".