Use of Clinical Simulations to Evaluate the Impact of Health Information Systems and Ubiquitous Computing Devices Upon Health Professional Work
Bibliographic record
Abstract
Health information systems, and in particular ubiquitous computing devices (UCD), promise to revolutionize healthcare. However, before this can be widely achieved UCD need to be adapted to fit the information, workflow and cognitive needs of users of such devices. Indeed systems and devices that are not developed appropriately may inadvertently introduce error in healthcare (“technology-induced error”). This chapter describes an approach to applying clinical simulations to evaluate the impact of health information systems and ubiquitous computing devices on health professional work. The approach allows for an assessment of “cognitive-socio-technical fit” and the ability to modify and improve systems and devices before they are released into widespread use. The application of realistic clinical simulations is detailed, including the stages of development of such simulations (from the creation of representative clinical environments to subject selection and data collection approaches). In order to ensure the success and widespread adoption of UCD, it is argued that greater emphasis will need to be placed on ensuring such systems and devices have a high degree of fit with user’s cognitive and work processes.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".