Simulations to AssessMedication Administration Systems
Bibliographic record
Abstract
A range of new technologies/information systems are increasingly being used by nurses in healthcare organizations. These technologies/information systems are being implemented in clinical settings in order to reduce errors associated with the medication administration process. At present, few published articles report empirical findings on the effects of using electronic medication administration systems where nurses’ work is concerned. This chapter reports that there are both intended and unintended consequences, associated with implementing such systems in the clinical setting. Simulation methods can be used to assess the impact of integrating new technology/information systems into the nurses’ work environment prior to full-scale implementation of a health technology/information system. Simulations as an evaluative tool emerged from a direct need to assess unintended and intended consequences of health information systems upon nurses’ work before systems are fully implemented. Simulations, as an evaluative methodology, emerged from the cognitive-socio-technical literature. Methods for analyzing workflow and conducting simulations of user behavior can be used to assess and improve the cognitive-socio-technical fit of technologies/information systems. They can be used to identify work and workflow problems (i.e. unintended consequences) involving health technologies/information systems as they are designed, developed, procured, purchased and implemented. Nurse informatician use of simulations to assess and test health technologies/information systems will allow nurses to determine the impact of a new software and/or hardware upon aspects of nurses’ work before its implementation to allow for appropriate system modifications.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.010 | 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".