Implementation and Use of Real-Time Location Systems in Hospital Environments – Rapid Scoping Review of Human Factors Considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-Time Location Systems (RTLS) are rapidly being adopted in healthcare environments to monitor or track patients, workers, equipment, supplies and more, with numerous studies discussing the impact of this emerging technology. However, a more global view of such studies, those focused on human factors considerations in implementing RTLS is lacking. In response, we present preliminary findings from a rapid scoping review of factors related to implementing RTLS in healthcare environments, with a specific focus on impact to healthcare workers. We conducted keyword searches of databases such as CINAHL, EI Engineering Village, Scopus, IEEE, Web of Science, and several ProQuest journals between July and September 2022. The results were screened to identify results pertaining specifically to the experience of implementing RTLS in recent years. The extracted studies offer insight into the human factors that affect the implementation and use of this technology. Namely, the literature points to undesirable outcomes that occur when organizational efficiency is emphasized over providing demonstrable benefits to workers. Conversely, successful implementations are shown to feature increased worker involvement in the design process and increased communication and training following implementation. Further, the reviewed literature supports the involvement of human factors practitioners in future research activities investigating RTLS implementation and its impact on hospital infrastructure and operations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it