Implementation and Use of Real-Time Location Systems in Hospital Environments – Rapid Scoping Review of Human Factors Considerations
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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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 | 0.033 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".