RFID-enabled Real-Time Location System (RTLS) to improve hospital's operations management: An up-to-date typology
Bibliographic record
Abstract
Recent deployments of Real-Time Location Systems (RTLS) around the world illustrate a key trend in Radio Frequency Identification (RFID) technologies supporting innovative applications within numerous industrial sectors. Managers in charge of implementing RTLS face several challenges when (a) selecting the right active/passive RFID system for their needs; (b) implementing and integrating the system; and (c) leveraging on it, to move from automated object identification to information management and decision-making. Although some information is available to support researchers and practitioners, the existing documentation often focuses on specific aspects of the technology, and much of the information found in the professional literature is not vendor-neutral, resulting in some confusion for decision-makers. This paper clarifies the different technological options presently available on the market and proposes up-to-date typologies for RTLS hardware and software solutions used in hospitals. Findings can help potential adopters select an RTLS that meets their specific needs while highlighting the critical steps and pitfalls at the front end phases of a RTLS implementation project.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".