Leveraging on RFID-IoT Technologies and Simulation to Design and Develop a Smart Shelf for Managing Low Value Medical Supplies
Bibliographic record
Abstract
In this collaborative research project between one of the largest hospitals in the province of Quebec and university researchers, we are exploring the use of Radio Frequency Identification-Internet of things (RFID-IoT) technologies to improve the management of medical supplies in point of care units. While most of the research on RFID-IoT in the healthcare sector focuses on mobile device management and patient monitoring, this research contributes to our understanding of the potential of RFID-IoT technologies to improve (internal) hospital's logistics processes for low value medical supplies associated with patient care activities. The first objective is to contribute to the development of a "smart" shelf prototype that will combine different technologies including passive RFID technology and indicative LED. Since testing realistic impacts of such solutions on operational performance is not easy, the second objective is to use a hybrid simulation approach to evaluate the impact of an loT 2bin Kanban "smart" shelf replenishment system for medical supplies that can be used in a hospital nursing unit. The simulation approach facilitates the selection of the "best" replenishment system. The originality of this project lies (a) in the open innovation approach which is of great value in the healthcare context (b) in the combination of physical prototyping of an RFID-IoT smart shelf and simulation, which uses real operations data to explore the impact of the solution on business and operational processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".