Redesigning the replenishment process of medical supplies in hospitals with RFID
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a case study of a hospital nursing unit that has evaluated and approved a two‐bin “e‐kanban” replenishment system based on passive high frequency radio‐frequency identification (RFID) technology. Design/methodology/approach The case study analysis is based on both qualitative and quantitative data that were collected using semi‐structured interviews, on‐site observations and experience from previous implementations. The data and simulation analysis presented in this paper were validated by key respondents thereby increasing their reliability. Findings Results indicate that implementing the e‐kanban RFID solution in conjunction with the redesign of the ward floor and of the roles and functions can substantially improve business and operational performance. The most important benefits for the hospital are derived from the time saved from non‐value‐added activities that can be transferred to patient care activities and the significant reduction of on‐hand inventory at distributed storage locations. The solution is considered an alternative that requires less initial investment than RFID‐enabled cabinets used in the replenishment of consignment and high‐value supplies in operating rooms and cardiac catheterization laboratories. Research limitations/implications There is a need to conduct further research on RFID supply chain management (SCM) applications in the healthcare sector as this area holds a great potential for performance improvements. Additionally, there is a need to conduct more in‐depth research into the isolated impact of RFID technology in comparison to the change management and process redesign that it generates. One key limitation of this research is the case study approach based on a single case. This paper, therefore provides direction for practitioners on how to assess RFID's potential impact in the healthcare supply chain. Originality/value While most of the research on RFID in healthcare sector focuses on active RFID technology for asset management, this research presents a novel RFID application and contributes to our understanding of RFID's potential in intra‐organizational SCM 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".