Improving logistics processes of surgical instruments: case of RFID technology
Bibliographic record
Abstract
Purpose Since mid-2000s, hospitals have begun implementing radio-frequency identification (RFID) technology in order to improve their operations. The purpose of this paper is to explore the potential of RFID technology in improving the traceability of surgical instruments in a hospital environment. Design/methodology/approach A case study is conducted at a teaching hospital in Montreal, Canada. Business process reengineering approach and simulation techniques are used to assess the realistic potential of the RFID technology. The application of different scenarios and how they influence the efficiency of process flow between the central sterilization department and operating rooms of the hospital is investigated. Findings Research outcomes demonstrated how tagging individual instruments or their sets lead to reduction of the time spent in re-processing the soiled instrument as well as the reduction of costs related to staff. Furthermore, specific key performance indicators are identified and eventual issues related to implementation of the re-designed processes are discussed. Originality/value Implementing RFID-enabled solutions in hospital context is still an emerging phenomenon that involves various stakeholders in a change management project. While implementing RFID technology can benefit hospitals by improving business processes and workflows, the adoption is still slow, especially for managing surgical instruments. It is, hence, crucial to compare the advantages and drawbacks of RFID-enabled surgical instruments solutions with other well-established traceability technologies such as barcoding.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".