Assessment of the Reliability of Active Radiofrequency Identification Technology for Patient Tracking in the Pediatric Emergency Department
Bibliographic record
Abstract
INTRODUCTION: Radiofrequency identification (RFID) technology has been used in other emergency department settings but has not been assessed in a pediatric emergency department setting for its reliability in its application as a patient tracking system. The goal of this study was to assess the accuracy, precision, and reliability of the technology in a simulated pediatric emergency department setting to collect patient tracking information. METHODS: A simulated pediatric emergency department clinical assessment room was developed to serve as a test room to collect patient tracking information. This information included the interaction times between simulated patients, parents, physicians, and nurses. Direct observation of these interaction times were recorded by an observer. A patient tracking system based on active RFID technology recorded interaction times between models wearing RFID devices and recorded this information in a computerized data log. Comparison between the direct observation record and the data log was used to determine accuracy, precision, and reliability. RESULTS: A total of 152 directly observed interactions were recorded. Data extraction from the data log yielded 152 sensor-recorded interactions, resulting in a reliability of 1.0. Data pair comparison on all events resulted in a mean difference of 2.88 seconds. CONCLUSIONS: Active RFID-based patient tracking systems are a precise and reliable means of recording patient interaction events in the pediatric emergency department.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".