Real-Time, Location-Based Patient-Device Association Management
Bibliographic record
Abstract
Background: Hospitals need to accurately manage mobile devices (e.g., intravenous pumps) associated to their patients and health providers to ensure patient safety. Some hospitals have already invested substantially in real-time location system (RTLS) technology, a specific type of Internet of Things (IoT) application for indoor positioning, to manage mobile clinical devices. Objective: This paper investigates the reuse of RTLS systems to monitor patients and their assigned devices and to manage their connectivity automatically, in real time. Method: A system called Real-time Patient-Device Association and Disassociation (RPDAD) is designed, implemented, and tested in a hospital room and in a university laboratory. Results: RPDAD helps manage patient-device associations through a tablet application, with accurate suggestions for closest devices and automated detection of unexpected disassociations, resulting in real-time alerts. Conclusion: RPDAD offers a usable means of managing associations that does not depend on bar-coding technologies. It also helps amortize investments in RTLS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".