RFID Modeling in Healthcare
Bibliographic record
Abstract
Increasingly, healthcare management systems include investment in and implementation of technology to track the status and movement of various entities within the healthcare environment, including patients, healthcare workers and physical assets. This is often a means of understanding patient flow, controlling inventory, tracking equipment usage, and thereby (ideally) assessing efficiencies in order to optimize resources and processes within that environment (Wang et al., 2006). The focus of this chapter is to highlight the contributions of RFID systems modeling, particularly in relation to an understanding of the nature and extent of system error that is often overlooked experientially. The healthcare environment was chosen as it is an increasingly complex and interesting application area for RFID, and in which a wide range of RFID-based applications and devices already exist and can be envisioned for the future. The insights gained through modeling provide a complementary set of data to those gained from the experiential knowledge of performance in existing installations. To that end, this chapter focuses on a case study of an agent-based model (ABM) of a hospital emergency department (ED), with extensions to modeling the provisioning of a real-time location system (RTLS) using RFID for patient tracking. To contextualize this work, Section 2 reviews conventional and emerging RFID applications in healthcare. Section 3 introduces the agent based modeling technique, invoked to investigate system performance in an application for RFID-enabled patient tracking within an ED. The ABM was developed as a decision support tool oriented towards optimizing RFID placement (minimizing uncertainty) for an actual ED where healthcare managers are considering the deployment of such systems. Section 4 outlines the ABM simulation results, with a particular focus on the nature and extent of system error and uncertainty – both spatial and temporal – that modeling illuminates. Section 5 discusses implementation strategies for an RFID RTLS system reflecting a Service Oriented Architecture approach that leverages existing software systems and focuses on being IP-centric and application- and device-agnostic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".