A new data source to support hospital operations modeling, message-exchange protocols as illustrated through simulation
Bibliographic record
Abstract
Studies pertaining to hospital operations typically face significant data collection challenges, particularly when defining patient flow patterns. The majority of such studies determine patient flows through observations, stakeholder interviews, and historical patient data analysis. Such methods are time-consuming and typically omit important interactions between resources and patients. This leads to incomplete descriptions of current practices, which can hinder the development and practical application of quantitative models. Furthermore, such processes are expensive and, possibly, subjective. This article presents a methodology for collecting large volumes of very detailed patient flow information. This information is obtained from message-exchange protocols used by hospital information systems to communicate among themselves. The methodology outlines a procedure for extracting detailed information related to (1) individual patient paths, (2) interaction among shared resources, and (3) task duration. The granularity of this information is flexible but can cover various actions in great detail, such as time, location, and person conducting a particular lab test. In this article, we present the general framework of the proposed method, steps for extracting patient flow information, and an illustrative example of a well-known problem from hospital operations management.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".