Patient transfers during hospitalization: An examination of intra facility patient locations using network analysis
Bibliographic record
Abstract
Patient movements following hospitalization are difficult to track. In a large Midwestern academic institution, we analyzed data using network statistics for patients discharged by the hospitalist service between June 2016-June 2018. We retrieved all major patient movements logged in the patient throughput management system following admission. The 4,869 patients discharged by the hospitalist service during the study period experienced 6,832 movements. The mean was 1.4 movements per patient while the maximum was 8. Most patients (72.3%) moved once following hospitalization while 27.7% moved more than once. The predominant movement type was downgrades which comprised 51.8% (n = 3,543) of all movements. Lateral movements were the next most common (25.9%, n = 1,771). Network statistics revealed progressive care units to be central to patient flow across the system. Transfers following hospitalization are common. Visualizing these transfers using network statistics may provide valuable insights to enhance patient safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".