The Metrics of Acute Care Reentry and Emergency Department Visits by Recently Discharged Inpatients
Bibliographic record
Abstract
Research on acute care reentry by recently discharged inpatients has generally focused on hospital readmissions, with less attention given to presentations to the emergency department (ED). This omission results in underestimation of the extent of reentry and its impact on ED patient volumes and flow. This project involved an analysis of administrative data to examine the rate of ED presentations by recently discharged inpatients using 3 time metrics-within 0-3 days, 0-7 days, and 0-30 days of discharge. Descriptive-correlational analyses were conducted to examine the rates of reentry and ability to predict ED presentations using patient demographic (age and sex) and clinical profile (length of hospital stay and day of presentation). Approximately 12% of hospital discharges to home involved patients who presented to the ED within 30 days, and almost half occurred within the first week. Results of multivariable analyses suggest that the influences of ED presentations differ depending on the time metric examined. Emergency department presentations within 3 and 7 days of discharge compared with 30 days were not predicted by patient age or sex but were more likely to involve those with shorter hospital stays. A weekend presentation was also more likely among case patients presenting within 3 days of discharge. Only about one third of ED presentations resulted in readmission. Emergency department presentations are an important component of acute care reentry. Establishment of a common reentry metric for ED presentations would facilitate efforts to determine the impact of these events. Emergency nurses working in advanced practice roles are ideally positioned to assume a leadership role in addressing the needs of recently discharged inpatients who present to the ED. By reviewing these cases and collaborating with the inpatient unit staff, it may be possible to identify strategies for augmenting discharge planning and the provision of transitional care.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".