Quality of care and outcomes in internal medicine patients bedspaced to noninternal medicine units
Bibliographic record
Abstract
ABSTRACT: Hospital overcrowding has led to a practice known as bedspacing (in which admitted patients are placed on a different specialty's inpatient ward), yet little is known about the impact of this practice on healthcare quality.We investigated whether hospital outcome measures differ between bedspaced general internal medicine (GIM) patients vs nonbedspaced patients.Our retrospective study included patients admitted to GIM wards at 2 academic hospitals (2012-2014), comparing bedspaced to nonbedspaced patients, and identifying adverse events from the hospital's Electronic Patient Record.We compared these groups with respect to actual length of stay vs the expected length of stay (% ELOS), which is defined as length of stay (LOS) divided by expected length of stay (ELOS), 30-day readmission, adverse events (falls, medication-related incidents, equipment-related incidents, first treatment related incidents, laboratory-related incidents, and operative/invasive events), and in-hospital mortality.There were 22,519 patients analyzed with 15,985 (71%) discharged from a medical ward and 6534 (29%) discharged from a non-medical ward. Bedspaced patients had shorter lengths of stay (4.1 vs 6.2 days, P < .001) and expected lengths of stay (ELOS) (6.1 vs 6.4 days, P < .001). Bedspaced patients had a lower percentage of ELOS (% ELOS) than nonbedspaced patients (70% vs 91%, P < .001), similar readmission rates (9.8 vs 10.3 events per 100 patients, P = .24), lower in-hospital mortality rates (2.6 vs 3.3 events per 100 patients, P = .003) and fewer adverse events (0.20 vs 0.60 events per 100 patient days, P < .01).Bedspacing of patients is common. Patients who are bedspaced to off-service wards have better outcomes. This may relate to preferential allocation practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".