Alternatives to conventional hospitalization for improving lack of access to inpatient beds: a 12-year cross-sectional analysis
Bibliographic record
Abstract
Background/Objective: When hospitals cannot guarantee available hospital beds for inpatient admission, patients are exposed to prolonged waits, cancellations and diversions that negatively affect their safety and quality of care. The purpose of this study was to evaluate the effect of a multifaceted intervention for eliminating inpatient access delays. Methods: By using multi-time point cross-sectional analysis, data from all hospitalizations from adult patients registered at an 850-bed public, tertiary-care university hospital were compared from March 1998 to March 2000 (control period) with data from April 2000 to March 2010 (intervention period), after a set of 15 actions for avoiding unnecessary inpatient admissions and to reduce length of hospital stay was implemented by a clinician-administrator taskforce. Response variable was the daily average of “boarded” patients waiting for a hospital bed at 8:00 am in the emergency department (ED). Other measurements included daily contextual and performance hospital variables captured automatically by computer-assisted processes. Results: Between March 1998 and March 2010, 348,960 consecutive hospitalizations were registered. Despite daily ED visits increasing from 288 (IQR Q1-Q3: 270-309) to 335 patients (IQR Q1-Q3: 306-359; P<.01), multifaceted intervention reduced the daily emergency “boarded” patients from 7 (IQR Q1-Q3: 3-14) to 3 patients (IQR Q1-Q3: 1-6; P<.01), and length of hospital stay from 10.9 (IQR Q1-Q3: 9.6-12.5) to 8.1 days (IQR Q1-Q3: 6.8-9.8; P<.01), while increasing daily scheduled admissions from 32 (IQR Q1-Q3: 17-58) to 63 patients (IQR Q1-Q3: 13-90; P<.01). Conclusion: Major changes in hospital procedures for bridging the gap between inpatient and outpatient care by implementing alternatives to conventional hospitalization solved the “inpatient boarding” phenomenon in the ED.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".