The association between the transfer of emergency department boarders to inpatient hallways or off-service beds and the quality of oncology care.
Bibliographic record
Abstract
113 Background: Emergency department (ED) crowding is an important issue in the delivery of high-quality medical care. At our quaternary care hospital a policy was implemented to ease ED crowding by moving suitable admitted patients into inpatient hallway beds or off-service beds. This study assesses the impact of off service and hallway bed admissions on patient care and satisfaction. Methods: Retrospective and prospective data were collected from Jan 1 to Dec 31, 2011, on admissions to the oncology service via the ED. Patient care data was collected as follows: chest/abdominal exams performed at first MD visit, number of MD visits within 48 hours, time to antibiotic administration, time to complete vitals, and mean time spent in the ED. Satisfaction surveys were also given to all patients. Results: One hundred and eighteen patients were admitted to a hallway bed (HALL). A random sample of 90 patients were used for comparison in the on service (ON) and off service (OFF) groups. Among HALL patients, 4% percent discharged themselves against medical advice (0% of OFF and ON patients). MD visits within 48 hours were the same among all groups (mean=6). Forty-two percent of hallway patients had a chest/abdominal exam during the first MD visit (32% and 33% for OFF and ON patients, respectively). Time to first completion of vitals was 1:05 (hh:mm) for HALL patients (1:21 and 00:34 for OFF and ON patients, respectively). Time to antibiotic administration was 15:34 for hallway patients (23:59 and 12:35 for OFF and ON patients, respectively). More HALL patients expressed dissatisfaction with their hospital stay (16.7%) compared to OFF (0%) and ON patients (0%). Mean time for admitted patients in the ED awaiting their HALL bed was 9:14, considerably longer than for OFF patients (3:08) and ON patients (4:19). Conclusions: Admission of oncology patients in hallway or in off-service beds did not appear to compromise the timeliness or frequency of medical assessments. However, delays in nursing care (completion of vital signs and drug administration) were noted and patient satisfaction was decreased. Moreover, the policy did not meet its intent to reduce patient time spent 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".