Patient Reports of Night Noise in Hospitals Are Associated With Unplanned Readmissions Among Older Adults
Bibliographic record
Abstract
OBJECTIVE: Sleep disturbance is a key contributor to posthospital syndrome; a transient period of vulnerability following discharge from hospital. We sought to examine the relationship between patient-reported hospital quietness at night, via a validated survey, and unplanned hospital readmissions among hospitalized seniors (ages 65 and older) in Alberta, Canada. DESIGN: Retrospective, cross-sectional analysis of survey responses, linked with administrative inpatient records. SETTING: Using the Canadian Patient Experiences Survey-Inpatient Care and Discharge Abstract Database, patients aged 65 and older, and living with one or more chronic conditions were identified. PARTICIPANTS: Of all, 25 674 respondents discharged from hospital between April 2014 and December 2017. MAIN OUTCOME MEASURE: All-cause, unplanned readmission within 30 or 90 days (yes vs no). RESULTS: Approximately half (50.5%) of the respondents reported that the area around their room was always quiet at night. Eight (8.1%) percent of respondents (2066) were readmitted within 30 days (2241 total readmissions), while 15.6% (4000) were readmitted within 90 days (5070 total readmissions). When controlling for a variety of demographic and clinical factors, patients not reporting "always" to the survey question regarding hospital quietness at night had slightly greater odds of readmission within 30 (adjusted odds ratio [aOR] = 1.32, 95% confidence interval [CI]: 1.20-1.45) and 90 days (aOR = 1.14, 95% CI: 1.06-1.23). CONCLUSION: Our results demonstrate a clear association between patient-reported hospital quietness at night and subsequent readmission within the first 30 and 90 days following discharge. Efforts to minimize hospital noise, particularly at night, may help promote a restful environment, while reducing readmissions among older patients living with chronic conditions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".