Associations of hospital unit occupancy with inpatient falls and fall-risk assessment completion: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Inpatient fall assessment and prevention efforts are primarily performed by nursing teams. Operating at high occupancy levels may, therefore, reduce the care team's ability to deliver these efforts in a timely and effective way. We investigated the associations of unit-level hospital occupancy with the rate of inpatient fall and the completion of patient fall-risk assessment. METHODS: We conducted a retrospective cohort study using data from a large teaching hospital network in Ontario, between 2017 and 2020. We used a multi-state semi-Markov model to represent the time from admission to inpatient care to primary outcomes of first inpatient fall, and completion of fall-risk assessment in the presence of other competing events. Unit-level occupancy was defined as the time-dependent maximum ratio of unit census to unit capacity and further categorized based on whether it was below or above a given threshold or "tipping point". We estimated the tipping point as well as the association between unit-level occupancy below and above the tipping point with the cause-specific hazard rate of each outcome, adjusting the estimates for confounders. RESULTS: The final cohort had 83 839 inpatient stays for the fall outcome and 83 853 inpatient stays for the fall-risk assessment outcome. Unit occupancy levels above the estimated tipping point of 95% were associated with an increased rate of falls [Hazard Ratio (HR): 2.10, 95% Confidence Interval (CI): 1.05-4.20], whereas occupancy levels above the estimated tipping point of 77% were associated with a decreased rate of completion of fall-risk assessment [HR: 0.87, 95% CI: 0.82-0.91]. CONCLUSIONS: Our study provides evidence for a significant increase in the rate of falls and decrease in the rate of assessment completion when occupancy levels exceed certain tipping points. The results motivate the design of safety protocols tailored for periods of high-capacity strain to support nursing teams and prioritize delivery of assessments and interventions to patients at high risk of fall.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".