Applications of Semi-Markov Models to Investigate the Associations of Hospital Capacity Strain, Inpatient Falls, and Fall-Risk Assessment Completion
Bibliographic record
Abstract
In-hospital falls are among the most reported patient safety incidents. To prevent falls, nursing teams provide fall-risk assessments and subsequently implement personalized fall- prevention measures. As such, high hospital occupancy may hinder the nursing teams’ ability to deliver these efforts in a timely and effective manner. Although the impact of hospital capacity strain on mortality has been previously studied, evidence for its impacts on rates of fall and fall-risk assessment completion is lacking. In this work, we propose two semi-Markov models of patient hospital stay. We use observational data from a large teaching hospital network in Toronto, Ontario, to investigate the impacts of high occupancy levels by estimating the transition rates and holding times of the semi-Markov processes. We find evidence for the existence of threshold or “tipping points” for the maximum unit-level occupancy that a patient is exposed to, above which increased occupancy is associated with increased rate of fall and reduced rate of fall-risk assessment completion. Our results high- light the need for design of customized protocols during periods of capacity strain, including prioritization of high-risk patients for receiving fall-risk assessments and interventions.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".