Quantifying Spatial Shadow Zones and Their Association With Hospital Falls in Acute Care Unit: Real-Time Location System Observational Study
Bibliographic record
Abstract
BACKGROUND: Hospital falls represent a persistent and significant threat to safety within health care systems worldwide, impacting both patient well-being and the occupational health of health care staff. While patient falls are a primary concern, addressing fall risks for all individuals within the health care environment remains a key objective. Caregiver visibility and spatial monitoring are recognized as crucial considerations in mitigating fall-related incidents. OBJECTIVE: This study aimed to investigate the association between the percentage of spatial shadow zone, defined as areas within an acute care unit unvisited by mobile workstations for prolonged periods, and the incidence of hospital falls and intensive care unit (ICU) transfers. METHODS: This retrospective observational study was conducted in a 400-square-meter acute care unit of a tertiary hospital for over 210 days. An ultrawideband real-time location system was deployed to continuously track mobile workstations' spatial coverage. Spatial shadow zones were defined as areas unvisited by mobile workstations for 60 continuous minutes. The primary outcome was hospital falls; the secondary outcome was ICU transfers. Multivariable logistic regression analysis, adjusted for patient-to-nurse ratio and day of week, was used to examine the association between the percentage of spatial shadow zone and these outcomes. Sensitivity analyses were performed by varying the spatial dilation distance (1-4 meters) and temporal shadow zone thresholds (15-90 minutes). RESULTS: During this study's period, 8 hospital falls and 89 ICU transfers occurred. Real-time location system validation indicated a mean positional error of 0.346 (SD 0.282) meters. In multivariable regression, a higher percentage of spatial shadow zone was significantly associated with an increased odds of hospital falls (odds ratio 1.02, 95% CI 1.01 to 1.03, P<.001). Conversely, a higher percentage of spatial shadow zone was associated with decreased odds of ICU transfer (odds ratio 0.99, 95% CI 0.99 to 0.99, P<.001). Sensitivity analyses demonstrated consistency of the association between spatial shadow zones and falls across varying parameter settings. CONCLUSIONS: This study provides novel evidence for a significant positive association between the percentage of spatial shadow zones and hospital falls, underscoring the critical role of caregiver visibility in fall prevention. The findings suggest that proactively minimizing spatial shadow zones through optimized hospital design, workflow strategies, and technology-enabled monitoring may be a valuable approach to enhance patient safety and reduce hospital falls in acute care settings.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".