Exploring Changes in Patient Safety Incidents During the COVID-19 Pandemic in a Canadian Regional Hospital System: A Retrospective Time Series Analysis
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has placed unprecedented strain on healthcare systems and may have consequential impacts on patient safety incidents (PSIs). The primary objective of this study was to examine the impact of the COVID-19 pandemic on PSIs reported in Niagara Health. METHODS: Flexible Farrington models were used to retrospectively detect weeks from January to September 2020 where PSI counts were significantly above expected counts. Incident counts were adjusted to weekly inpatient-days. Outcomes included overall incident numbers, incidents by category, and incidents by ward type. RESULTS: The overall number of PSIs across Niagara Health did not increase during the first wave of the COVID-19 pandemic. However, significant increases in falls were observed, suggesting that other types of incidents decreased. Falls increased by 75% from February to March 2020, coinciding with the onset of the first wave of the pandemic. Further investigation by unit type revealed that the number of falls increased specifically on internal medicine and complex continuing care wards. CONCLUSIONS: Despite no observed changes in overall number, significant composition shifts in PSIs occurred during the first wave of the COVID-19 pandemic, with increased falls on internal medicine and complex continuing care wards. Possible explanations include restrictions on patient visitation, reduced patient contact/supervision, and/or personal protective equipment requirements. Providers should maintain a particularly high vigilance for patient falls during pandemic outbreaks, and hospitals should consider targeting resources to higher-risk locations. The results of this study reinforce the need for ongoing pandemic PSI monitoring and rapidly adaptive responses to new patient safety concerns.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".