The economic burden of nurse‐sensitive adverse events in 22 medical‐surgical units: retrospective and matching analysis
Bibliographic record
Abstract
AIMS: The aim of this study was to assess the economic burden of nurse-sensitive adverse events in 22 acute-care units in Quebec by estimating excess hospital-related costs and calculating resulting additional hospital days. BACKGROUND: Recent changes in the worldwide economic and financial contexts have made the cost of patient safety a topical issue. Yet, our knowledge about the economic burden of safety of nursing care is quite limited in Canada in general and Quebec in particular. DESIGN: Retrospective analysis of charts of 2699 patients hospitalized between July 2008 - August 2009 for at least 2 days of 30-day periods in 22 medical-surgical units in 11 hospitals in Quebec. METHODS: Data were collected from September 2009 to August 2010. Nurse-sensitive adverse events analysed were pressure ulcers, falls, medication administration errors, pneumonia and urinary tract infections. Descriptive statistics identified numbers of cases for each nurse-sensitive adverse event. A literature analysis was used to estimate excess median hospital-related costs of treatments with these nurse-sensitive adverse events. Costs were calculated in 2014 Canadian dollars. Additional hospital days were estimated by comparing lengths of stay of patients with nurse-sensitive adverse events with those of similar patients without nurse-sensitive adverse events. RESULTS: This study found that five adverse events considered nurse-sensitive caused nearly 1300 additional hospital days for 166 patients and generated more than Canadian dollars 600,000 in excess treatment costs. CONCLUSION: The results present the financial consequences of the nurse-sensitive adverse events. Government should invest in prevention and in improvements to care quality and patient safety. Managers need to strengthen safety processes in their facilities and nurses should take greater precautions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".