The impact of hospital harm on length of stay, costs of care and length of person-centred episodes of care: a retrospective cohort study
Bibliographic record
Abstract
<h3>BACKGROUND:</h3> There is a lack of data in Canada on the longitudinal effects of adverse events that occur in hospital, specifically in the period after discharge. Our objective was to quantify the impact of adverse events on hospital length of stay, length of person-centred episodes of care (PCEs) and costs of PCEs, as well as their impact on the total health system. <h3>METHODS:</h3> We conducted a population-based, retrospective cohort study using linked health administrative databases. We included adults in Ontario who had an acute hospital admission between Apr. 1, 2015, and Mar. 31, 2016. We grouped hospital admissions into 1 of 9 episode types and used the Canadian Institute for Health Information methodology for hospital harm to measure adverse events. We specified generalized linear models to estimate the impact of hospital harm on the following: incremental length of index acute hospital admission, incremental length of the PCE, and incremental costs of the PCE. <h3>RESULTS:</h3> Out of 610 979 hospital admissions, 36 004 (5.9%) involved an occurrence of harm. The impact of harm on the incremental length of hospital stay ranged from 0.4 to 24.2 days (<i>p</i> < 0.001); the incremental length of the PCE ranged from 0.3 to 30.2 days (<i>p</i> < 0.001); and the incremental costs of the PCE ranged from $800 to $51 067 (<i>p</i> < 0.001). Total hospital days attributable to hospital harm amounted to 407 696, and the total attributable cost to the Ontario health system amounted to $1 088 330 376. <h3>INTERPRETATION:</h3> We found that experiencing harm in hospital significantly affects both in-hospital and post-discharge use of health services and costs of care, and constitutes an enormous expense to Ontario’s publicly funded health system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".