Impact of patient communication problems on the risk of preventable adverse events in acute care settings
Bibliographic record
Abstract
BACKGROUND: Up to 50% of adverse events that occur in hospitals are preventable. Language barriers and disabilities that affect communication have been shown to decrease quality of care. We sought to assess whether communication problems are associated with an increased risk of preventable adverse events. METHODS: We randomly selected 20 general hospitals in the province of Quebec with at least 1500 annual admissions. Of the 145,672 admissions to the selected hospitals in 2000/01, we randomly selected and reviewed 2355 charts of patients aged 18 years or older. Reviewers abstracted patient characteristics, including communication problems, and details of hospital admission, and assessed the cause and preventability of identified adverse events. The primary outcome was adverse events. RESULTS: Of 217 adverse events, 63 (29%) were judged to be preventable, for an overall population rate of 2.7% (95% confidence interval [CI] 2.1%-3.4%). We found that patients with preventable adverse events were significantly more likely than those without such events to have a communication problem (odds ratio [OR] 3.00; 95% CI 1.43-6.27) or a psychiatric disorder (OR 2.35; 95% CI 1.09-5.05). Patients who were admitted urgently were significantly more likely than patients whose admissions were elective to experience an event (OR 1.64, 95% CI 1.07-2.52). Preventable adverse events were mainly due to drug errors (40%) or poor clinical management (32%). We found that patients with communication problems were more likely than patients without these problems to experience multiple preventable adverse events (46% v. 20%; p = 0.05). INTERPRETATION: Patients with communication problems appeared to be at highest risk for preventable adverse events. Interventions to reduce the risk for these patients need to be developed and evaluated.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".