Predicting patient complaints in hospital settings
Bibliographic record
Abstract
BACKGROUND: The prediction of patient complaints is not clearly understood. This is important in so far as patient complaints have been shown to correlate with other adverse outcomes of interest in acute care facilities. OBJECTIVES: To evaluate the complexity of the patient case and patient safety culture as predictors of patient complaints. DESIGN: A matched case-control analysis of data from patients filing complaints (cases) and matched patients who did not file complaints (controls) in 2005. Staff surveys were used to measure the Patient Safety Culture on individual units. SETTING: 45 inpatient acute care units from four general hospitals in a large metropolitan centre in western Canada. SAMPLE: 586 patients registering complaints in 2005. METHOD: The primary outcome was patient complaints (number and type). Predictors included unit-level measures of patient safety culture based on a survey and patient admission characteristics (including age, gender, treatment unit, primary diagnosis, case resource intensity). RESULTS: The probability of a patient complaint was positively associated with cases of higher complexity (beta = 0.145, p = 0.032; odds ratio = 1.16; CI 0.994 to 1.344). The culture of patient safety within hospital units was not related to the probability of complaints within a given unit. CONCLUSIONS: Patient complaints are associated with higher clinical complexity. However, the confidence interval around the odds ratio for this association just crosses 1.0 and is thus not "significant" in a traditional framework of dichotomously judging statistical significance at the 95% confidence level. The lack of association with a unit's safety culture, meanwhile, implies that the non-modifiable clinical complexity factor is a more important determinant of patient complaints.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".