Association of inpatient hospital experience with patient safety indicators: a cross-sectional, Canadian study
Bibliographic record
Abstract
OBJECTIVES: There remains concern regarding the use of survey data to assess aspects of healthcare quality. The relationship between patient experience and adverse events as documented by patient safety indicators (PSIs) is a timely research topic. The objectives were to document the association of PSIs and patient experience scores, and to determine risk-adjusted odds of high experience scores versus PSI presence. SETTING AND PARTICIPANTS: From April 2011 to March 2014, 25 098 patients completed a telephone survey following discharge from 93 inpatient hospitals in Alberta, Canada. RESEARCH DESIGN: A modified version of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) instrument was used. Surveys were linked to inpatient records and PSI presence was documented using a validated algorithm. MEASURES: Three questions about overall hospital, physician and nurse ratings were scored on an 11-point Likert scale from 0 (worst) to 10 (best). Experience was classified as high (9 or 10) versus low (0-8). Demographic/clinical differences between respondents with/without a PSI were assessed. Logistic regression examined the relationship between factors including PSI and experience ratings. RESULTS: Overall, physician and nurse care was rated high by 61.9%, 73.7% and 66.2% of respondents. 1085 patients (4.3%) had a documented PSI. Most frequent PSIs were haemorrhagic events (n=502; 2.0% of sample), events relating to obstetrics (n=373; 1.5%) and surgical-related events (n=248; 1.0%). Risk-adjusted models showed patients with PSIs had decreased odds of having high overall (OR=0.86; 95% CI 0.75 to 0.97), physician (OR=0.76; 95% CI 0.66 to 0.87) and nurse (OR=0.83; 95% CI 0.73 to 0.94) ratings. CONCLUSIONS: There is clear evidence that inpatient experience ratings are associated with PSIs, one element of quality of care. Future research, examining individual PSIs and patient experience questions, is warranted, as this may inform targeted quality improvement initiatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".