Person-centred quality indicators are associated with unplanned care use following hospital discharge
Bibliographic record
Abstract
OBJECTIVE: Performance indicators are used to evaluate the quality of healthcare services. The majority of these, however, are derived solely from administrative data and rarely incorporate feedback from patients who receive services. Recently, our research team developed person-centred quality indicators (PC-QIs), which were co-created with patients. It is unknown whether these PC-QIs are associated with unplanned healthcare use following discharge from hospital. DESIGN: A retrospective, cross-sectional study. METHODS: Survey responses were obtained from April 2014 to September 2020 using the Canadian Patient Experiences Survey - Inpatient Care instrument. Logistic regression models were used to predict the link between eight PC-QIs and two outcomes; unplanned readmissions within 30 days and emergency department visits within 7 days. RESULTS: A total of 114 129 surveys were included for analysis. 6.0% of respondents (n=6854) were readmitted within 30 days, and 9.9% (n=11 287) visited an emergency department within 7 days of their index discharge. In adjusted models, 'top box' responses for communication between patients and physicians (adjusted OR (aOR)=0.82, 95% CI: 0.77 to 0.88), receiving information about taking medication (aOR=0.86, 95% CI: 0.80 to 0.92) and transition planning at hospital discharge (aOR=0.79, 95% CI: 0.73 to 0.85) were associated with lower odds of emergency department visit.Likewise, 'top box' responses for overall experience (aOR=0.87, 95% CI: 0.82 to 0.93), communication between patients and physicians (aOR=0.73, 95% CI: 0.67 to 0.80) and receiving information about taking medication (aOR=0.90, 95% CI: 0.83 to 0.98), were associated with lower odds of readmission. CONCLUSIONS: This study demonstrates that patient reports of their in-hospital experiences may have value in predicting future healthcare use. In developing the PC-QIs, patients indicated which elements of their hospital care matter most to them, and our results show agreement between subjective and objective measures of care quality. Future research may explore how current readmission prediction models may be augmented by person-reported experiences.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".