Examining the adaptability and validity of interRAI acute care quality indicators in a surgical context
Bibliographic record
Abstract
Background: Currently, the use of quality indicators in the surgical setting may be challenged by diverse patient needs, clinical complexity, and health trajectories. Therefore, the objective of this study was to examine the adaptability of existing quality indicators to a surgical context and propose new time points. Methods: A multi-method approach included an environmental scan of the literature, consultation with multinational experts, and analysis of surgical patient data. Quality indicators from the nurse-administered interRAI Acute Care instrument were examined within a surgical context using secondary data from a hospital in Brisbane, Australia (N = 1006 surgical cases). Results: A lack of relevancy of existing time points can preclude meaningful quality indicator measurement. Definitions of some quality indicators were adapted to ensure relevancy for the surgical population. As well, a surgical baseline (measured preoperative and post-injury) and a 48-h postoperative time point were added to the existing measurement timeline. Conclusion: Distinct measurement timelines were created for elective and non-elective surgical patients. The use of surgery-specific time points that can be embedded into an existing Acute Care measurement framework supports consistent quality indicator reporting. This study represents the first steps towards standardized quality reporting for health information systems across different care settings.
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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 | 0.079 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".