Defining quality outcomes for complex-care patients transitioning across the continuum using a structured panel process
Bibliographic record
Abstract
BACKGROUND: No standardised set of quality measures associated with transitioning complex-care patients across the various healthcare settings and home exists. In this context, a structured panel process was used to define quality measures for care transitions involving complex-care patients across healthcare settings. METHODS: A modified Delphi consensus technique based on the RAND method was used to develop measures of quality care transitions across the continuum of care. Specific stages included a literature review, individual rating of each measure by each of the panelists (n=11), a face-to-face consensus meeting, and final ranking by the panelists. RESULTS: The literature review produced an initial set of 119 measures. To advance to rounds 1 and 2, an aggregate rating of >75% of the measure was required. This analysis yielded 30/119 measures in round 1 and 11/30 measures in round 2. The final round of scoring yielded the following top five measures: (1) readmission rates within 30 days, (2) primary care visit within 7 days postdischarge for high-risk patients, (3) medication reconciliation completed at admission and prior to discharge, (4) readmission rates within 72 h and (5) time from discharge to homecare nursing visit for high-risk patients. CONCLUSIONS: The five measures identified through this research may be useful as indicators of overall care quality related to care transitions involving complex-care patients across different healthcare settings. Further research efforts are called for to explore the applicability and feasibility of using the quality measures to drive quality improvement across the healthcare system.
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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 | 0.279 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".