What initiatives do healthcare leaders agree are needed for healthcare system improvement? Results of a modified-Delphi study
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to identify five quality improvement initiatives for healthcare system leaders, produced by such leaders themselves, and to provide some guidance on how these could be implemented. DESIGN/METHODOLOGY/APPROACH: A multi-stage modified-Delphi process was used, blending the Delphi approach of iterative information collection, analysis and feedback, with the option for participants to revise their judgments. FINDINGS: The process reached consensus on five initiatives: change information privacy laws; overhaul professional training and work in the workplace; use co-design methods; contract for value and outcomes across health and social care; and use data from across the public and private sectors to improve equity for vulnerable populations and the sickest people. RESEARCH LIMITATIONS/IMPLICATIONS: Information could not be gathered from all participants at each stage of the modified-Delphi process, and the participants did not include patients and families, potentially limiting the scope and nature of input. PRACTICAL IMPLICATIONS: The practical implications are a set of findings based on what leaders would bring to a decision-making table in an ideal world if given broad scope and capacity to make policy and organisational changes to improve healthcare systems. ORIGINALITY/VALUE: This study adds to the literature a suite of recommendations for healthcare quality improvement, produced by a group of experienced healthcare system leaders from a range of contexts.
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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 | 0.090 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".