Guideline-based quality assurance: a conceptual framework for the definition of key elements
Bibliographic record
Abstract
BACKGROUND: In 2017, the European Commission's Joint Research Centre (JRC) started developing a methodological framework for a guideline-based quality assurance (QA) scheme to improve cancer quality of care. During the first phase of the work, inconsistency emerged about the use of terminology for the definition, the conceptual underpinnings and the way QA relates to health questions that are answered in guidelines. The objective of this final of three articles is to propose a conceptual framework for an integrated approach to guideline and QA development and clarify terms and definitions for key elements. This work will inform the upcoming European Commission Initiative on Colorectal Cancer (ECICC). METHODS: A multidisciplinary group of 23 experts from key organizations in the fields of guideline development, performance measurement and quality assurance participated in a mixed method approach including face-to-face dialogue and several rounds of virtual meetings. Informed by results of a systematic literature review that indicated absence of an existing framework and practical examples, we first identified the relations of key elements in guideline-based QA and then developed appropriate concepts and terminology to provide guidance. RESULTS: Our framework connects the three key concepts of quality indicators, performance measures and performance indicators integrated with guideline development. Quality indicators are constructs used as a guide to monitor, evaluate, and improve the quality of the structure, process and outcomes of healthcare services; performance measures are tools that quantify or describe measurable elements of practice performance; and performance indicators are quantifiable and measurable units or scores of practice, which should be guided by guideline recommendations. CONCLUSIONS: The inconsistency in the way key terms of QA are used and defined has confused the field. Our conceptual framework defines the role, meaning and interactions of the key elements for improving quality in healthcare. It directly builds on the questions asked in guidelines and answered through recommendations. These findings will be applied in the forthcoming ECICC and for the future updates of ECIBC. These are large-scale integrated projects aimed at improving healthcare quality across Europe through the development of guideline-based QA schemes; this will help in implementing and improving our approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".