Enabling continuous learning and quality improvement in health care
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how the design and implementation of learning models for performance management can foster continuous learning and quality improvement within a publicly funded, multi-site community hospital organization. Design/methodology/approach Niagara Health's patient flow performance management system, a learning model, was studied over a 20-month period. A descriptive case study design guided the analysis of qualitative observational data and its synthesis with organizational learning theory literature. Emerging from this analysis were four propositions to inform the implementation of learning models and future research. Findings This performance management system was observed to enable: ongoing performance-related knowledge exchange by creating opportunities for routine social interaction; collective recognition and understanding of practice and performance patterns; relationship building, learning for improvement, and "higher order" learning through dialogue facilitated using humble inquiry; and, alignment of quality improvement efforts to organizational strategic objectives through a multi-level feedback/feed-forward communication structure. Research limitations/implications The single organization and descriptive study design may limit the generalizability of the findings and introduce confirmation bias. Future research should more comprehensively evaluate the impact of learning models on organizational learning processes and performance outcomes. Practical implications This study offers novel insight which may inform the design and implementation of learning models for performance management within and beyond the study site. Originality/value Few studies have examined the mechanics of performance management systems in relation to organizational learning theory and research. Broader adoption of learning models may be key to the development of continuously learning and improving health systems.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".