Using systems thinking to support clinical system transformation
Bibliographic record
Abstract
Purpose - The British Columbia Ministry of Health's Clinical Care Management initiative was used as a case study to better understand large-scale change (LSC) within BC's health system. Using a complex system framework, the purpose of this paper is to examine mechanisms that enable and constrain the implementation of clinical guidelines across various clinical settings. Design/methodology/approach - Researchers applied a general model of complex adaptive systems plus two specific conceptual frameworks (realist evaluation and system dynamics mapping) to define and study enablers and constraints. Focus group sessions and interviews with clinicians, executives, managers and board members were validated through an online survey. Findings - The functional themes for managing large-scale clinical change included: creating a context to prepare clinicians for health system transformation initiatives; promoting shared clinical leadership; strengthening knowledge management, strategic communications and opportunities for networking; and clearing pathways through the complexity of a multilevel, dynamic system. Research limitations/implications - The action research methodology was designed to guide continuing improvement of implementation. A sample of initiatives was selected; it was not intended to compare and contrast facilitators and barriers across all initiatives and regions. Similarly, evaluating the results or process of guideline implementation was outside the scope; the methods were designed to enable conversations at multiple levels - policy, management and practice - about how to improve implementation. The study is best seen as a case study of LSC, offering a possible model for replication by others and a tool to shape further dialogue. Practical implications - Recommended action-oriented strategies included engaging local champions; supporting local adaptation for implementation of clinical guidelines; strengthening local teams to guide implementation; reducing change fatigue; ensuring adequate resources; providing consistent communication especially for front-line care providers; and supporting local teams to demonstrate the clinical value of the guidelines to their colleagues. Originality/value - Bringing a complex systems perspective to clinical guideline implementation resulted in a clear understanding of the challenges involved in LSC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".