Development and validity testing of a matrix to evaluate maturity of clinical pathways: a case study in Saskatchewan, Canada
Bibliographic record
Abstract
Abstract Background Healthcare systems are transforming into learning health systems that use data-driven and research-informed approaches to achieve continuous improvement. One of these approaches is the use of clinical pathways, which are tools to standardize care for a specific population and improve healthcare quality. Evaluating the maturity of clinical pathways is necessary to inform pathway development teams and health system decision makers about required pathway revisions or implementation supports. In an effort to improve the development, implementation, and sustainability of provincial clinical pathways, we developed a clinical pathways maturity evaluation matrix. To explore the initial content and face validity of the matrix, we used it to evaluate a case pathway within a provincial health authority in Saskatchewan, Canada. Methods By using iterative consensus-based processes, we gathered feedback from stakeholders including patient and family partners, policy makers, clinicians, and quality improvement specialists, to rank, retain, or remove enablers and sub-enablers of the draft matrix. We tested the matrix on the Chronic Pain Pathway (CPP) for primary care in a local pilot area and revised the matrix based on feedback from the CPP development team leader. Results The final matrix contains five enablers (i.e., Design, Ownership and Performer, Infrastructure, Performance Management, and Culture), 20 sub-enablers, and three trajectory definitions for each sub-enabler. Supplemental documents were created for six sub-enablers. The CPP scored 15 out of 40 possible points of maturity. Although the pathway scored highest in the Design enabler (10/12), it requires more attention in several areas, specifically the Ownership and Performer and the Performance Management enablers, each of which scored zero. Additionally, the Infrastructure and Culture enablers scored 2/4 and 3/8 points, respectively. These areas of the CPP are in need of improvement in order to enhance the overall maturity of the CPP. Conclusions We developed a clinical pathways maturity matrix to evaluate the various dimensions of clinical pathways’ development and implementation. The goals of this initial work were to develop and validate a tool to assess the maturity and readiness of new or existing pathways and to track pathways' revisions and improvements.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".