Development and implementation of an online clinical pathway for adult chronic kidney disease in primary care: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Primary care physicians and other primary health care professionals from Alberta, Canada identified a clinical pathway as a potential tool to facilitate uptake of clinical practice guidelines for the diagnosis, management and referral of adults with chronic kidney disease. We describe the development and implementation of a chronic kidney disease clinical pathway (CKD-CP; www.ckdpathway.ca ). METHODS: The CKD-CP was developed and implemented based on the principles of the Knowledge-To-Action Cycle framework. We used a mixed methods approach to identify the usability and feasibility of the CKD-CP. This included individual interviews, an online survey and website analytics, to gather data on barriers and facilitators to use, perceived usefulness and characteristics of users. Results are reported using conventional qualitative content analysis and descriptive statistics. RESULTS: Eighteen individual interviews were conducted with primary care physicians, nephrologists, pharmacists and nurse practitioners to identify themes reflecting both barriers and facilitators to integrating the CKD-CP into clinical practice. Themes identified included: communication, work efficiency and confidence. Of the 159 participants that completed the online survey, the majority (52 %) were first time CKD-CP users. Among those who had previously used the CKD-CP, 94 % agreed or strongly agreed that the pathway was user friendly, provided useful information and increased their knowledge and confidence in the care of patients with CKD. Between November 2014 and July 2015, the CKD-CP website had 10,710 visits, 67 % of which were new visitors. The 3 most frequently visited web pages were home, diagnose and medical management. Canada, Indonesia and the United States were the top 3 countries accessing the website during the 9 month period. CONCLUSIONS: An interactive, online, point-of-care tool for primary care providers can be developed and implemented to assist in the care of patients with CKD. Our findings are important for making refinements to the CKD -CP website via ongoing discussions with end-users and the development team, along with continued dissemination using multiple strategies.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".