Health system capacity and infrastructure for adopting innovations to care for patients with venous thromboembolic disease.
Bibliographic record
Abstract
BACKGROUND: Diagnosis and treatment for venous thromboembolic disease (VTE) have evolved considerably through diagnostic and therapeutic innovations. Despite their considerable potential for enhancing care, however, the extent to which these innovations are being adopted in usual practice is unknown. We documented the infrastructure available in hospitals and health regions across Canada for provision of optimal diagnosis and therapy for VTE disease. METHODS: Over the period January 2008 through October 2009, we studied health system infrastructure for care of VTE disease in Canada's 10 provinces and 3 territories and all 94 health regions therein. We interviewed health system managers and/or clinical leaders from all 658 acute care hospitals in Canada and documented key elements of health system infrastructure at the hospital level for these institutions. RESULTS: There was considerable variation across Canada in the availability of key infrastructure for the diagnosis and management of VTE disease. Provinces with higher populations tended to have a large proportion of hospitals with capability to measure d-dimer levels, whereas less populated provinces were more likely to send samples to centralized analysis facilities for d-dimer testing. All provinces and territories had some facilities offering advanced diagnostic imaging, but the number of institutions and the availability of imaging were highly variable (with the proportion offering at least limited availability ranging from 0% to 90%). Only 6 provinces had regions with availability of dedicated early and/or long-term outpatient clinics for VTE disease. CONCLUSIONS: Infrastructure in Canada for optimal care of patients with VTE disease was suboptimal during the study period and was not entirely in step with the evidence. Such shortfalls in health system infrastructure limit the extent to which health care providers can deliver optimal, evidence-based care to their patients. Nationwide evaluations of health system infrastructure such as this one should be undertaken internationally to better characterize quality of care and potential for improvement.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".