The Effect of Multidisciplinary Heart Failure Clinic Characteristics on 1-Year Postdischarge Health Care Costs
Bibliographic record
Abstract
BACKGROUND: Although multidisciplinary heart failure (HF) clinics are efficacious, it is not known how patient factors or HF clinic structural indicators and process measures have an impact on the cumulative health care costs. RESEARCH DESIGN: In this retrospective cohort study using administrative databases in Ontario, Canada, we identified 1216 HF patients discharged alive after an acute care hospitalization in 2006 and treated at a HF clinic. The primary outcome was the cumulative 1-year health care costs. A hierarchical generalized linear model with a logarithmic link and gamma distribution was developed to determine patient-level and clinic-level predictors of cost. RESULTS: The mean 1-year cost was $27,809 (range, $69 to $343,743). There was a 7-fold variation in the mean costs by clinic, from $14,670 to $96,524. Delays in being seen at a HF clinic were a significant patient-level predictor of costs (rate ratio 1.0015 per day; P<0.001). Being treated at a clinic with >3 physicians was associated with lower costs (rate ratio 0.78; P=0.035). Unmeasured patient-level differences accounted for 97.4% of the between-patient variations in cost. The between-clinic variation in costs decreased by 16.3% when patient-level factors were accounted for; it decreased by a further 49.8% when clinic-level factors were added. CONCLUSIONS: From a policy perspective, the wide spectrum of HF clinic structure translates to inefficient care. Greater guidance as to the type of patient seen at a HF clinic, the timeliness of the initial visit, and the most appropriate structure of the HF clinics may potentially result in more cost-effective care.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".