Barriers of Successful Implementation of Discharge Criteria at a Tertiary Heart Function Clinic: A Retrospective Cohort Analysis
Bibliographic record
Abstract
Background Most heart function clinics cannot absorb their high volume of referrals. Effectiveness of clinic discharge protocols to offload stable patients is understudied. We examined predictors and barriers of implementing discharge criteria at our tertiary heart function clinic. Methods This is a retrospective analysis of discharge protocol implementation between August 1st, 2023, and March 31, 2024. Outcomes were discharge and rates of acute care utilization within 6-months post-discharge. Results Out of 153 patients reviewed, 92 were suitable for discharge, but only 56/92 (60.9%) were discharged. Discharge failure was associated with atrial fibrillation (66.7% not discharged vs 30.4% discharged; p<0.001), having ejection fraction <50% at the last visit (77.8% not discharged vs 51.8% discharged; p=0.012), having worse kidney function (initial visit creatinine 101.0 μmol/L discharged vs 86.5 not discharged; and at last visit 106.5 vs 99.0 μmol/L), and provider experience <10 years (36.1% not discharged vs 16.1% discharged; p=0.028). Reasons cited for discharge failure were providers awaiting an extra echocardiogram (48.6%) or coordinating with other cardiac care teams (27.0%). In the 6 months following discharge, 3 (5.4%) patients visited the emergency department for heart failure (HF), 1 (1.8%) patient was hospitalized for HF, and 1 (1.8%) patient passed away from cancer. Three out of five adverse outcomes were judged to be unavoidable even if clinic follow-up was continued. Conclusions Discharge rate from our clinic is suboptimal, which impedes timely care for new referrals. We identified several patient- and provider-specific barriers to discharge. More studies are needed to explore this important area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".