Implementation of a Specialized Heart Function Inpatient Unit to Improve Patient Care, Trainee Education, and Healthcare System Efficiencies
Bibliographic record
Abstract
A BSTRACT Background: Heart failure is an increasingly prevalent condition characterized by long length of stays (LOSs) in hospital and high 30-day readmission rates. To accommodate the growing number of patients, a new specialized heart function inpatient unit (HFIU) was implemented to allow dedicated personnel trained in heart failure management. The HFIU aimed to improve staff satisfaction and reduce LOS and 30-day readmissions. We report this initial evaluation at a cardiac center in Hamilton, ON. Methods and Design: The HFIU service had a capacity of 7–10 beds and was staffed by a cardiologist and nurse practitioner trained in heart failure management. Online surveys were administered at 3- and 12-month postimplementation to all staff involved, using a 7-point Likert scale. In addition, patient metrics for 6-month preimplementation and 12-month postimplementation were reviewed from a hospital database, including LOS and 30-day readmission rates. Results: Staff expressed satisfaction with the HFIU at 12-month postimplementation on 7-point Likert scale with mean responses of 5.40 (standard deviation [SD] =1.07) for physicians ( n = 10) and 5.50 (SD = 1.13) for nurses ( n = 11). The median LOS was 8.3 days (interquartile range [IQR] =0.75) versus 7.70 days (IQR = 2, Z = −0.52, P = 0.6), and readmission rate was 21% vs. 19% (t (7) = −0.64, P = 0.62) in the pre- versus post implementation groups. Conclusions: The HFIU enabled focused and dedicated heart failure care. Staff satisfaction and a trend of reduced mortality rate (23% vs. 5%, P = 0.04) in the postimplementation group were observed. These results indicate a potential benefit in considering the implementation and further evaluation of specialized units in the care of heart failure patients.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".