Readmission after Heart Failure Hospitalization: An Environmental Scan of Alberta Initiatives and Outcomes
Bibliographic record
Abstract
Abstract This environmental scan aims to identify and describe initiatives implemented in Alberta, one of the few Canadian provinces with a unified health delivery system, to reduce heart failure (HF) readmissions. It also acknowledges the challenges in attributing direct benefits to these interventions. Using snowball sampling, we identified and recruited 11 employees and clinicians from Alberta Health Services (AHS) who possessed significant historical institutional knowledge about HF. Academic and grey literature were reviewed related to Alberta’s readmission reduction initiatives and reported outcomes. Unstructured, in-depth interviews were conducted to clarify timelines and provide detailed descriptions of these interventions. Our findings indicate substantial clinician efforts over 15 years to address all-cause readmissions post HF hospitalization in Alberta, encompassing a range of interventions from small-scale projects to large multi-city, multi-stakeholder initiatives. Assessing the impact of smaller interventions on provincial readmission rates proved challenging; however, five major initiatives collectively led to a 1.8% reduction in 30-day all-cause readmissions province-wide (from 22.2% to 20.4$, p=0.04). Key factors that appeared to support these efforts included utilization of the EMR system, stakeholder engagement in standardized care, effective communication practices, and appropriate resource allocation. Clinical teams are now integrating successful components from these initiatives into the province-wide clinical information system, Connect Care, to enhance care coordination and patient outcomes. This environmental scan highlights various comprehensive initiatives in Alberta aimed at improving patient care and reducing readmission after HF hospitalization. While an overall 1.8% reduction in readmission rates was observed over the 15 years, attributing this change directly to the interventions is challenging due to various implementation barriers and the complexity of healthcare delivery. Continued efforts towards personalized care and innovative EMR utilization hold promise for further improvement in HF readmission rates.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".