Association of electronic order set usage for heart failure and early post hospital outcomes
Bibliographic record
Abstract
Background: Care pathways and electronic medical records (EMR) have been advocated for use in management of acute heart failure. We sought to determine the clinical impact of a specifically designed hospital order set for acute heart failure. Methods: Data from a metropolitan hospital group for all unique hospitalizations with a primary diagnosis of acute heart failure from January 2010 to December 2012 were reviewed. An electronic chart was universally employed for all ordering and testing functions. The heart failure order set was created by a multidisciplinary team and tested for compatibility within the EMR by dedicated personnel. Elements in the order set (in addition to routine admission orders) included items and reminders relating to standard investigations and treatments as well as discharge planning and post discharge follow up. All clinical orders and results were recorded in the EMR and interrogated for this study. Results: Demographic and clinical data were collected on 3946 unique individuals. There were 2045 (52%) females, the average age 75.5 years, creatinine 131 umol/L and heart rate 81 bpm. The median length of stay was 10 days. The electronic order set was utilized in 705 cases and was associated with cardiology involvement as attending or consulting physician (41% vs. 29%, p, 0.001). No other baseline differences were seen. Overall, in-hospital mortality was 10% and 30 day readmission rate 20%. In unadjusted analysis, use of the order set was associated with 1 less day length of stay and 7% lower 7 and 30 day readmission rates. After adjustment for demographic and clinical variables, there persisted significantly lower 7 and 30 day composite clinical (mortality plus all cause readmission) outcome rates, driven by events occurring in the first 7 days post discharge. Table 1. Adjusted 7 and 30 day outcomes according to heart failure order set use Conclusions: Introduction of an electronic heart failure order set within the context of a hospital medical record is associated with lower early rehospitalization and mortality, primarily driven by outcomes in the first week post discharge. Further studies are warranted to evaluate the impact of a larger implementation of this order set.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".