Computerized Electronic Order Set: Use and Outcomes for Heart Failure Following Hospitalization
Bibliographic record
Abstract
BackgroundQuality improvement initiatives improve health care delivery but may be resource intensive and disrupt clinical care. An embedded heart failure order set (HFOS) within a computerized physician order-entry system may mitigate these concerns.MethodsAn HFOS, based on proven interventions, was implemented within an existing computerized physician order-entry system in all adult acute-care hospitals in a single Canadian metropolitan city and interrogated between January 1, 2013 and December 31, 2015. The composite of repeat hospitalization or death within 30 days of hospital discharge and hospital length of stay were reported.ResultsIn total, 8969 patients were included with mean age 75.6 ± 13.5 years; 4673 (52.1%) were male. The HFOS was used in 731 (8.2%) patients. After analysis of 724 pairs of propensity-score matched cohorts, patients with HFOS use experienced a lower median length of stay (8.6 vs 9.4 days, P = 0.016) and a trend toward lower composite repeat hospitalization or death (14.5% vs 17.7%, P = 0.115, hazard ratio 0.79 (0.60–1.05). Patients with HFOS use were more likely to undergo a test for left ventricular ejection fraction (88.6% vs 76.7%, P < 0.001, and to be referred to a heart failure clinic (48.5% vs 6.3%), with similar rates of discharge prescription of beta-blockers (88.7% vs 86.3) and angiotensin-converting enzyme inhibitors (87.4% vs 89.0%).ConclusionsUse of a designated HFOS within a computerized physician order-entry system is associated with shorter hospital length of stay without increase in deaths or readmissions. These findings should be confirmed in a prospective controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".