Computerized Electronic Order Set: Use and Outcomes for Heart Failure Following Hospitalization
Bibliographic record
Abstract
Background Quality 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. Methods An 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. Results In 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%). Conclusions Use 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.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".