Implementation of proven interventions in general medical inpatients: development and evaluation of a new quality indicator for drug therapy
Bibliographic record
Abstract
BACKGROUND: Among adult general medical inpatients, there are numerous interventions whose benefits outweigh their risks. However, there are no published reports describing the overall use of such proven interventions in this population. OBJECTIVES: To determine implementation rates of a broad range of interventions while accounting for valid reasons for non-use, predictors of implementation and feasibility of generating new indices to describe quality of care. METHODS: Based on a review of current practice guidelines and clinical trials related to five common conditions, implementation rates of 17 interventions were assessed retrospectively. Subjects were a complete sample of 150 adults with target medical conditions discharged from general medical units at an urban community hospital. RESULTS: The Ideal Intervention Index (3I), which described the proportion of ideal intervention opportunities that were implemented, was 0.74 (95% CI 0.70 to 0.78). The Justified Non-Use Index (JNUI), which described the proportion of all the interventions not implemented that were justified by a valid reason for non-use, was 0.49 (95% CI 0.41 to 0.55). Smoking cessation therapy in high-risk patients had the lowest indices (3I 0.30, 95% CI 0.00 to 0.60; JNUI 0.00), and aspirin for secondary stroke prevention had the highest (3I 1.0; JNUI 1.0). CONCLUSIONS: Overall, proven interventions are underused among the patients studied, and the reasons for non-use are frequently not readily discernible. There is potential for improvement, but research is required to further investigate reasons for non-use. It is feasible to measure implementation rates of proven interventions as an indicator of quality of care using the indices developed.
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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 | 0.032 | 0.003 |
| 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) | 0.001 | 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".