The business case for hospital-based Behavioral Screening and Intervention
Bibliographic record
Abstract
Under the Affordable Care Act, hospitals are challenged to avoid growing penalties for adverse outcomes, including readmissions, and to adapt to value-based purchasing, where parent organizations will ultimately regard hospital revenues as costs. Hospitals are responding by implementing quality improvement programs, strengthening coordination of care around and after discharge, and enhancing chronic disease management, but many hospitals continue to suffer penalties. An additional response could be to systematically conduct screening and intervention for “upstream” behavioral risks and disorders – smoking, unhealthy drinking and depression – which are associated with admissions, inferior medical and surgical outcomes, readmissions, and ample costs. By increasing smoking quit rates, reducing binge drinking and enhancing depression outcomes, Behavioral Screening and Intervention (BSI) could improve outcomes for various chronic diseases, prevent acute disease and injury, decrease hospital admissions and readmissions, avert surgical complications, and improve hospitals’ bottom lines. This article discusses how hospitals could implement BSI and potential benefits, barriers and limitations.
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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 | 0.001 | 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".