Evaluation of the value of team-based psychiatric consultation in a general hospital setting
Bibliographic record
Abstract
Background With the increase use of pay for performance in healthcare, 30-day readmissions after discharges are critically important. Objective A team-based psychiatric consultation approach was tested in an inpatient hospital setting. This is the first study that examines 30-day readmission rate with this approach. Methods In this quality improvement study, 164 patients received a team-based psychiatric consultation that included daily meetings during the weekdays between psychiatrists and hospitalists and 436 received care of treatment-as-usual or traditional consultation-liaison services. Results Overall 30-day readmission rate was not significantly different between intervention and nonintervention groups. However, in subgroups with high risk of mortality or severe illness, the intervention group had a 0% 30-day readmission rate for both high risk of mortality and severe illness subgroups, while the nonintervention group's readmission rate was 5% for high risk of mortality group and 3% for severely ill patients. Annual hospital cost saving is estimated between a quarter million and 1.5 million dollars for these subgroups. Conclusion The team-based psychiatric consultation approach demonstrated the potential for substantial cost savings in providing care for patients with high risk of mortality and severe illness. Thus, this intervention may be very useful in caring for patients with complex chronic conditions.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".