Estimation of Costs-Savings and Improved Patient Outcomes of Implementing a Consultation-Liaison Service at Health Sciences North
Bibliographic record
Abstract
Objective: The study was conducted to assess the implementation of a psychiatric consultation-liaison service (C-L) from the perspective of cost-savings, staff satisfaction, patient satisfaction and to assess the general features of patients referred to the C-L service. Methodology: Cost-savings were evaluated using a large cohort of referrals to the hospital were identified using data derived from the Institute of Clinical Evaluative Sciences (N = 2246); these data were divided into pre and post periods with respect to imitation of the C-L service. To evaluate staff satisfaction, 170 nurses and physicians completed an online survey. Patient satisfaction was assessed through a survey assessing various aspects of their experiences with the C-L service that was completed by each patient (N = 40). Finally referrals to the C-L service (N = 445) were analyzed to discern indicators of the C-L service’s efficacy (i.e. reasons for referral, time to accommodate referral). Results: The data indicated: 1) a reduction in the number of re-admissions and length of stay after the initiation of the C-L service translating into significant cost-savings for the hospital, 2) that increased staff satisfaction was associated with providing confidence, support, and improved communication, and 3) that the C-L service accommodated approximately 90% of patients within 1 day. Conclusion: The results of this study support stakeholders’ decisions to implement C-L services and also indicate areas of improvement that may improve the quality of C-L services within other institutions.
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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 | 0.005 | 0.024 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".