Impact of a Specialized Early Intervention Service for Psychotic Disorders on Patient Characteristics, Service Use, and Hospital Costs in a Defined Catchment Area
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of a new early intervention service for first-episode psychosis on patient characteristics, service use, and hospital costs. METHOD: We examined clinical records of all first admissions to hospitals of patients diagnosed with first-episode psychoses (nonaffective) over a 3-year period before and after the introduction of an early intervention service, the Prevention and Early Intervention Program for Psychosis (PEPP), in a defined catchment area. We examined demographic, clinical, and service use indices covering a 2-year period subsequent to the index admission for each patient. RESULTS: Patients in the post-PEPP phase were significantly younger (P < 0.01), were more often male (P < 0.05), and were less likely to be admitted to hospital with an involuntary status (P < 0.05) or with injuries (P < 0.05) at the time of their first hospital admission. Over the 2 years following the initial admission, post-PEPP patients had significantly fewer admissions to a regular psychiatric service (P < 0.001) and made significantly fewer visits to the hospital emergency department (P < 0.01). There was a significant mean reduction in costs per case of regular hospital bed use ($1028.49, SD 528.02, compared with $792.28, SD 528.02; P < 0.01) and emergency visits ($519.18, SD 353.79, compared with $353.79, SD 345.0; P < 0.01). Time series analyses followed by Chow tests failed to confirm that these cost changes could be attributed specifically to the introduction of an early intervention service. CONCLUSION: Introduction of a specialized early intervention program may be beneficial to patients and to the health care system. To evaluate the cost-benefit ratio of early intervention services, longer term and more detailed data may be required.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".