Delivering On Accountable Care: Lessons From A Behavioral Health Program To Improve Access And Outcomes
Bibliographic record
Abstract
Patients with behavioral health disorders often have worse health outcomes and have higher health care utilization than patients with medical diseases alone. As such, people with behavioral health conditions are important populations for accountable care organizations (ACOs) seeking to improve the efficiency of their delivery systems. However, ACOs have historically faced numerous barriers in implementing behavioral health population-based programs, including acquiring reimbursement, recruiting providers, and integrating new services. We developed an evidence-based, all-payer collaborative care program called Behavioral Health Associates (BHA), operated as part of UCLA Health, an integrated academic medical center. Building BHA required several innovations, which included using our enterprise electronic medical record for behavioral health referrals and documentation; registering BHA providers with insurance plans' mental health carve-out products; and embedding BHA providers in primary care practices throughout the UCLA Health system. Since 2012 BHA has more than tripled the number of patients receiving behavioral health services through UCLA Health. After receiving BHA treatment, patients had a 13 percent reduction in emergency department use. Our efforts can serve as a model for other ACOs seeking to integrate behavioral health care into routine practice.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".