Incentivizing co-occurring disorder diagnoses through blended payments
Bibliographic record
Abstract
BACKGROUND: Treatments for mental health and substance use problems have historically been unintegrated, limiting co-occurring disorders treatment. Blending discrete payment models is one potential facilitator of integrated care. This study assesses the impact of one blended payment strategy on the diagnosis of co-occurring disorders in a community mental health system. METHODS: Electronic health record data for 19373 individuals, with 173889 observations from January 2017 through December 2019 was analyzed for this study. Multilevel growth modelling was used for data analysis. A binary dependent variable represented whether a service user held diagnoses of co-occurring disorders within a month. Fixed effects included time variables and a variable representing blended payment initiation as well as race, gender, age, and payor. Service user and agency variables were modeled as random effects. FINDINGS: Blended capitated and fee-for-service payments were found to increase the odds of service users receiving co-occurring diagnoses. People of color had lower odds of receiving a co-occurring diagnosis, although this effect did not hold in an analysis of rural agencies. Service users receiving care in unintegrated agencies had higher odds of receiving co-occurring diagnoses. CONCLUSION: This study is one of the first to assess the impacts of a blended payment model on behavioral health access. Blended payment models can incentivize behavioral health providers and systems to identify complex diagnoses that may go unrecognized in routine care.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.003 |
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".