Incentivizing co-occurring disorder diagnoses through blended payments

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.

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.

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.

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.