Hospital Use Declines After Implementation Of Virginia Medicaid's Addiction And Recovery Treatment Services.

Medicaid programs responded to the opioid crisis by expanding treatment coverage and reforming delivery systems. We assessed whether Virginia's Addiction and Recovery Treatment Services (ARTS) program, implemented in April 2017, influenced emergency department and inpatient use. Using claims for January 2016-June 2018 and difference-in-differences models, we compared beneficiaries with opioid use disorder before and after ARTS implementation to beneficiaries with no substance use disorder. After program implementation, the likelihood of having an emergency department visit in a quarter declined by 9.4 percentage points (a 21.1 percent relative decrease) among beneficiaries with opioid use disorder, compared to 0.9 percentage points among beneficiaries with no substance use disorder. Similarly, the likelihood of having an inpatient hospitalization declined among beneficiaries with opioid use disorder. In contrast to other states, Virginia has a new Medicaid expansion population whose beneficiaries enter a delivery system in which reforms of the addiction treatment system are well under way.

Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

OBJECTIVE: To propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system. DATA SOURCES: 2012-2013 Truven MarketScan database. STUDY DESIGN: We implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning. The algorithm collection included seven unique algorithms that were supplied with three differing sets of MHSUD-related predictors alongside demographic covariates: HCC, CCS, and HCC + CCS diagnostic variables. Performance was evaluated based on cross-validated R2 and predictive ratios. PRINCIPAL FINDINGS: Results show that super learning had the best performance based on both metrics. The top single algorithm was random forests, which improved on ordinary least squares regression by 10 percent with respect to relative efficiency. CCS categories-based formulas were generally more predictive of MHSUD spending compared to HCC-based formulas. CONCLUSIONS: Literature supports the potential benefit of implementing a separate MHSUD spending risk adjustment formula. Our results suggest there is an incentive to explore machine learning for MHSUD-specific risk adjustment, as well as considering CCS categories over HCCs.

Risk-Adjustment Simulation: Plans May Have Incentives To Distort Mental Health And Substance Use Coverage.

Under the Affordable Care Act, the risk-adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the risk-adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders. Our assessment points to systematic underpayment to plans for people with these diagnoses. We document how Marketplace risk adjustment does not remove incentives for plans to limit coverage for services associated with mental health and substance use disorders. Adding mental health and substance use diagnoses used in Medicare Part D risk adjustment is one potential policy step toward addressing this problem in the Marketplaces.