Project Summary People with opioid use disorder (OUD) have higher rates of all-cause mortality than the general population; nearly 20% of people with OUD eventually die from drug overdose. Medications for opioid use disorder (MOUD) can treat withdrawal symptoms, reduce overdose risk, and help keep patients engaged in treatment, but medications are under-prescribed in healthcare settings. Concurrent methamphetamine use disorder (MAUD) with OUD is common in the Western United States. Patients who have OUD and MAUD are less likely to accept MOUD, and have increased overdose risk. Interventions to improve initiation and uptake of MOUD, and for behavioral treatment for MAUD, are urgently needed. Addiction consult services (ACS) are an emerging standard of care for patients hospitalized with OUD and MAUD. ACS's have been shown to both reduce substance use and increase engagement in medical care, after hospitalization. However, the effect of ACS's on post-discharge overdose and all-cause mortality is unknown. In the midst of the opioid and methamphetamine epidemics, healthcare systems need more information about the effectiveness of ACS's for improving these outcomes. I propose a mentored training plan to develop advanced modeling skills that will further my long-term goal of becoming a physician-scientist who conducts health services research that improves the lives of underserved communities, including people with addiction. I will learn to use time-varying simulation modeling to rapidly test ACS scenarios and capture robust estimates of study outcomes such as mortality, which can support healthcare system decision-making and answer salient clinical questions in the midst of the opioid and methamphetamine epidemics. These virtual labs test counterfactual scenarios--what could have happened if researchers went back in time and removed or implemented different interventions. Modeling inpatient care scenarios can guide healthcare systems in addressing a rapidly evolving epidemic more quickly and adaptively than randomized trials. In Aim 1, I will build and validate two Markov models (one for patients with OUD only, and one for patients with OUD/MAUD) that capture post-discharge all-cause and overdose mortality. In Aim 2, I will use the models to evaluate how many deaths an ACS has avoided since implementation. I will then predict deaths avoidable by ACS implementation in the next five years, and show how patients with OUD may be impacted by emerging external scenarios such as changes in fentanyl prevalence and COVID-19. This project will provide point estimates for the number of overdose and all-cause deaths avoided by an ACS, and avoidable in the future under varying conditions. This proposal builds on my work modeling substance use among hospitalized patients. Through this grant, I will gain skills as a health systems researcher, including advanced quantitative skills to support healthcare policy and innovation.