# Effectiveness of the Consult for Addiction Treatment and Care in Hospitals (CATCH) model for engaging patients in opioid use disorder treatment: Pragmatic trial in a large municipal hospital system > **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $669,981 ## Abstract Treatment for opioid use disorder (OUD) is highly effective, yet dramatically underutilized. Individuals with OUD are frequently hospitalized, but have poor health outcomes and low rates of addiction treatment involvement. Hospital-based addiction consult services have the potential to make a significant contribution to narrowing the OUD treatment gap. These multidisciplinary teams can start medication for addiction treatment (MAT) while patients are hospitalized, and directly link them to ongoing post-discharge outpatient treatment. Such addiction consult services are proliferating in response to the opioid epidemic, but with little research on their effectiveness or guidance on their implementation. There now exists a unique and time-limited opportunity to study an addiction consult model (Consult for Addiction Treatment and Care in Hospitals (CATCH)) as it rolls out in the largest municipal hospital system in the US. The overarching objective of our proposal is to evaluate the effectiveness of CATCH as a strategy for engaging patients with OUD in MAT. A pragmatic trial at 4 hospitals, conducted in collaboration with the NYC Health and Hospitals system (H+H) and the NYC Dept. of Health, will study the CATCH intervention in real-world settings and at scale. All intervention costs are borne by H+H. Guided by the RE-AIM evaluation framework, this hybrid effectiveness- implementation study (Type 1) focuses primarily on effectiveness, but also measures implementation outcomes inform the intervention's adoption and sustainability. A rigorous stepped-wedge cluster randomized trial design determines the impact of CATCH on opioid treatment outcomes in comparison to usual care for a control period, followed by a 12-month intervention period and a 12-18 month maintenance period, and utilizes existing administrative data to evaluate outcomes. Aim 1 (primary aim) is to evaluate the effectiveness of CATCH in increasing post-discharge initiation and engagement in MAT, defined respectively as receiving outpatient MAT within 14 days of discharge, and having at least 2 additional MAT visits in the first month. Aim 2 is to assess the effectiveness of CATCH for increasing treatment retention, defined as continuous receipt of MAT for 6 months. Aim 3 is to compare the frequency of acute care utilization and overdose deaths, and their associated costs, among patients with OUD who are hospitalized during the CATCH period versus usual care. Aim 4 is to evaluate implementation outcomes at CATCH sites using a mixed methods approach to assess the intervention's Reach (proportion of eligible patients reached); Adoption (utilization by medical staff); and Implementation fidelity (barriers to delivering high-quality MAT). This research will provide the first evaluation of an addiction consult model in a multi-site trial, and promises to generate knowledge that can rapidly transform practice and inform the intervention's potential for widespread dissemination, in NYC and nati... ## Key facts - **NIH application ID:** 9838737 - **Project number:** 5R01DA045669-03 - **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE - **Principal Investigator:** Jennifer McNeely - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $669,981 - **Award type:** 5 - **Project period:** 2018-03-15 → 2021-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9838737 ## Citation > US National Institutes of Health, RePORTER application 9838737, Effectiveness of the Consult for Addiction Treatment and Care in Hospitals (CATCH) model for engaging patients in opioid use disorder treatment: Pragmatic trial in a large municipal hospital system (5R01DA045669-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9838737. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*