EPINET Supported Projects

The National Institute of Mental Health awards grants to promote advances in early psychosis services and science. This database captures the volume of projects that have been supported by EPINET.

Hub	Project	Link	Project Number	Opportunity No.	Project Lead	Organization	Project Abstract
Connection LHS	Harnessing a Two-State FEP LHS to Optimize Engagement and Prevent Disengagement in CSC	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11190929	P01MH139228	RFA-MH-24-105	Melanie E. Bennett, Monica Calkins	University of Maryland Baltimore	This Program Project Grant entitled Harnessing a Two-State FEP LHS to Optimize Engagement and Prevent Disengagement in CSC will leverage Connection Learning Healthcare System (CLHS) - a two-state learning healthcare system and Early Psychosis Intervention Network (EPINET) hub (MH120550, Bennett PI) - to pursue integrated research and knowledge generation focused on optimizing Coordinated Specialty Care (CSC) engagement and preventing disengagement in youth with first episode psychosis (FEP). CLHS has a demonstrated track record of collecting, managing, and analyzing data from the EPINET Core Assessment Battery (CAB) from 23 CSC programs across Pennsylvania and Maryland. The close collaboration and combined expertise of investigators from the University of Maryland, the University of Pennsylvania, Johns Hopkins University, the University of Pittsburgh, and Sheppard Pratt Healthcare has supported practice-based data collection, hub-centered data analysis, and translation of data to practice through learning healthcare and culture building initiatives. The group of investigators has internationally renowned experience in research on schizophrenia, FEP, and CSC implementation, as well as in psychiatric services research, interventions development and implementation in psychosis, cognition and cognitive assessment; neuroimaging and brain functioning in schizophrenia, clinical trials, implementation science, psychopharmacology, culturally competent clinical care, participatory, and peer services research. This consortium has interacted and in a superbly collaborative manner sharing technical and scientific information through formal monthly face-to-face or Zoom-based meetings, frequent telephone or electronic communication and across-campus visits. This offers tremendous opportunities for in- depth study of disengagement risk and of strategies that can prevent it. The Administrative Core will build on the infrastructure of CLHS to support two well-specified research projects and to foster additional research, knowledge generation, and quality improvement initiatives focused on preventing CSC disengagement by providing services in data collection, management, and analysis; supporting learning healthcare system activities that integrate CSC program ideas into research development; and fostering involvement of participants with lived experience of psychosis and family members to make research on disengagement responsive to the experiences of people who are directly impacted by CSC. The Clinical Practice Data Research Project will use CLHS CAB data to develop and validate longitudinal models for predicting risk of CSC disengagement and to integrate stakeholder input on the clinical utility of using risk information in clinical practice. The Prospective Practice-Oriented Research Project to develop a hub-based engagement navigator service for supporting participants and families at high risk for disengagement. We will use robust participatory research methods to ensure the integration of CSC program staff, participants, and family members in the development of all aspects of and materials for the navigator service and conduct a mixed methods hub wide ENS evaluation using a hybrid type I open cohort stepped wedge design to examine feasibility, acceptability, and effectiveness in changing target mechanisms and improving disengagement outcomes.
Connection LHS	Project 1 - Clinical Practice Data Research Project	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11190937	6554	RFA-MH-24-105	Melanie E. Bennett, Monica Calkins	University of Maryland Baltimore	Numerous studies have demonstrated the effectiveness of coordinated specialty care (CSC) for the treatment of people with a first episode of psychosis (FEP). However, there is a major problem of patient disengagement, which has adversely affected the impact of CSC on the long-term course of FEP. The lack of an individualized, empirically developed approach to assess risk of disengagement for a given patient has compromised the ability of the field to address this issue. Consistent with the transformative movement towards precision medicine, individualized risk calculators for many medical conditions have been developed, validated and applied in clinical settings. On the heels of these successes, the mental health field has seen a proliferation of research aiming to develop and validate risk- prediction models for various mental health conditions, however research that fosters ethical, practical and clinically useful implementation of risk prediction in applied settings has lagged behind. Our primary objective is to develop, validate, and lay the groundwork for implementing a risk calculator for CSC program outcomes. A precision psychiatry approach to enhancing retention in CSC programs is a logical and necessary next step to advance this field and optimize CSC client outcomes. Our central hypothesis is that a calculator to predict personalized CSC program outcome will allow us to identify participants most likely to disengage from care. Our approach rests on our extensive experience: (a) implementing and analyzing the hub-wide and national Core Assessment Battery (CAB), (b) developing and validating risk-prediction models in related contexts, (c) demonstrating feasibility via the preliminary analyses of our admission CAB data that provide support for risk prediction with moderate accuracy, and (d) weighing ethical and practical issues related to risk prediction. Our long-term goal is to develop a personalized medicine approach to identify and rapidly address risk for disengagement and retain individuals in CSC to optimize its benefit. This Prospective Practice-Oriented Research Project entitled Developing and Validating Models to Predict Risk of CSC Disengagement proposes to use CLHS CAB data to develop and validate longitudinal models for predicting risk of CSC disengagement and to integrate stakeholder input on feasibility, acceptability, utility, facilitators, and barriers to using risk information in clinical practice. We will develop several versions of a risk calculator predicting length of time in program, program completion, and disengagement (Aim 1) and establish longitudinal validity (2-3 years) to compare different calculators predictive accuracy (Aim 2). We will leverage the ACs participatory research resources to develop implementation strategies for using risk information in practice (Aim 3).
Connection LHS	UMB Admin Core - Administrative Core	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11190930	6553	RFA-MH-24-105	Melanie E. Bennett, Monica Calkins	University of Maryland Baltimore	This Program Project Grant (PPG) entitled Harnessing a Two-State FEP LHS to Optimize Engagement and Prevent Disengagement in CSC is supported by an Administrative Core to provide resources and maintain the communication needed to coordinate the research and financial activities being conducted in the five institutions the University of Maryland, the University of Pennsylvania, Johns Hopkins University, the University of Pittsburgh, and Sheppard Pratt Healthcare that are participating in this research. The Administrative Core is directed by Drs. Melanie Bennett and Monica Calkins. The Administrative Core is located at the University of Maryland School of Medicine in the Department of Psychiatry, Division of Psychiatric Services Research. The focus of the Administrative Core is the successful implementation of the program goals to support a coordinated research program focused on optimizing Coordinated Specialty Care (CSC) engagement and preventing disengagement in youth with first episode psychosis (FEP).The Administrative Core is integral to the overall goals of this PPG, particularly regarding synergy among projects. The AC and the Research Projects are highly inter-related and each interacts with the others. The Administrative Core will maintain a stable, flexible, yet centralized infrastructure to promote and coordinate multi-disciplinary research in promoting engagement involving experts in psychiatric services research, interventions development and implementation in psychosis, cognition and cognitive assessment, clinical trials, implementation science, and participatory research. The leadership team and administrative support described herein has proven to be very effective and flexible working together during the current Early Psychosis Intervention Network (EPINET) project (MH120550).
Connection LHS	Project 2 - Prospective Practice-Oriented Research Project	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11190939	6555	RFA-MH-24-105	Melanie E. Bennett, Monica Calkins	University of Maryland Baltimore	Coordinated Specialty Care (CSC) disengagement is alarmingly common and compromises the vision of CSC to support recovery in first episode psychosis Few efforts are focused on developing services to prevent disengagement that do not require additional training and implementation burden of community-based CSC programs. The Early Psychosis Intervention Networks (EPINET) hub-based structure offers an opportunity to circumvent program-level implementation barriers by developing and implementing systems-level strategies to address disengagement. One strategy employs a centralized service, staffed by dedicated professionals working across a hub, to (1) receive and organize referrals, (2) complete ongoing specialized training in engagement science and evidence-based strategies, (3) provide unbiased support when participants and/or families relationships with a CSC program are poor, (4) re-engage participants in CSC or actively connect them with other services if preferred, and (5) continue contact with those who refuse treatment in case they reconsider. Currently, no hub-based strategy exists to address disengagement. The primary objective of this project is to use participatory research methods within a learning health system research framework to develop and evaluate a hub-based Engagement Navigator Service (ENS) to prevent CSC disengagement. Our central hypothesis is that, once developed, ENS will support more months in treatment and lower disengagement rates than usual care (UC). Our long-term goal is to co-produce a hub-based navigator service to reduce disengagement. We will attain our objectives via the following specific aims: This Prospective Practice-Oriented Research Project entitled Developing and Evaluating a Hub-Based Engagement Navigator Service to Reduce CSC Disengagement proposes to develop a hub-based engagement navigator service (ENS) for participants and families at high risk for disengagement. We will use robust Participatory Research methods to ensure integration of CSC program staff, participants, and family members in developing all aspects of and materials for ENS (Aim 1). We will conduct mixed methods feasibility/acceptability testing in three CSC programs and use this information to guide ENS refinements (Aim 2). This will be followed by a mixed methods hub wide evaluation using a hybrid type I open cohort stepped wedge design to examine feasibility, acceptability, and effectiveness to improve disengagement outcomes and target mechanisms (Aim 3).
LEAP	Laboratory for Early Psychosis Research (LEAP)	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11179451	P50MH115846	PAR-18-701	Dost Ongur, Miguel Hernan, John Hsu	McLean Hospital	We propose to continue our existing NIMH P50 ALACRITY Center (Laboratory for Early Psychosis or LEAP Center). Although coordinated specialty care (CSC) is standard of care for patients with first episode psychosis (FEP), there remain gaps in our knowledge base regarding selection and deployment of treatments. The LEAP Center has been addressing these gaps through development of a statewide CSC clinic system in Massachusetts, standardized data collection and participation in the EPINET consortium, and adapting methods developed outside of mental health. In the first LEAP Center period, we have generated strong interdisciplinary collaborations between clinicians, policy makers/regulators, and stakeholders, and FEP experts from across the country plus scientific experts from outside of mental health, e.g., in data science, machine learning, epidemiology, and health policy. In the next period, we will leverage these collaborations to conduct a randomized clinical trial and to access diverse data sources to examine the role of specific treatment interventions in FEP. We also will apply and develop modern methods for clinical prediction and comparative effectiveness research (CER) for FEP research. We have three overall Center aims: 1) Collaborations; 2) Clinical research; and 3) Prediction and CER methods. The Center will include four complementary projects: 1) Project 1 is a signature project to conduct a cluster randomized clinical trial of enhanced CSC. 2) Project 2 will apply causal inference techniques on FEP-CAUSAL, an international data platform; 3) Project 3 will leverage EPINET national registry data to estimate the effect of treatment exposures studied in Project 1; 4) Project 4 will compare CSC care with non-CSC care at the state level using both electronic health record and statewide administrative datasets. The Administrative Core and Methods Core bind these projects and the multiple types of data (i.e., experimental data and observational data from electronic health records, national registries, or administrative datasets) and analytic approaches together. We have a group of Scientific Advisors, Center Faculty from diverse scientific disciplines (e.g., those that historically have had limited mental health research exposure), and Stakeholders (e.g., policy makers, organizational leaders, and patient and family advocates) who will be deeply involved in the Center from design to dissemination. The overall goal is to continue the evolution towards a state-wide learning health system for early psychosis which started in the first LEAP Center period and to continue to support NIMH-sponsored EPINET learning health system consortium.
LEAP	Comparative effectiveness of pharmacologic strategies to treat first episode psychosis	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11179458	9448	PAR-20-293	Miguel Hernan	McLean Hospital	Antipsychotic medications are a cornerstone of care for FEP patients because they reduce the risk of relapse and hospitalization. Therefore, strategies to enhance adherence to antipsychotics are an urgent need in psychiatric practice. Ideally, antipsychotic medications would be studied in randomized trials. However, progress in the management of FEP cannot rely exclusively on randomized trials, which cannot answer all clinically relevant questions. Because randomized trials cannot realistically answer all questions about the effectiveness of early FEP interventions in all clinical populations and for all outcomes in a timely way, the findings from randomized trials need to be complemented with those from observational studies. Causal inference from observational data can be viewed as an attempt to emulate a hypothetical pragmatic randomized trialthe target trial. We will use data from the FEP-CAUSAL Collaboration to emulate target trials of initiation of oral and long-acting injectable antipsychotic medications. To overcome concerns about confounding due to noncomparability of individuals in different treatment groups, we will conduct our observational analyses in two steps. First, we will identify the observational data required to replicate findings from two flagship randomized clinical trials in this area: the EUFEST and PRELAPSE trials. This benchmarking of the observational effect estimates to existing randomized trial estimates allows to calibrate, and increase confidence in, the observational analyses. Second, we will emulate target trials that extend the results from previous trials for the management of individuals with FEP. Specifically, we will study the risks of nonadherence, relapse, and hospitalization over longer follow-up periods in adolescents and adults under different treatment strategies after FEP diagnosis. The findings from these analyses will help guide the choice of antipsychotics to enhance adherence and clinical outcomes. This Project 2 also complements the findings from the randomized trial in Project 1 using the FEP-CAUSAL Collaboration, an international consortium of prospective cohorts of individuals with FEP that is coordinated by LEAPs Methods Core. Our target trial emulations will pioneer the implementation of causal inference methodology to observational prospective cohorts in FEP. The superiority of this methodological approach has been demonstrated in several areas of medicine but not yet in psychiatry. We expect that our work will result in methodological advances and in case studies that will be generally applicable across many areas of mental health research.
LEAP	LEAP Methods Core	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11179454	9446	PAR-20-293	Miguel Hernan	McLean Hospital	The Methods Core of the LEAP Center will continue to work on a data platform that integrates information obtained at different levels of care of First Episode Psychosis (FEP) patients and will develop the computational tools that will allow Center investigators to access and analyze the integrated databases. Specifically, the Methods Core will provide the methodological expertise for the application of state-of-the-art machine leaning algorithms for clinical prediction, and for the application of cutting-edge causal inference techniques for comparative effectiveness research. First, the Methods Core will provide the database infrastructure to securely store, harmonize, link, manage, and analyze the high-dimensional databases that will be used by Center investigators. These databases include detailed clinical, demographic, socioeconomic information for each FEP patient, plus randomized controlled trial data, electronic health records and insurance claims, and longitudinal datasets on clinics characteristics and services offered. Many of these data sources have never been used for mental health research, either independently or in concert. We will create the only U.S.-based consortium approaching a thousand FEP patients. Second, the Methods Core will ensure that recent advances in clinical prediction and comparative effectiveness research can be applied to large databases of FEP patients and will be an incubator of methodological research in response to the requirements of the Center projects. It will serve as a platform for a synergistic collaboration between experts in several disciplinespsychiatry, statistics, epidemiology, and health policywho will engage in high-impact studies to improve the clinical outcomes of FEP patients. Our project also provides a potential model for data consolidation and expertise sharing across multiple NIMH Alacrity P50 Centers. In summary, the Methods Core will support the data analysis activities throughout all projects, and will disseminate methodological advances amongst the community of mental health researchers and will therefore function as a national resource that facilitates the use of innovative methods beyond the Center investigators.
LEAP	Randomized controlled trial of enhanced coordinated specialty care (CSC 2.0)	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11179455	9447	PAR-20-293	Dost Ongur	McLean Hospital	Trial and meta-analytic evidence support early intervention after a first episode of psychosis (e.g., through coordinated specialty care [CSC]) to improve patient outcomes, but clinics often face real-world challenges in delivering components of this care and have difficulty retaining patients in treatment. This disconnect between the evidence suggesting CSC efficacy and the real-world challenges lead to worse outcomes for patients that one might expect given the trial data, i.e., effectiveness falls below efficacy. The challenges include staffing shortages and allocation inefficiency, particularly with smaller scale clinics. Moreover, some patients need additional services, e.g., cognitive remediation. The LEAP Center signature project will be a cluster-randomized controlled trial (RCT) of enhanced coordinated specialty care (CSC 2.0) compared to standard, real-world CSC in a network of clinics in Massachusetts which together will serve as a research laboratory for testing improvements to CSC. The CSC 2.0 intervention includes peer providers, digital outreach, family groups, coordination with ED/inpatient providers/PCPs, and cognitive remediation, all organized using a centrally managed hub-and-spoke design. We selected these components based on clinic feedback about existing fidelity challenges and patient needs and because of their promise to improve patient engagement/retention in CSC, a critical mechanism for improving outcomes. The trial will randomize 350 patients by clinic*month using a scheme that both will split evenly the sample and blind clinic staff to the randomization process. The control arm consists of usual care within the clinics, which we will track. The intervention arm uses a hub-and-spoke model deploying the intervention to all clinics, often delivering care remotely for both clinician and patient. Because follow-up losses are most pronounced during the first year of care, our primary outcome will be the number of months (0-12) during which the patient attended the expected number of CSC appointments. The trial will leverage existing electronic health record data and EPINET registry data, maximizing data collection efficiency. We have developed and refined these types of data during the first funding period. The trial will leverage the efforts of the Administrative and Methods Cores as well as the other projects, which in turn will complement and extend the trial capabilities. In sum, this project will provide clinicians, patients, and policy makers rigorous experimental data on the impact of an enhanced CSC delivery approach compared with the usual care offered by CSC clinics with all of their real-world challenges.
LEAP	LEAP Administrative Core	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11179452	9445	PAR-20-293	Dost Ongur	McLean Hospital	The Administrative Core for the Laboratory in Early Psychosis Center, a.k.a. LEAP Center, will support continued activities of the currently existing Center. This involves the research consortium of first episode psychosis clinics in Massachusetts and an interdisciplinary team of leading researchers. The LEAP Center is dedicated to research in first episode psychosis, including testing an intervention strategy in our consortium of clinics in a randomized controlled trial, extensive multimodal longitudinal data collection, and prediction and comparative effectiveness research using various clinical datasets to better understand heterogeneity in patient outcomes and treatment impacts. The Administrative Core will serve the Centers goals by facilitating regular communication, fostering synergy among disciplines, supporting training opportunities and pilot projects, disseminating information on Center activities, organizing stakeholder panels, and overseeing evaluation of the Center itself. The Core will be led by the Center Leads Drs. Öngür, Hsu, and Hernan, and supported by a Center Administrator. It will be housed in dedicated office space at McLean Hospital. The Core has four aims. First, it will oversee and coordinate all activities essential to the functioning of the Center, including maintenance of regulatory and contractual obligations, management of budgets and subcontracts, interacting with academic, state, and federal agencies, completion of reporting requirements, supporting regular communication between Center elements, and fostering and tracking collaborations. Second, the Core will oversee and coordinate governance structures including the Steering, Manuscript, Data Management and Access, Pilot Project, and Training committees within the Center. Third, it will manage the solicitation, review, funding, monitoring, and reporting of the conduct of an average of two R03-level pilot projects each year. And fourth, the Administrative Core will coordinate and manage engagement with a diverse array of stakeholders including patient and family groups, national and state policy makers, payers, as well as a panel of senior scientific experts. The Core will also support dissemination of Center activities and advances through conferences and short courses, visiting scholar series, clinical case conferences, as well as a website. Finally, the Administrative Core will track objective metrics of Center performance and convene annual meetings of the Advisory Board (composed of senior investigators within and outside of psychiatry research which will conduct assessments of Center progress and suggest refinements). Taken together, the Core activities are designed to ensure that the LEAP Center remains a unified whole that is greater than the sum of its parts.
LEAP	LEAP Methods Core	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/9928116	7131	PAR-18-701	Miguel Hernan	McLean Hospital	The Methods Core of the LEAP Center will create a data platform that integrates information obtained at different levels of care of First Episode Psychosis (FEP) patients and will develop the computational tools that will allow Center investigators to access and analyze the integrated databases. Specifically, the Methods Core will provide the methodological expertise for the application of state-of-the-art machine leaning algorithms for clinical prediction, and for the application of cutting-edge causal inference techniques for comparative effectiveness research. First, the Methods Core will provide the database infrastructure to securely store, harmonize, link, manage, and analyze the high-dimensional databases that will be used by Center investigators. These databases include detailed clinical, demographic, socioeconomic information for each FEP patients, plus electronic health records and insurance claims, and longitudinal datasets on clinics characteristics and services offered. Many of these data sources have never been used for mental health research, either independently or in concert. We will create the only U.S.-based consortium approaching a thousand FEP patients. Second, the Methods Core will ensure that recent advances in clinical prediction and comparative effectiveness research can be applied to large databases of FEP patients and will be an incubator of methodological research in response to the requirements of the Center projects. It will serve as a platform for a synergistic collaboration between experts in several disciplinespsychiatry, statistics, epidemiology, and health policywho will engage in high-impact studies to improve the clinical outcomes of FEP patients. Our project also provides a potential model for data consolidation and expertise sharing across multiple NIMH Alacrity P50 Centers. In summary, the Methods Core will support the data analysis activities throughout all projects, and will disseminate methodological advances amongst the community of mental health researchers and will therefore function as a national resource that facilitates the use of innovative methods beyond the Center investigators.
LEAP	Examining Pathways to Care	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/9928118	7132	PAR-18-701	John Hsu	McLean Hospital	Project 1 will examine the impact of increasing availability of coordinated specialty care (CSC) for patients in their first episode of psychosis (FEP). The growing clinical evidence base supporting CSC, combined with recent federal/state policy changes, has led to increased financing and availability of CSC care in the United States. There is, however, limited information on the impact of these changes on patients or clinical care, and limited data to guide future policy decisions. Moreover, these changes are occurring within a highly fragmented and under-resourced delivery system for mental health care. For example, in many parts of the United States, there are few or no psychiatrists in the area, much less mental health clinics that could implement CSC. Recent federal increases in FEP financing help address these needs, but the funds are limited and future investment decisions will need to be judicious. To address these gaps, we propose to use population level information from multiple sources, starting with the Massachusetts All Payer Claims Database (APCD), supplemented with state data on FEP clinics, and other area-level information. We will apply state-of-the-art statistical approaches for making causal inferences about the growth in CSC availability and our study outcomes. We have three study aims: 1) Differential access we will compare the characteristics and history of patients receiving care in clinics offering CSC versus elsewhere; 2) Outpatient care estimation of the effect of receiving care in a clinic offering CSC on outpatient process measures, e.g., time to follow-up care, assessment of lipid status, and adherence to antipsychotic drug therapy; and 3) Unfavorable clinical events examination of the effects on emergency department visits or hospitalizations. Our approach addresses several major gaps in the literature by including as much as ten years of follow-up after diagnosis, leveraging data on tens of thousands of newly diagnosed patients (thereby having adequate power to estimate changes precisely), and having near complete follow-up on all subjects (through the state APCD). This information will supplement existing knowledge from prior clinical trials, support the other projects in this NIMH P50 LEAP Center, and help frame the potential generalizability of information gained from advanced mental health clinics. This information could help inform federal and state policy as well as decisions by patients, clinicians, and organizational decision-makers.
LEAP	Enhancing the Data Science Capabilities	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/9928119	7133	PAR-18-701	Dost Ongur	McLean Hospital	Patients with a first episode of psychosis (FEP) often have substantial amounts of morbidity during the months and years after onset, and elevated mortality in later years. Growing amounts of evidence suggest that early treatment might help; several recent national and state policies have increased focus, shifted priorities, and provided additional resources to enhance FEP care, yet important gaps remain in the clinical knowledge base. This project helps build the data foundation for addressing these gaps, and leverages the recently launched Massachusetts Department of Mental Health (DMH) Center of Excellence in Early Psychosis (CoE). This state- led effort is mandating standardized data collection on the delivery structure, processes, and outcomes of FEP care, funding the data collection, and creating a process for improving data collection over time. Funding for this work comes from recent SAMHSA policy directives concerning the state Mental Health Block Grants. In close collaboration with DMH, this project will address three aims: 1) Synthesize the longitudinal data collected by the state, including reviewing the data quality and altering the state data collection processes if needed; 2) Validate care delivery measures that form the basis of clinical feedback and research; and 3) Integrate multiple dimensions and perspectives of care outcomes. The structural data include information on clinic staffing and CSC fidelity; the clinical data include measures on symptomatic stability, cognitive function, safety-related outcomes, symptomatic relapse, and community function; the claims data come from the APCD used in Project 1. Working with the Methods Core, Project 2 will pay special attention to inherent challenges in data collection in FEP research such as complexity of phenotypes and patterns of missingness (i.e. random vs. systematic). Project 3 will result in validated, longitudinal data on patient phenotypes, which we will use in Project 3 to examine clinical heterogeneity, and in pilot studies described in the Administrative Core. The protocols and measures developed will inform state and state efforts to enhance data collection in FEP research. In sum, this project takes creates the foundation through which large amounts of raw data collected by the state become the basis of research that converts these data into useful clinical and policy knowledge.
LEAP	ConProject-003	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/9999215	8553	PAR-18-701	Dost Ongur, Miguel Hernan, John Hsu	McLean Hospital	We propose to create a NIMH P50 ALACRITY Center that develops the collaborations, data systems, and methods necessary to form a state-wide Laboratory for Early Psychosis Research, aka the LEAP Center. While recent trial evidence suggests great promise for coordinated specialty care (CSC) for patients with first episode psychosis (FEP), there remain several gaps in the knowledge base for the care of psychosis in general. Now is an ideal time to address these gaps because of recent policy changes enhancing FEP care financing, practice changes in the field, and methods developed outside of mental health. To create this Center, we will start with interdisciplinary collaborations between CSC clinics in Massachusetts, policy makers/regulators, and stakeholders, and include FEP experts from across the country plus scientific experts from outside of mental health, e.g., in data science, machine learning, epidemiology, and health policy. We also will leverage recent Massachusetts efforts to mandate, standardized, and support data collection on FEP care structures, delivery, and outcomes from all FEP clinics within the state. We also will apply and develop modern methods for clinical prediction and comparative effectiveness research (CER), e.g., machine learning and g-methods, for FEP research. Accordingly, we have three overall Center aims: 1) Collaborations; 2) Data systems; and 3) Prediction and CER methods. The Center will start with three foundational and complementary projects: 1) Using the state All Payer Claims Database (APCD), Project 1 will apply a population-level approach to examine which patients receive care in FEP clinics offering CSC versus elsewhere, as the number of clinics increases, then estimate the treatment effect on unfavorable clinical event rates such as hospitalizations; 2) Project 2 will review the data collected by the state from all of the FEP clinics, assess and improve data collection quality, validate measures, and integrate perspectives; and 3) Project 3 will define clusters of patients using longitudinal outcome data (i.e., begin to unpack the amount of clinical heterogeneity), predict the cluster type for individual patients, and examine the impact of CSC treatment accounting for this clinical heterogeneity. The Administrative Core and Methods Core (Prediction and CER) bind these projects together. The Center will include a group of Scientific Advisors from across North America, Center Faculty from diverse scientific disciplines, including those that historically have had little exposure in mental health research, and Stakeholders, including policy makers, organizational decision makers, and patient and family advocates. All three groups will be deeply involved in the Center from design to dissemination. The overall goal is to create a state-wide learning health system for early psychosis. This effort to create the protocols and apply the methods necessary to convert large amounts of data into useful clinical and policy knowledge will inform other national efforts, e.g., NIMHs EPINET.
LEAP	LEAP Administrative Core	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/9928115	7130	PAR-18-701	Dost Ongur	McLean Hospital	The Administrative Core for the Laboratory in Early Psychosis Center, a.k.a. LEAP, will support the creation of a research consortium spanning three major first episode psychosis clinics in Massachusetts and the formation of a transdisciplinary team of leading researchers. The LEAP Center is dedicated to T2 translational research in first episode psychosis, including analysis of the impact of specialized care, extensive multimodal longitudinal data collection, and prediction and comparative effectiveness research to better understand heterogeneity in patient outcomes and treatment effects. The Administrative Core will serve the Centers goals by facilitating regular communication, fostering synergy among disciplines, supporting training opportunities and pilot projects, disseminating information on Center activities, organizing stakeholder panels, and overseeing evaluation of the Center itself. The Core will be led by the Center Leads Drs. Öngür, Hsu, and Hernan, and supported by a Center Administrator. The Core has four aims. First, it will oversee and coordinate all activities essential to the functioning of the Center, including maintenance of regulatory and contractual obligations, management of budgets and subcontracts, interacting with academic, state, and federal agencies, completion of reporting requirements, supporting regular communication between Center elements, and fostering and tracking collaborations. Second, the Core will oversee and coordinate governance structures including the Steering, Manuscript, Data Management and Access, Pilot Project, and Training committees within the Center. Third, it will manage the solicitation, review, funding, monitoring, and reporting of the conduct of an average of two R03-level pilot projects each year. And fourth, the Administrative Core will coordinate and manage engagement with a diverse array of stakeholders including patient and family groups, national and state policy makers, payers, as well as a panel of senior scientific experts. The Core will also support dissemination of Center activities and advances through conferences and short courses, visiting scholar series, clinical case conferences, protocols and software, as well as a website intended to serve the general public. Finally, the Administrative Core will track objective metrics of Center performance and convene annual meetings of the Advisory Board (composed of senior investigators within and outside of psychiatry research which will conduct assessments of Center progress and suggest refinements) as well as convene regular meetings of the Centers Faculty Panel, and Stakeholder Committees. Taken together, the Core activities are designed to ensure that the LEAP Center becomes a unified whole that is greater than the sum of its parts.
LEAP	ConProject-004	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/9999216	8554	PAR-18-701	Dost Ongur, Miguel Hernan, John Hsu	McLean Hospital	We propose to create a NIMH P50 ALACRITY Center that develops the collaborations, data systems, and methods necessary to form a state-wide Laboratory for Early Psychosis Research, aka the LEAP Center. While recent trial evidence suggests great promise for coordinated specialty care (CSC) for patients with first episode psychosis (FEP), there remain several gaps in the knowledge base for the care of psychosis in general. Now is an ideal time to address these gaps because of recent policy changes enhancing FEP care financing, practice changes in the field, and methods developed outside of mental health. To create this Center, we will start with interdisciplinary collaborations between CSC clinics in Massachusetts, policy makers/regulators, and stakeholders, and include FEP experts from across the country plus scientific experts from outside of mental health, e.g., in data science, machine learning, epidemiology, and health policy. We also will leverage recent Massachusetts efforts to mandate, standardized, and support data collection on FEP care structures, delivery, and outcomes from all FEP clinics within the state. We also will apply and develop modern methods for clinical prediction and comparative effectiveness research (CER), e.g., machine learning and g-methods, for FEP research. Accordingly, we have three overall Center aims: 1) Collaborations; 2) Data systems; and 3) Prediction and CER methods. The Center will start with three foundational and complementary projects: 1) Using the state All Payer Claims Database (APCD), Project 1 will apply a population-level approach to examine which patients receive care in FEP clinics offering CSC versus elsewhere, as the number of clinics increases, then estimate the treatment effect on unfavorable clinical event rates such as hospitalizations; 2) Project 2 will review the data collected by the state from all of the FEP clinics, assess and improve data collection quality, validate measures, and integrate perspectives; and 3) Project 3 will define clusters of patients using longitudinal outcome data (i.e., begin to unpack the amount of clinical heterogeneity), predict the cluster type for individual patients, and examine the impact of CSC treatment accounting for this clinical heterogeneity. The Administrative Core and Methods Core (Prediction and CER) bind these projects together. The Center will include a group of Scientific Advisors from across North America, Center Faculty from diverse scientific disciplines, including those that historically have had little exposure in mental health research, and Stakeholders, including policy makers, organizational decision makers, and patient and family advocates. All three groups will be deeply involved in the Center from design to dissemination. The overall goal is to create a state-wide learning health system for early psychosis. This effort to create the protocols and apply the methods necessary to convert large amounts of data into useful clinical and policy knowledge will inform other national efforts, e.g., NIMHs EPINET.
EPINET-Affiliated	Impact of cannabis legalization and commercialization on substance use and mental health outcomes in psychosis	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11245739	K23DA062173	PA-20-205	Andrew Hyatt	Cambridge Health Alliance	Legalization of cannabis and subsequent expansion of commercial cannabis markets are spreading rapidly across the United States. While many can use cannabis without significant harm, individuals with serious mental illnesses such as schizophrenia and other psychotic disorders are disproportionally likely to experience harms from cannabis use, and cannabis commercialization could worsen outcomes for this population. As an early career psychiatrist and health services researcher with specialty interests in addictions and psychosis, I am proposing to use quasi-experimental difference in difference methods to study the effects of commercialization on cannabis use and mental health outcomes among individuals with psychosis. I am first proposing to analyze the Population Assessment of Tobacco and Health (PATH) study to probe the effects of the opening of commercial cannabis markets on cannabis and other substance use among individuals with psychosis (Aim 1). Next, I will utilize data from an NIH funded consortium of first episode psychosis clinics, the Early Psychosis Intervention Network (Epinet), to assess the effect of commercialization on substance use, psychosis relapse (emergency department visits and psychiatric hospitalizations), and secondary outcomes including criminal legal involvement among youth and young adults with early psychosis (Aim 2). Triple difference methodologies will be utilized in each analysis to measure whether commercialization is linked to differences in outcomes, both between individuals with psychosis and the general population (Aim 1) and between individuals with factors associated with better and poorer outcomes once diagnosed with psychosis (Aim 2). In the proposed training plan, I will develop expertise in: 1) substance use disorder research, policy, and implementation, 2) statistical methods for causal inference including difference in difference designs, and 3) rigorous analysis of differences in health outcomes across populations to inform clinical and policy efforts to improve outcomes for vulnerable populations. The research will take place at the Cambridge Health Alliance, a public sector teaching hospital of Harvard Medical School, within the Health Evaluation Research Lab which has deep experience in health policy research in large datasets. In line with NIDAs strategic plan, this proposal will study the effects of local, state, and national drug policies on public health (Goal 1.3 2022-26) and will lay the foundation for my development into a leading health services and policy researcher straddling clinical and public health realms to develop substance use policies that maximize benefits and minimize harms for vulnerable populations.
ESPRITO	Early-Phase Psychosis: Practice-Based Research to Improve Treatment Outcomes	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11063670	P01MH139136	RFA-MH-24-105	Delbert Robinson	Feinstein Institute for Medical Research	Our group has a long-standing commitment to improving care for individuals with first episode psychosis (FEP), e.g. we developed the NAVIGATE coordinated specialty care (CSC) treatment model for the RAISE-ETP study. The current EPINET project allowed us to improve FEP care within a learning health system (LHS) framework. Our current ESPRITO EPINET has enrolled 963 participants. Our proposed ESPRITO EPINET will enroll an additional 625 participants from 13 clinics in 6 states. All sites provide care with the NAVIGATE model, serve diverse populations and except for one are community facilities without any academic affiliations. Moving forward, the ESPRITO central team will continue to provide sites the successful supports developed in the current ESPRITO for LHS and research project recruitment, retention, obtaining core assessment battery (CAB) assessments, data management including provision of CAB data to the EPINET Data Coordinating Center. NAVIGATE treatment and the LHS model are based upon measurement-based care and we will continue on-going training to site personnel on assessment and the use of assessments to improve care. For the proposed work, we will expand the range of dashboards and means to convey information to sites. Our statistical analyses include innovations both in the methods that will be used and the incorporation of visualization tools to communicate the results to clinicians who may have limited statistical expertise. A novel measure will be used to evaluate social determinants of health (SDoH) to further understand site diversity. Combining data from the current ESPRITO project and the proposed work will also permit analyses of subpopulations of interest (e.g., those of Hispanic ethnicity). To enhance sites ability to improve care the proposed work includes a learning collaborative about overall treatment issues and another about enhancing patient participation in the LHS and in NAVIGATE treatment. We will also mentor the next generation of LHS/CSC research and clinical leaders. We will also perform 2 research studies. 1) Determine clinical characteristics of individuals who disengage from the LHS or from NAVIGATE treatment. Using data that can be obtained during clinical care (e.g. the Core Assessment Battery) we will characterize patient participants who disengage at two levels: those who enroll in the LHS but do not participate in the data collection needed for measurement-based care and those who prematurely terminate all CSC treatment. 2) Determine the utility at real world clinics of automated speech and language analysis to identify participants at risk for treatment disengagement or for relapse and hospitalization. Automated speech and language analysis has the potential to provide sites important information about patient status beyond what is obtained by usual clinical measures. In addition to the 2 defined projects, we will also collaborate with other EPINET hubs via the EPINET research consortium and development of collaborative research projects.
ESPRITO	Early-Phase Psychosis: Practice-Based Research to Improve Treatment Outcomes-Administrative Core	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11063671	6080	RFA-MH-24-105	Delbert Robinson	Feinstein Institute for Medical Research	The overall goal of the ESPRITO Administrative Core is to support and improve CSC care and outcomes for LHS participants. The Core activities build on the success of the current ESPRITO in LHS enrollment (963) and CAB completion (2,600). It will provide the home for a range of critical CSC and LHS functions. It will be the base for leadership and linkage to the ENDCC and NIMH EPINET. The Program Project PI, Delbert Robinson will serve as PI of the Core and will serve on the EPINET Research Consortium. An Executive Committee will develop policy and review ongoing progress quarterly. A Core Operations Team led by Dr. Robinson will include the leadership of Research Projects and other Key Personnel. It will meet biweekly. This will allow the group to provide close coordination with the Operations Group led by Patricia Marcy that is responsible for direct interaction with our 13 sites and all reporting to the ENDCC that includes CABs, raw data and data analyses from our Research Projects. The Operations Group will also work to link site clinicians to dashboards developed by Clinical Informatics that are tailored to support measurement-based care with NAVIGATE, the CSC that all ESPRITO sites provide. This structure is designed to address EPINET requirements and profits from the extensive experience of the ESPRITO team in research with First Episode Psychosis (FEP), research and delivery of services in community clinical settings that provide CSC and almost five years of work in the current EPINET initiative. Our Prospective Practice-Oriented Research Project: Predicting hospitalization and disengagement with automated speech and language analysis and our Clinical Practice Research Project: Enhancing Patient Engagement in LHS Participation and in CSC Treatment are linked by providing complementary approaches to addressing disengagement. They also depend on the Administrative Core. For our automated speech and language analysis, the Operations Group will manage participant recruitment and retention as well as data collection. The Clinical Practice Research Project to enhance patient engagement takes advantage of CAB data and iterative interaction with sites and NAVIGATE clinicians that requires active engagement with the Operations Group. The Administrative Core also includes a statistics group that will apply innovative approaches to analyses of CAB data that are designed for communication to clinicians to support improved outcomes. ESPRITO sites span the US and are also diverse in terms of social determinants of health. We will employ multiple statistical approaches to understanding this diversity. Finally, Administrative Core leadership will use multiple methods including a learning collaborative to develop and nurture the next generation of leaders in providing CSC services and the future research that will be needed to ensure that the LHS model can continue to evolve and improve.
ESPRITO	Clinical Practice Data Research Project: Enhancing Patient Engagement in LHS Participant and in CSC Treatment	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11063672	6081	RFA-MH-24-105	Delbert Robinson	Feinstein Institute for Medical Research	Although coordinated specialty care (CSC) treatment compared with standard care for first episode psychosis (FEP) improves retention in treatment, treatment disengagement remains a major clinical challenge even with CSC care. Premature treatment disengagement limits individuals with FEP from receiving the full benefits of CSC care. A key component of EPINET learning health systems is use of data to improve outcomes for individuals with FEP. The main data source is the Core Assessment Battery (CAB) that all EPINET sites use. The CAB contains clinician-reported and patient-reported outcomes (PROs). Across the EPINETS, a substantial number of participants do not do PROs assessments, thus limiting sites ability to understand participants perceptions of their treatment and outcomes. Using data from our current ESPRITO EPINET project and data from the proposed ESPRITO project we will use CAB data to characterize participants who disengage either from doing PROs assessments or from CSC treatment. For the proposed work, we will supplement CAB data with data on participant perception of how burdensome it is to fulfill the requirements of treatment and qualitative interviews. We will also examine whether disengagement in PROs assessments is related to treatment disengagement. Information from the analyses will be used in a learning collaborative focused upon enhancing engagement in PROs assessment and treatment retention. The study results have potential benefits to participants through enhanced engagement in treatment, to sites by enhancing PRO assessments and thus understanding of participants perceptions and to the field in general by increased understanding of factors associated with missing data in the de-identified ESPRITO data that will be available in the NIMH National Data Archive.
ESPRITO	Prospective Practice-Oriented Research Project: Predicting hospitalization and disengagement with automated speech and language analysis	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11063673	6082	RFA-MH-24-105	Sunny Xiaojing Tang	Feinstein Institute for Medical Research	The goal of this prospective practice-oriented project is to develop a means for predicting rehospitalization and disengagement in coordinated specialty care (CSC) for psychosis. CSC has demonstrated efficacy in improving outcomes, yet challenges persist with rehospitalization and treatment disengagement. There is an urgent need for predictive tools that are scalable and efficient. Automated speech and language analysis holds several advantages: they are accurate markers of mental states related to psychosis, assessments can be conducted repeatedly and longitudinally, and little specialized equipment or training is required to collect necessary speech samples. The study will be conducted within the ESPRITO hub of the EPINET consortium, drawing upon the expertise of a multidisciplinary team in psychosis, early intervention, and natural language processing. Feature extraction will be approached through data-driven (Aim 1A/2A) and insight-driven (Aim 1B/2B) methods based on existing datasets. The existing data for Aim 1 will include 200 clinical notes describing individual participants early warning signs for psychosis relapse, and relevant data for Aim 2 will include 400 psychotherapy progress notes from the health records. Themes related to the outcomes of interest will be automatically identified using topic modeling. Features will be derived by calculating the semantic distance between the identified themes and the content of prospectively collected transcripts. In the insight- driven approach, the study team will review and interpret existing data in the context of prior work and clinical expertise; hypothesis-driven acoustic and NLP features will be identified and calculated. Prospectively collected speech data from psychotherapy sessions (n=150 participants; ~1200-1500 sessions) will then be used to develop and test predictive algorithms for hospitalization (Aim 1C) and disengagement (Aim 2C). The outcomes will be considered in a time-to-event setting as well as in a binary variable setting (hospitalization in 30 days & 90 days, disengagement in 90 days & 6 months). The primary analytic strategy for time-to-event outcomes will be based on regularized Cox regression, which allows for the selection of features significantly associated with hazard function for hospitalization or disengagement. Additionally, with a focus on improving prediction accuracy, machine learning-based survival and classification models will be considered (e.g. Deep Convolutional and Deep Recurrent Neural Network models: CNN-Surv & RNN-Surv). Speech and language features recorded from all sessions across all participants will be used. Training and testing for all models will be based on the standard 60%/20%/20% random split into training, validation, and test datasets. Key deliverables include the development of speech-based tools capable of identifying individuals at risk for rehospitalization and treatment disengagement in real-time, which enables timely interventions to mitigate adverse outcomes. Successful completion of this project could pave the way for future randomized clinical trials to evaluate the effectiveness of real-time implementation in improving outcomes within CSC.
EPINET-Affiliated	Optimizing Disability Benefit Decisions and Outcomes in First Episode Psychosis	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11190994	RF1MH125868	PA-20-185	Howard H. Goldman, Genevra Jones	University of Maryland Baltimore	A major NIMH goal for early psychosis treatment is to prevent deterioration and disability among individuals suffering from psychotic illness. However, rates of Social Security Administration disability (SSI/DI) enrollment remain high for young people in early psychosis treatment. Existing studies on SSI/DI have limited information on the FEP population. Enrollment in SSI/DI may provide benefits (including cash assistance, access to Medicaid/Medicare, and other social entitlements), but also detrimentally impact identity, vocational aspirations, career development and employment; individuals rarely leave SSA Disability benefits for full-time work. It is crucial to generate knowledge with the potential to inform decision-making about the benefits and trade-offs inherent in SSI/DI participation and help optimize outcomes within the context of SSI/DI participation among this population of young people (e.g. generally aged 16-30). The primary goal of this project is to investigate factors influencing decisions to apply for SSI/DI, the impact of these decisions, and the longitudinal relationships between SSI/DI and career development. This information will then be used to develop systematic strategies for improving services, supporting client decision making and optimizing outcomes. The NIMH-funded Early Psychosis Intervention Network (EPINET), spanning 101 coordinated specialty care (CSC) programs across 16 states, affords an exceptional opportunity to better understand these issues. Leveraging the EPINET initiative, this multi-phase, mixed methods study will provide important new knowledge regarding predictors of application and the relationship between SSI/DI and vocational functioning, will facilitate development and evaluation of a multi-level menu of actionable targets and associated implementation strategies, for example, specific ways of improving programmatic supports and policy changes designed to strengthen SSA Disability-related outcomes (translation of research to practice). The project is guided by the multi-level Disability Creation Process (DCP2) framework, and implementation activities by the Consolidated Framework for Implementation Research (CFIR). The proposed project will utilize quantitative data from the EPINET common dataset (n=5000+), supplemented with primary quantitative (n = 330) and qualitative (n = 110) data collection in four distinct states. The study aims are to: determine the extent and predictors of SSI/DI application, SSA Disability enrollment and work/school functioning among clients enrolled in CSC programs, systematically investigate the client experience of SSI/DI decision making (through quantitative and qualitative data) and using implementation mapping to develop strategies to optimize CSC services as they impact SSI/DI.
EPINET-Affiliated	ConProject-001	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11190995	7825	PA-20-185	Howard H. Goldman	University of Maryland Baltimore	N/A
EPINET-Affiliated	SCH: A structural causal framework for adaptive experiments	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/11307544	R01AI197146	PAR-25-001	Michele Santacatterina, Ivan Diaz	New York University School of Medicine	Adaptive randomized clinical trials are critical in infectious disease research, offering flexibility to adjust sample sizes, introduce new interventions, discontinue ineffective treatments, and target specific subgroups to enhance treatment efficacy. This adaptability is particularly valuable in rapidly evolving public health crises, such as the development of treatments for emerging infectious diseases like COVID-19. However, adaptive trials present significant challenges, including unclear inferential targets, statistical biases from temporal and spatial variability, complexities in handling dynamic data structures, and an increased risk of false-positive findings. These concerns are reflected in recent FDA guidance on estimands, which emphasizes the need for clearly defined inferential targets, and on adaptive designs, which acknowledges that statistical bias in adaptive trials remains an understudied issue. Despite these recognized challenges, current research lacks a principled framework for structurally representing and unbiasedly estimating causal effects in adaptive trials. This project will develop a structural causal framework for adaptive trials, leveraging modern causal inference and statistical techniques alongside secondary data from the Adaptive COVID-19 Treatment Trial (ACTT)an adaptive trial evaluating novel therapeutics in hospitalized COVID-19 patientsto enable transparent, efficient, and statistically unbiased estimation of causal effects. To achieve this, we propose the following specific aims: Aim 1: Develop a structural causal approach that deals with temporal variability. Aim 2: Extend our framework to handle spatial variability. Aim 3: Expand our framework to handle complex data structures, including failure-time and missing data, while dealing with false-positive results. Our project aligns with NIAIDs mission by advancing key methodologies for infectious disease clinical trials, particularly in adaptive designs for pandemic response, emerging pathogens, and the development of antiviral treatments. While our primary focus is on infectious disease trials, our methods have broader applicability to other disease areas, such as schizophrenia. We show this by also leveraging secondary data from schizophrenia studies, including the DECIFER trial, the RAISE study, and the EPINET study. RELEVANCE (See instructions): This research aims to improve how adaptive clinical trials are designed and analyzed. By developing methods that address key challenges in adaptive trials, our work will help ensure more accurate and reliable results, ultimately leading to better treatments and public health responses to emerging infectious diseases.
EPI-CAL	California Collaborative Network to Promote Data Driven Care and Improve Outcomes in Early Psychosis (EPI-CAL)	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/10695140	R01MH120555	RFA-MH-19-150	Tara Ann Niendam	University of California at Davis	A prolonged first episode of psychosis (FEP) without adequate treatment is the most consistent predictor of poor clinical and functional outcomes 1, poor health outcomes 2 and significant economic burden 3. Team-based coordinated specialty care (CSC)4 for early psychosis (EP) has established effectiveness in promoting clinical and functional recovery 5 . EP treatment programs have expanded rapidly with increased funding across the US without formal coordination of training or implementation. While EP programs share many features, the lack of state and national coordination and data infrastructure limits the capacity for large-scale evaluation or accelerated dissemination of best practices 6. Based on prior collaborations with 30 California (CA) EP programs and experiences using mobile health (MOBI mHealth) technology to measure individual outcomes in EP care, the UC Davis (UCD) team is uniquely poised to create EPI-CAL, a CA network that will contribute systematically collected outcomes data on over 1000 FEP clients per year, from 6 community and 6 university EP clinics, to a national EP network supported by the NIMH EPINET program. Building on our prior work evaluating CA EP programs, EPI-CAL programs will participate in a formative evaluation in Year 1 to define core EP clinical features, intervention targets, and outcomes needed to harmonize network input. A core battery based on current measures collected at the sites, the PhenX toolkit 7 and expanded to cover all critical domains, will be installed across the network in Year 2. Core client outcomes and metrics of data use for treatment decisions will be collected using the custom MOBI mHealth data network at the client, program, and state level to allow easy data analysis, interpretation and dissemination. Training and ongoing monitoring will be provided at all EPI-CAL sites to ensure appropriate implementation. EPI-CAL will contribute de-identified data to the national coordinating hub. Using the RE-AIM implementation science framework 8,9, we will systematically evaluate the impact of MOBI on EP programs across 5 dimensions: reach, efficacy, adoption, implementation, and maintenance (see Figure 1). To demonstrate the networks research capacity, in the R34 component of this application, we propose to develop and validate a measure of the Duration of Untreated Psychosis (DUP) that is feasible for use in community settings and psychometrically sound. Although DUP is a significant predictor of both short-term CSC treatment response5 and long-term outcomes 10 for FEP, no measure currently exists that has been rigorously validated and is feasible for use by community providers 7,11. We will utilize stakeholder feedback (clients, family members, academic experts and CSC staff) to develop a tool with standardized DUP definitions that includes anchored assessment of psychosis onset and start of treatment. Developing such a tool will allow standardized assessment of this critical moderator of CSC outcomes across the entire EPINET.
AC-EPINET	Academic-Community EPINET (AC-EPINET): Mitigating Barriers to Care	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/10691909	R01MH120588	RFA-MH-20-205	Alan Breier	Indiana University Indianapolis	Comprehensive specialty care programs for young people in the initial phases of psychotic disorders, such as Coordinated Specialty Care (CSC), deliver superior clinical outcomes compared to usual care. Challenges associated with CSC that, if addressed, would further enhance its effectiveness include the utilization of health care data to continuously improve clinical decision-making and services delivery. In addition, innovative interventions that strengthen treatment engagement and improve key outcomes, such as hospitalization rates, would also enhance CSC effectiveness. The Academic-Community EPINET (AC-EPINET) will address these challenges through a network of six early intervention (spoke) sites connected through advanced informatics to a central hub. Our network will implement a Learning Healthcare System (LHS), embedded in the everyday workflow of spoke clinics, to identify performance gaps, drive continuous quality improvement and enable practice-based research. The LHS will utilize the EPINET common assessment battery and CSC-ONE will serve as the informatics platform. It passively extracts data from electronic health records to minimize dual entry and supports a culture of measurement and continuous improvement. Dashboard displays of outcomes permit real-time comparisons within and across spoke clinics, driving patient outcomes towards international best practice standards, while maintaining critical privacy standards. Our six clinical spoke sites share the following: 1) established early psychosis programs following the CSC model; 2) deep expertise in data collection, assessments, and clinical trial research to enhance the conduct of the pilot research study; 3) community-based, real-world early psychosis clinics enrolling underserved populations, including urban poor and rural populations; and 4) strong interests and experience with telehealth (TH). The central hub will provide study governance, oversight, data management, training, data transfer to the NDCC. The leadership team has deep expertise in comparative effectiveness trials, TH services delivery, informatics, and large data set outcomes analytics. Thus, the AC-EPINET is well positioned to achieve data-driven, improved clinical services through the use of a common assessment battery. Moreover, we will assess the effectiveness of CSC treatment delivered through telehealth (CSC-TH) compared to standard, clinic-based CSC (CSC-SD) to improve engagement and hospitalization rates in a 12-month, randomized trial. Several studies have demonstrated that TH treatment enhances engagement by overcoming barriers, such as long travel commutes to clinics, unavailability of reliable transportation for clinic appointments, stigma associated with receiving care in psychiatric clinics, and inconveniences adapting to inflexible service schedules and workflow patterns. In addition, TH treatment has shown advantages for decreasing hospitalization rates, which is supported by our preliminary studies. Combined with the LHS, the TH intervention has the potential to enhance the effectiveness of AC-EPINET to improve the lives and outcomes of individuals with early psychosis.
EPINET-TX	Advancing the Early Psychosis Intervention Network in Texas (EPINET-TX)	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/10701700	R01MH120599	RFA-MH-20-205	Molly A. Lopez	University of Texas at Austin	Coordinated specialty care (CSC) has been shown to be more effective for the treatment of first episode psychosis (FEP) than usual care, resulting in better functioning, fewer symptoms, fewer relapses, and less hospital use. The number of CSC teams has increased dramatically in the U.S. since federal funding has been provided to support states' implementation; Texas currently has 26 CSC teams. This rapid growth of early psychosis intervention programs, operating with similar core features and serving similar participants, provides a unique opportunity. By utilizing a standard battery of reliable service and outcome measures, a data informatics system to facilitate shared data and feedback, and staffing to support quality improvement and research infrastructure, Texas can create an environment in which providers, program administrators, and policy makers can continuously develop new knowledge. The regional network will link programs together through the Texas FEP Consortium, support a culture of measurement-based care, and create a framework for addressing research questions that impact the service delivery system, contribute to the research literature, and reduce the burden of psychosis on young people and their families. Research in persons with FEP has shown that co-occurring substance use is common and can lead to an exacerbation of psychotic symptoms, decreased medication adherence, and poorer long-term outcomes. Research has shown that between 23% and 73% of individuals in FEP programs continue to use substances after 6 to 12 months in care. While CSC models have incorporated practices from brief intervention and treatment models to address substance use, little is known about the use of these strategies in community- based programs or their effectiveness in motivating this group of young people to reduce or stop use. The series of proposed studies will advance an understanding of critical issues for addressing substance use in young people who continue use after entering care. The first study will use a chart review approach to examine whether standard CSC-model interventions are successful at engaging participants in behavior change and the extent to which diverse participants (e.g., African American, Latinx) directly identify reduced substance use as a goal. Qualitative studies will examining the perceptions of CSC participants and CSC peer specialists on the feasibility and acceptability of peer-led interventions for supporting substance use recovery. After partnering with peer specialists and individuals receiving CSC services to develop the intervention, a pilot study will be conducted to examine the feasibility of deployment, study recruitment, retention, and other study procedures. This work will set the stage for a statewide partnership with researchers, decision-makers, providers, young people and their families to become a healthcare system that uses data to continuously learn and transform.
Connection LHS	Connecting FEP Research and Practice through a Learning Health System	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/10256085	R01MH120550	RFA-MH-20-205	Melanie E. Bennett	University of Maryland Baltimore	The National Institute of Mental Health has developed the Early Psychosis Intervention Network (EPINET) to learn from the national implementation of coordinated specialty care (CSC) for first episode psychosis (FEP). We propose an EPINET node [Connection Learning Health System (LHS)] that will support uniform data collection, analysis, feedback, and infrastructure development to promote a cultural of continuous quality improvement across 14 well- established CSC programs in Maryland and Pennsylvania serving over 500 young adult clients per year in urban, suburban, and rural settings. We will implement a core assessment battery and develop a system to manage and analyze data across programs and to provide feedback to programs on patient outcomes and fidelity to the CSC model. Building on our experience in cross-state clinician training and consultation, we will expand our existing learning collaborative for CSC implementation in Maryland into a cross-state an LHS that will identify clinical questions, analyze data, and apply findings to optimize practice across all of our CSC programs. We propose a practice-based research project to adapt a motivational interviewing intervention for nontreatment-seeking heavy cannabis-using adolescents to target CSC engagement, medication adherence, and risk reduction to improve use, functioning, and recovery outcomes in FEP patients who are persistent cannabis users. We will work with the EPINET National Data Coordinating Center to implement standard measures and share common data elements to answer clinical questions to improve CSC for young adults experiencing FEP.
OnTrack	OnTrackNY's LearningHealthcareSystem	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/10223998	R01MH120597	RFA-MH-19-150	Lisa B. Dixon, Iruma Bello	New York State Psychiatric Institute DBA ResearchFoundation for Mental Hygiene, Inc.	This application proposes OnTrackNY as a regional scientific hub for the Early Psychosis Intervention Network (EPINET) program as part of NIMH's creation of a national learning health care system (LHS) for early psychosis care. OnTrackNY has grown into a 21-site network, under the leadership of Lisa Dixon, MD, MPH. Created and supported by the New York State's Office of Mental Health (OMH), OnTrackNY is a nationally recognized model providing coordinated specialty care (CSC) for adolescents and young adults within two years of the onset of non-affective psychosis. OMH regulates and licenses all mental health programs in New York and is a direct-services provider via state-operated programs statewide. This makes OMH an ideal partner for establishing a statewide learning health care system for early psychosis care. Further, OnTrackNY's administration, OnTrackCentral, operates within the OMH-supported Center for Practice Innovations at Columbia Psychiatry. This location within a vibrant academic research enterprise supports OMH's mission of scaling up CSC services statewide while addressing key practice-based research questions in the delivery of CSC care. In this model, OnTrackCentral serves as the hub and the 21 OnTrackNY programs serve as the spokes. Since 2014, the still-growing OnTrackNY network has served over 1,200 individuals. From its inception, OnTrackNY has aimed to deliver high-quality, data-driven, accountable and culturally competent care consistent with an LHS. As a condition of funding, all OnTrackNY providers follow established protocols that require submission of patient- and site-level standard measures of early psychosis clinical features, services, and treatment outcomes. Notwithstanding OnTrackNY's considerable strengths, it lacks key supports and resources needed to fully implement the Institute of Medicine's (IOM's) model for a continuously learning healthcare system that connects multiple stakeholders from across a healthcare system to capture and review data, identify new technologies and approaches, and develop and apply strategies to improve quality and increase efficiency. Our proposed EPINET regional hub, the OnTrackNY LHS, will emphasize and enhance two critical foundational components - Aim 1: proactively engage stakeholders to optimize understanding of key problems and their solutions at every LHS phase; and Aim 2: develop data systems with enhanced standardized data collection, including post-discharge data and linkages to external data systems, and enhancing data analytics that will allow for client-level treatment planning and prospective analytics, delivering real time, dynamic and actionable information to stakeholders. These LHS components do not follow in a step- wise sequence but instead operate in parallel and interact to facilitate and enhance quality improvement processes. This backbone will support the development of practice-based research (Aim 3); the initial project will address the knowledge gap in addressing suicidal ideation and behavior among people with early psychosis by developing and testing an adapted suicide prevention protocol.
ESPRITO	Early-phase Schizophrenia: Practice-based Research to Improve Treatment Outcomes (ESPRITO)	https://reporter.nih.gov/search/lMTEe1paAUueRQhV-f7OIQ/project-details/10171918	R01MH120594	RFA-MH-19-150	Delbert G. Robinson, John M. Kane	Feinstein Institute for Medical Research	For the RAISE initiative, we developed the NAVIGATE model of coordinated specialty care (CSC) for first episode psychosis which supports the EPINET goal of furthering measurement-based care and shared treatment decisions. To meet the EPINET goal of improving treatment, having all sites providing treatment using the same model has distinct advantages. Therefore, we have engaged 11 NAVIGATE sites in 4 states, enabling us to address challenges in different regions and find solutions that are not dependent on the environment and support of a single state. Based on our experience training NAVIGATE sites and analyses of RAISE-ETP data, we have identified targets for improvement of services. Our specific targets for improvement divide into two categories. First, initial approaches added to NAVIGATE care for all participants to provide an enhanced version of NAVIGATE (E-NAVIGATE). Second, three research projects target critical junctures in CSC care. Study 1 aims to reduce duration of untreated psychosis (DUP). In RAISE-ETP median DUP was 74 weeks and had a significant impact on quality of life and symptoms. We will study the effect of targeted ads that appear in response to specific terms when someone searches the internet. We expect that this will increase the number of young people who will come to our clinics with shorter DUP. Study 2 addresses further reduction of hospitalization. Even with CSC, approximately one third will be hospitalized within 2 years and poor adherence to medication can lead to hospitalization. Direct observation of treatment can substantially improve adherence and reduce hospitalization. We will study a unique suite of methods for direct observation delivered as an app on a smart phone to support adherence. In a randomized trial we will compare this intervention to usual E-NAVIGATE to improve adherence and reduce hospitalization. Study 3 is designed to identify E-NAVIGATE participants who are at high risk for disengagement, and to intervene in order to prevent/delay disengagement because even with CSC, 30-50% of participants will disengage from treatment within 2 years. We will compare an internet delivered version of E-NAVIGATE that reduces treatment burden to usual clinical strategies to prevent disengagement. We will build upon our RAISE-ETP and post-ETP experience to build a unique network that will deliver an evolving CSC that changes based upon feedback from experience and a dedicated informatics platform. Research, designed to be generalizable not only to our network but to CSC practice more broadly, will set the stage for the next generation of CSC deployment.
EPI-MINN	Targeting Cognition and Motivation in Coordinated Specialty Care for Early Psychosis	https://reporter.nih.gov/search/0ShALQgPiUCEvC_ymUmIjQ/project-details/9816739	R01MH120589	RFA-MH-19-150	Sophia Vinogradov, Piper Meyer-Kalos	University of Minnesota	The purpose of this study is to expand measurement-based psychiatric care across 6 early psychosis treatment teams in Minnesota, each providing coordinated specialty care and in total serving 200 individuals per year. Our first goal is to efficiently deploy valid longitudinal outcome measures across each team, implement state-of-the-art informatics tools, and aggregate pooled data to inform and support program evaluation activities as well as novel data-driven analytics. Our second goal is to perform a practice-based research project designed to answer two questions: 1) Does a structured personalized feedback session that includes an explicit focus on cognition and motivated behavior provide benefit to stakeholders--service users, family members, and primary clinicians? 2) Can cognition and motivated behavior be addressed as key treatment goals within real-world settings, using a 12-week mobile intervention program? Our central scientific premise is that cognitive dysfunction and impaired motivated behavior are critical unmet therapeutic needs in early psychosis. We have shown that auditory cognitive training can be successfully delivered on a mobile device to individuals with early schizophrenia, resulting in significant gains in global cognition that endure 6 months after the end of the intervention. We have also demonstrated that the addition of social cognition training drives improvements in measures of motivated behavior. More recently, we demonstrated that a 12-week mobile digital health coaching and social networking app designed to target motivated behavior in early psychosis resulted in significantly greater improvements in self-reported depression, defeatist beliefs, self-efficacy, and a trend towards improved motivation/pleasure and negative symptoms (compared to a wait-list control). These improvements were maintained when re- assessed 3 months after the end of the trial. Based on this work and on our experience running successful coordinated specialty care teams, our project will address the following two aims: Aim 1: Establish highly reliable measurement-based psychiatric care for 200 early psychosis individuals per year across 6 clinical teams; Harness clinical encounter data to perform novel data-driven trajectory analyses, predictive modeling, and causal discovery analyses. Aim 2: Investigate potential benefits of identifying cognitive functioning and motivated behavior as explicit treatment targets for individuals entering care; Study a well-defined 12-week mobile intervention program to address these targets.
ENDCC	Early Psychosis Intervention Network (EPINET) Data Coordinating Center	https://reporter.nih.gov/search/ZbyPHacE30CiGjbkl7anpQ/project-details/11062641	U24MH139129	RFA-MH-24-106	Abram Rosenblatt	Westat	This application outlines and describes the Westat Team’s proposed approach to continuing to serve as the National Institute of Mental Health (NIMH) Early Psychosis Intervention Network (EPINET) National Data Coordinating Center (ENDCC). The current ENDCC, in partnership with NIMH, successfully collaborated with eight regional scientific hubs composed of 110 Coordinated Specialty Care (CSC) programs to fulfill the original design of EPINET by doing the following: establishing a Core Assessment Battery (CAB) of common measures; creating a harmonized and consolidated set of CAB data across the eight regional hubs for over 5,000 CSC program participants; developing and hosting an Analyst Zone that provides a secure method for accessing and analyzing the EPINET harmonized and consolidated data set; and submitting the required data to the NIMH National Data Archive (NDA). The ENDCC also developed additional components beyond the original EPINET design, including a web-based CAB data collection suite (WebCAB) and a Program Level CAB (PL-CAB). In the upcoming funding cycle, we will use the structures and processes already developed and implemented by the ENDCC to refine and expand the role of the ENDCC. The existing ENDCC health informatics infrastructure will incorporate enhanced data quality and validation processes, as well as expedited data transmission methods to create a FAIR (Findable, Accessible, Interoperable, and Reusable) dataset accessible to a wide range of scientific investigators. Next-generation data analytic tools, visualization methods and techniques, data query approaches, and dashboards will enable data to be turned into relevant program- and practice-level information. The knowledge gained and the data collected will be used to refine existing data collection tools and protocols in collaboration with members of the EPINET Research Consortium. Work initiated by the ENDCC and currently underway on the fidelity and quality of care measures and variables necessary as the context for participant-level CAB variables will be fully implemented. The ENDCC will expand on its current success in engaging CSC programs beyond those participating directly in EPINET, including enhancing the WebCAB data collection system, designed specifically for widespread implementation, to extend its role to CSC programs with common measures and metrics.

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