School of Medicine


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  • Lindsey Eileen Zimmerman

    Lindsey Eileen Zimmerman

    Affiliate, Psych/Public Mental Health & Population Sciences

    Bio Lindsey Zimmerman, PhD, is a Clinical and Community Psychologist, and Implementation Scientist at the National Center for PTSD, Dissemination and Training Division of the Veterans Health Administration.

    Dr. Zimmerman is principal investigator of National Institutes of Health (NIH) and Veterans Health Administration (VA) research that enlists participatory system dynamics to increase timely patient access to evidence-based pharmacotherapy and evidence-based psychotherapy for depression, PTSD, alcohol and opioid use disorder. See https://mtl.how/team



    Active NIH Grants

    Participatory System Dynamics vs Audit and Feedback: A Cluster Randomized Trial of Mechanisms Of Implementation Change to Expand Reach of Evidence-Based Addiction and Mental Health Care (R01DA046651)

    The most common reasons Veterans seek VA addiction and mental health care is for help with opioid and alcohol misuse, depression and PTSD. Research evidence has established highly effective treatments that prevent relapse, overdose and suicide, but even with policy mandates, performance metrics, and electronic health records to fix the problem, these treatments may only reach 3-28% of patients. This study tests participatory business engineering methods to better meet the addiction and mental health needs of Veterans and the U.S. population.


    Participatory System Dynamics for Evidence-Based Addiction and Mental Healthcare (R21DA042198)

    Limited access and delays to high-quality, evidence-based mental health and addiction treatments can lead to patients getting too little or ineffective care and contribute to chronic patient impairment, relapse, and death by suicide or overdose. This study evaluates a system for resolving problems with patient flow and organization in health care systems, using electronic medical record systems and a high-level of input from healthcare leadership, frontline providers and patients.


    Active VA Grants

    Participatory System Dynamics vs Usual Quality Improvement: Is Staff Use of Simulation an Effective, Scalable and Affordable Way to Improve Timely Veteran Access to High-quality Mental Health Care? (I01HX002521)

    Participatory system dynamics (PSD) helps improve quality with existing resources, critical in mental health and all VA health care. PSD uses learning simulations to improve staff decisions, showing how goals for quality can best be achieved given local resources and constraints. We aim to significantly increase the proportion of patients who start and complete evidence-based care, and determine the costs of using PSD for improvement.


    National Responsibilities

    2019 National Institutes of Health, Center for Scientific Review
    Community Influences on Health Behavior (CIHB) Study Section

    2019-present VA Quality Enhancement Research Initiative (QUERI) 
    QUERI/Health Services Research & Development, Scientific Merit Review Committee

    2019-present Emory University
    Prolonged Exposure Consultant Training Program Advisory Board

    2018-present National Institutes of Health
    Training Institute for Dissemination and Implementation Research in Health (TIDIRH)
    Mental Health Faculty Mentor

    2015-2017 National Institutes of Health Loan Repayment Program
    National Institute of Mental Health Clinical Research Review Committee



    Teaching Responsibilities

    Quality Improvement and Systems of Care Competencies
    Psychiatry & Behavioral Sciences Residency, Stanford University School of Medicine & VA Palo Alto Health Care System

    Postdoctoral Research Fellowship Program Seminar
    VA Palo Alto research centers of the National Center for PTSD (NCPTSD), Center for Innovation to Implementation (Ci2i), Mental Illness Research Education and Clinical Care (MIRECC), and War-related Illness and Injury Study Center (WRIISC).



    Open Science Resources for the Modeling to Learn Simulation Learning Program are available on GitHub at https://mtl.how and https://mtl.how/demo

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