School of Medicine
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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) 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.
Current NIH Research 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 that engage patients, providers and policy makers against the status quo approaches, such as data review, and will determine if it works, why it works, and whether it can be applied in many health care settings to guarantee patient access to the highest quality care and 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. Using innovative approaches from engineering, stakeholders identify optimal ways to align existing resources to best meet patients’ needs, mathematically evaluating the impact of different organizational fixes before undertaking difficult systems changes; this helps health systems avoid ineffective improvement strategies and increases the supply of the most effective treatments to meet patients’ needs.
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 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
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
Graduate, Medicine, Radiology
Bio I am a visiting graduate student researcher under supervision of Dr. Dan Ennis at Radiological Sciences Laboratories and VA Palo Alto. My research interests include cardiovascular flow imaging, in-vivo and in-vitro cardiac magnetic resonance imaging, patient-specific image-based modeling, quantitative image analysis, application of novel 3D printing technology.