# 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?

> **NIH VA I01** · VETERANS ADMIN PALO ALTO HEALTH CARE SYS · 2020 · —

## Abstract

Background: Evidence-based practices (EBPs) are the most high value treatments to meet Veterans’
addiction and mental health needs, reduce chronic impairment, and prevent suicide or overdose. Over 10
years, VA invested in dissemination of evidence-based psychotherapies and pharmacotherapies based on
substantial evidence of effectiveness as compared to usual care. Quality metrics also track progress.
Despite these investments, patients with prevalent needs, such as depression, PTSD and opioid use
disorder often don’t receive EBPs. Systems theory explains limited EBP reach as a system behavior
emerging dynamically from local components (e.g., patient demand/health service supply). Participatory
research and engagement principles guide participatory system dynamics (PSD), a mixed-methods
approach used in business and engineering, shown to be effective for improving quality with existing
resources. Significance/Impact: We propose our study in the high priority area of VA addiction and mental
health care to improve Veteran access to VA’s highest quality care. Our PSD program, Modeling to Learn
(MTL), improves frontline management of dynamic complexity through simulations of staffing, scheduling
and service referrals common in healthcare, across generalist and specialty programs, patient populations,
and provider disciplines/treatments. Innovation: Recent synthesis of VA data in the enterprise-wide SQL
Corporate Data Warehouse (CDW) makes it feasible to scale participatory simulation learning activities with
VA frontline addiction and mental health staff. MTL is an advanced quality improvement (QI) infrastructure
that helps VA take a major step toward becoming a learning health care system, by empowering local
multidisciplinary staff to develop change strategies that fit to local capacities and constraints. Model
parameters are from one VA source and generic across health services. If findings show that MTL is
superior to usual VA quality improvement activities of data review with facilitators from VA program offices,
this paradigm could prove useful across VA services. The PSD approach also advances implementation
science. Systems theory explains how dynamic system behaviors (EBP reach) are defined by general
scientific laws, yet arise from idiographic local conditions. Empowering staff with systems science simulation
encourages the safe prototyping of ideas necessary for learning, increasing ongoing quality improvement
capacities, and saving time and money as compared to trial-and-error approaches. Specific Aims: 1.
Effectiveness: Test for superiority of MTL over usual QI for increasing the proportion of patients (1a)
initiating, and (1b) completing a course of evidence-based psychotherapy (EBPsy) and evidence-based
pharmacotherapy (EBPharm). 2. Scalable: (2a) Evaluate usual QI and MTL fidelity. (2b) Test MTL fidelity for
convergent validity with participatory measures. (2c) Test the participatory theory of change: Evaluate
whether 12 month period EB...

## Key facts

- **NIH application ID:** 9838122
- **Project number:** 1I01HX002521-01A2
- **Recipient organization:** VETERANS ADMIN PALO ALTO HEALTH CARE SYS
- **Principal Investigator:** Lindsey Eileen Zimmerman
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2020-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9838122

## Citation

> US National Institutes of Health, RePORTER application 9838122, 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? (1I01HX002521-01A2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9838122. Licensed CC0.

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