Clinical trial NCT04208217: Participatory System Dynamics vs Usual Quality Improvement: Staff Use of Simulation as an Effective, Scalable and Affordable Way to Improve Timely Mental Health Care?
Official title: 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?
Study type: INTERVENTIONAL
Overall status: ENROLLING_BY_INVITATION
Phase: NA
Conditions: PTSD
Depression
Opioid Use Disorder
Keywords: implementation science
quality improvement
modeling to learn
usual quality improvement
evidence-based psychotherapy
evidence-based pharmacotherapy
addiction
mental health/behavioral health
Lead sponsor: VA Office of Research and Development
Sponsor class: FED
First posted: 2019-12-23
Enrollment: 720
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Modeling to Learn: Modeling to Learn is a facilitated health care quality improvement or evidence-based practice implementation strategy that includes frontline addiction and mental health staff running simulations of clinic improvement strategies to find the best approaches for improving the reach of evidence-based psychotherapy and evidence-based pharmacotherapy.

Usual Quality Improvement: Usual quality improvement is a health care quality improvement or evidence-based practice implementation strategy that includes frontline addiction and mental health staff reviewing team data to find the best approaches for improving the reach of evidence-based psychotherapy and evidence-based pharmacotherapy.

Anticipate that 720 frontline providers will participate across both arms of this trial. There will be no interaction with current patients for the purposes of research. No new data will be collected beyond data generated during routine care.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: Evidence-based VA care is best for meeting Veterans' mental health needs, such as depression, PTSD and opioid use disorder, to prevent suicide or overdose. But some key evidence-based practices only reach 3-28% of patients. 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. This study aims to significantly increase the proportion of patients who start and complete evidence-based care, and determine the costs of using PSD for improvement. Empowering frontline staff with PSD simulation encourages safe 'virtual' prototyping of complex changes to scheduling, referrals and staffing, before translating changes to the 'real world.' This study determines if PSD increases Veteran access to the highest quality care, and if PSD better maximizes VA resources when compared against usual trial-and-error approaches to improving quality.

Detailed description: 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: This study is proposed in the high priority area of VA addiction and mental health care to improve Veteran access to VA's highest quality care. The 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 EBP reach is mediated by team scores on participatory measures.
3. Affordable: (3a) Determine the budget impact of MTL. (3b). Calculate the average marginal costs per 1% increase in EBP reach.

Methodology: This study proposes a two-arm, 24-clinic (12 per arm) cluster randomized trial to test for superiority of MTL over usual QI for increasing EBP reach. Clinics will be from 24 regional health care systems (HCS) below the SAIL mental health median, and low on 3 of 8 SAIL measures associated with EBPs. Computer-assisted stratified block randomization will balance MTL and usual QI arms at baseline using Corporate Data Warehouse (CDW) data. Participants will be the multidisciplinary frontline teams of addiction and mental health providers.

Next Steps/Implementation: MTL was developed in partnership with the VA Office of Mental Health and Suicide Prevention (OMHSP) and if shown to be effective, scalable, and affordable for improving timely Veteran access to EBPs, MTL will be scaled nationally to more clinics by expanding MTL online resources, and training more VA staff to facilitate MTL activities instead of usual QI.

Eligibility criteria: Inclusion Criteria:

24 health care systems currently functioning below the median VA mental health recommendations for Strategic Analytics for Improvement \& Learning (SAIL) and below the median for 3 of 8 SAIL evidence-based treatment approaches.

* VA divisions and community-based outpatient clinics (CBOCs) or 'clinics' from regional VA health systems
* Must be below the overall VA quality median (as assessed by the Strategic Analytics for Improvement and Learning or SAIL), which includes 3 of 8 SAIL measures associated with four evidence-based psychotherapies and three evidence-based pharmacotherapies for depression, PTSD, and opioid use disorder.

Exclusion Criteria:

Health care systems functioning above median VA mental health recommendations for Strategic Analytics for Improvement \& Learning (SAIL) and below the median for 3 of 8 SAIL evidence-based treatment approaches. Only one health care system can be included per arm - MTL vs QI.

* clinics with less than 12 months of data in 2018
* clinics involved in Office of Veterans Access to Care (OVACS) quality improvement program at baseline
* clinics where the VA Cerner electronic health record (EHR) implementation rollout will occur during the project period (Veterans Integrated Services Networks (VISNs) 20, 21 ,22, and 7)
* clinics who serve less than 122 unique patients each month on average
* clinics without an onsite multidisciplinary team of mental health or addiction service providers (minimum required: 1 psychiatrist, 1 psychologist, 1 social worker onsite)

Primary outcomes:
measure: Proportion of patients initiating and completing a course of evidence-based psychotherapy (EBPsy) or evidence-based pharmacotherapy (EBPharm)
timeFrame: Pre-/Post- 12-month period average of EBP reach (24 months total observation)]
measure: Number of completed EBPsy templates during sessions with a relevant CPT code
timeFrame: Pre-/Post- 12-month period average of EBP reach (24 months total observation)]
measure: Number of combination of prescriptions placed with the VA pharmacy and sessions with a relevant CPT code
timeFrame: Pre-/Post- 12-month period average of EBP reach (24 months total observation)]
Secondary outcomes:
measure: Differences in team perceptions of MTL and QI assessed by the Acceptability of Intervention Measure (AIM)
timeFrame: at 6 months
measure: Differences in team perceptions of MTL and QI assessed by the Intervention Appropriateness Measure (IAM)
timeFrame: at 6 months
measure: Differences in team perceptions of MTL and QI assessed by the Feasibility of Intervention Measure (FIM)
timeFrame: at 6 months
measure: Patient Aligned Care Team Burnout Measure (PACT)
timeFrame: At baseline and 6 months
measure: Participatory Measure: Context
timeFrame: At baseline and 6 months
measure: Participatory Measure: Partnership Structural Values
timeFrame: At 6 months
measure: Participatory Measure: Relationships
timeFrame: At 6 months
measure: Participatory Measure: Synergy
timeFrame: At 6 months
measure: Participatory Measure: Capacity-Building Index
timeFrame: At 6 months
measure: Facilitator Quality: Engagement Principles
timeFrame: At 6 months
measure: MTL Fidelity Checklist for 12-Session Plan
timeFrame: Throughout 6 months
measure: QI Fidelity Checklist for 12-Session Plan
timeFrame: Throughout 6 months
measure: Quality Improvement Activity Tracking
timeFrame: Throughout 6 months
measure: Demographic Measures
timeFrame: At baseline and 6 months
Other outcomes:
Locations:
city: Palo Alto
country: United States
facility: VA Palo Alto Health Care System, Palo Alto, CA
state: California
Study officials:
affiliation: VA Palo Alto Health Care System, Palo Alto, CA
name: Lindsey E. Zimmerman, PhD
role: PRINCIPAL_INVESTIGATOR
References: