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

我们的广泛目标是使所有医疗保健利益相关者能够为所有患者提供最高质量的护理。 我们的具体目标是解决与实施循证医学相关的复杂性和权衡 在门诊成瘾和精神卫生系统的实践（EBP）。科学界一致认为 最好的循证心理治疗和药物治疗（EBP），以满足患者的需求， 阿片类药物和酒精使用障碍，创伤后应激障碍和抑郁症。然而，随着时间的推移，EBP协调， 跨多学科团队的供应商，是复杂的，不断变化的。退伍军人健康 政府（VA）的政策授权，国家培训计划和激励质量措施， 但这并不足以让超过3%至28%的患者获得最高质量的治疗。事实上， 有限的EBP覆盖范围在卫生系统中很常见，实施科学领域试图解决这个问题。 一个常规策略是数据审计与提供者反馈（审查和反馈; AF），然而， 是高度可变的。作为一种替代，参与式系统动力学（PSD）已被用来解释原因 60年来，企业管理中的复杂问题。我们与使用PSD的一线员工合作， 确定EBP如何从当地资源和限制中脱颖而出，并由系统决定 动态，如延迟和反馈。EBP到达的动力学在微分形式中被正式指定， 方程模型，并测试对VA数据来自国家VA SQL数据库。我们用这个 现有的企业数据，为每个护理团队定制模型参数。提供了私营部门司模型 通过“建模学习”界面和培训，在此期间，团队安全地评估了当地的变化情景 通过模拟找到满足退伍军人需求的最高收益选择。PSD学习模拟 为协调护理的团队提供即时、实时的反馈，改善日常决策 长期改进计划。我们提出了一项两组、24个中心（12个中心/组）的随机分组试验， 测试PSD模拟与更标准的团队AF数据审查相比的有效性。我们 假设PSD在改善EBP启动和剂量方面上级AF（目标1）。我们将测试 PSD理论的变化，PSD对改进EBP达到的效果解释为团队的改进 系统思维（目标2）。为了证实PSD的广泛用途的潜力，我们还将测试 PSD和AF组之间PSD因果动力学的普遍性（目标3）。这项研究有可能 通过确定什么能提高卫生系统质量（定义为EBP范围）， 它为什么会起作用，以及在什么条件下起作用。如果私营部门发展有效，研究活动将针对国家优先事项， 改善退伍军人的成瘾和精神卫生保健，以防止慢性症状，复发，自杀和 服药过量从提议的有效性、因果关系和一般性测试中得出的结果也可以催化 未来的应用，使整个医疗保健的连续性的重大公共卫生影响。

Our broad aim is to empower all healthcare stakeholders to provide the highest quality care to all patients. Our specific aims address the complexities and tradeoffs associated with implementing evidence-based practices (EBPs) in outpatient addiction and mental health systems. There is scientific consensus about the best evidence-based psychotherapies and pharmacotherapies (EBPs) to meet the needs of patients with opioid and alcohol use disorder, PTSD and depression. However, EBP coordination over time, within and across multidisciplinary teams of providers, is complex and constantly changing. Veterans Health Administration (VA) policy mandates, national training programs, and incentivized quality measures, have been insufficient for reaching more than 3 to 28% of patients with the highest quality treatments. In fact, limited EBP reach is common in health systems and the field of implementation science seeks to address it. One routine strategy is data auditing with provider feedback (audit-and-feedback; AF), however, the impact is highly variable. As an alternative, participatory system dynamics (PSD) has been used to explain causes of complex problems in business management for 60 years. We partnered with frontline staff using PSD to determine how EBP reach emerges from local resources and constraints, and is determined by system dynamics, such as delays and feedback. The dynamics of EBP reach were formally specified in differential equation models, and tested against VA data drawn from a national VA SQL database. We used this existing enterprise data to tailor model parameters to each care team. PSD models were made accessible via a `Modeling to Learn' interface and training, during which teams safely evaluated local change scenarios via simulation to find the highest yield options for meeting Veterans' needs. PSD learning simulations produce immediate, real-time feedback to the teams who coordinate care, improving day-to-day decisions and long-term improvement plans. We propose a two-arm, 24-site (12 sites/arm) cluster randomized trial to test the effectiveness of PSD simulation as compared to more standard team AF data review. We hypothesize that PSD will be superior to AF for improving EBP initiation and dose (Aim 1). We will test the PSD theory of change that the effect of PSD on improved EBP reach is explained by improvement in team systems thinking (Aim 2). To confirm the potential for widespread usefulness of PSD, we will also test the generalizability of PSD causal dynamics across PSD and AF arms (Aim 3). This study has the potential to inform a new paradigm, by determining what works to improve health system quality defined as EBP reach, why it works, and under what conditions. If PSD is effective, study activities will address a national priority to improve Veterans' addiction and mental health care to prevent chronic symptoms, relapse, suicide and overdose. Findings from the proposed tests of effectiveness, causality, and generality, could also catalyze future applications to make a significant public health impact across the continuum of healthcare.