California Collaborative Network to Promote Data Driven Care and Improve Outcomes in Early Psychosis (EPI-CAL)

加州合作网络促进数据驱动护理并改善早期精神病的结果 (EPI-CAL)

• 批准号: 10437668
• 负责人: Tara Ann Niendam
• 金额: $ 157.96万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-10 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10437668
• 关键词: Address; Adoption; California; Caring; Client; Client satisfaction; Clinic; Clinical; Collaborations; Communities; Coordinated Specialty Care; Custom; Data; Data Analyses; Development; Dimensions; Economic Burden; Effectiveness; Enrollment; Ensure; Equipment and supply inventories; Evaluation; Family member; Feedback; Funding; Health; Health Technology; Individual; Intervention; Maintenance; Measures; Monitor; National Institute of Mental Health; Outcome; Participant; PhenX Toolkit; Provider; Psychometrics; Psychoses; Quality of life; Reach Effectiveness Adoption Implementation and Maintenance; Recovery of Function; Reference Standards; Reporting; Research; Schizophrenia; Severities; Site; Standardization; Symptoms; Target Populations; Technology; Testing; Time; Training; Universities; Validation; Work; acceptability and feasibility; base; care outcomes; care providers; clinical care; community setting; dashboard; data de-identification; data infrastructure; duration of untreated psychosis; early psychosis; evidence base; experience; first episode psychosis; formative assessment; functional outcomes; health data; implementation barriers; implementation framework; improved; improved outcome; innovation; mHealth; novel; programs; psychotic symptoms; sound; support network; tool; treatment choice; treatment planning; treatment program; treatment response

Project Summary A prolonged first episode of psychosis (FEP) without adequate treatment is the most consistent predictor of poor clinical and functional outcomes 1, poor health outcomes 2 and significant economic burden 3. Team-based “coordinated specialty care” (CSC)4 for early psychosis (EP) has established effectiveness in promoting clinical and functional recovery 5 . EP treatment programs have expanded rapidly with increased funding across the US without formal coordination of training or implementation. While EP programs share many features, the lack of state and national coordination and data infrastructure limits the capacity for large-scale evaluation or accelerated dissemination of best practices 6. Based on prior collaborations with 30 California (CA) EP programs and experiences using mobile health (MOBI mHealth) technology to measure individual outcomes in EP care, the UC Davis (UCD) team is uniquely poised to create EPI-CAL, a CA network that will contribute systematically collected outcomes data on over 1000 FEP clients per year, from 6 community and 6 university EP clinics, to a national EP network supported by the NIMH EPINET program. Building on our prior work evaluating CA EP programs, EPI-CAL programs will participate in a formative evaluation in Year 1 to define core EP clinical features, intervention targets, and outcomes needed to harmonize network input. A “core battery” based on current measures collected at the sites, the PhenX toolkit 7 and expanded to cover all critical domains, will be installed across the network in Year 2. Core client outcomes and metrics of data use for treatment decisions will be collected using the custom MOBI mHealth data network at the client, program, and state level to allow easy data analysis, interpretation and dissemination. Training and ongoing monitoring will be provided at all EPI-CAL sites to ensure appropriate implementation. EPI-CAL will contribute de-identified data to the national coordinating hub. Using the RE-AIM implementation science framework 8,9, we will systematically evaluate the impact of MOBI on EP programs across 5 dimensions: reach, efficacy, adoption, implementation, and maintenance (see Figure 1). To demonstrate the network’s research capacity, in the R34 component of this application, we propose to develop and validate a measure of the Duration of Untreated Psychosis (DUP) that is feasible for use in community settings and psychometrically sound. Although DUP is a significant predictor of both short-term CSC treatment response5 and long-term outcomes 10 for FEP, no measure currently exists that has been rigorously validated and is feasible for use by community providers 7,11. We will utilize stakeholder feedback (clients, family members, academic experts and CSC staff) to develop a tool with standardized DUP definitions that includes anchored assessment of psychosis onset and start of treatment. Developing such a tool will allow standardized assessment of this critical moderator of CSC outcomes across the entire EPINET.

项目概要 首次精神病发作 (FEP) 时间延长且未得到充分治疗是最一致的预测因素 临床和功能结果不佳 1、健康结果不佳 2 和重大经济负担 3. 以团队为基础 针对早期精神病 (EP) 的“协调专业护理”(CSC)4 已在促进临床 和功能恢复 5．随着美国各地资金的增加，EP 治疗项目迅速扩大 没有正式的培训或实施协调。虽然 EP 程序有许多共同的功能，但缺乏 州和国家的协调和数据基础设施限制了大规模评估或 加速传播最佳实践 6. 基于之前与 30 个加州 (CA) EP 的合作 使用移动医疗（MOBI mHealth）技术来衡量个人结果的计划和经验 EP 护理，加州大学戴维斯分校 (UCD) 团队以独特的方式准备创建 EPI-CAL，这是一个 CA 网络，将做出贡献 每年系统地收集来自 6 个社区和 6 个大学的 1000 多名 FEP 客户的结果数据 EP 诊所，连接到由 NIMH EPINET 计划支持的国家 EP 网络。以我们之前的工作为基础 评估 CA EP 项目时，EPI-CAL 项目将在第一年参与形成性评估，以确定 协调网络输入所需的核心 EP 临床特征、干预目标和结果。一个“核心 电池”基于在现场收集的当前测量结果，PhenX 工具包 7 并扩展到涵盖所有关键 域，将在第二年通过网络安装。核心客户结果和数据使用指标 将使用客户、项目和机构的定制 MOBI mHealth 数据网络收集治疗决策 州级，以便轻松进行数据分析、解释和传播。培训和持续监测将 在所有 EPI-CAL 站点提供，以确保适当实施。 EPI-CAL 将贡献去识别化的信息 数据传输至国家协调中心。使用 RE-AIM 实施科学框架 8,9，我们将 从 5 个维度系统地评估 MOBI 对 EP 项目的影响：覆盖范围、功效、采用、 实施和维护（见图 1）。为了展示网络的研究能力，在 R34 作为该应用程序的组成部分，我们建议开发和验证未处理持续时间的衡量标准 精神病 (DUP) 可以在社区环境中使用，并且在心理测量上是合理的。虽然 DUP 是一个 FEP 的短期 CSC 治疗反应 5 和长期结果 10 的显着预测因子，否 目前存在的措施已经过严格验证，可供社区提供者使用7,11。 我们将利用利益相关者（客户、家庭成员、学术专家和 CSC 工作人员）的反馈来制定 具有标准化 DUP 定义的工具，其中包括对精神病发作和开始的锚定评估 治疗。开发这样的工具将能够对 CSC 的关键调节者进行标准化评估 整个 EPINET 的结果。

项目成果

Incorporating Community Partner Perspectives on eHealth Technology Data Sharing Practices for the California Early Psychosis Intervention Network: Qualitative Focus Group Study With a User-Centered Design Approach.

• DOI: 10.2196/44194
• 发表时间: 2023-11-14
• 期刊: JMIR HUMAN FACTORS
• 影响因子: 2.7
• 作者: Tully, Laura M.;Nye, Kathleen E.;Ereshefsky, Sabrina;Tryon, Valerie L.;Hakusui, Christopher Komei;Savill, Mark;Niendam, Tara A.
• 通讯作者: Niendam, Tara A.