Implementation and Evaluation of an Expanded Bilingual Electronic Symptom Management Program across a Multi-site, Fully-integrated Comprehensive Cancer Center

癌症和癌症治疗会引起许多症状，这些症状会对生活质量产生不利影响。尽管 制定改进的症状管理干预措施，以及一些基于证据和共识的 尽管这些服务的及时提供与指导方针相一致，但在卫生保健系统中仍然不均衡。我们的研究中心，西北大学 大学IMPACT（NU IMPACT）建立在综合癌症症状监测和管理的基础上 系统（NMPRO），目前由我们的医疗保健提供系统部署。我们将测试一种 在西北纪念医院实施全系统症状管理干预 医疗保健公司（NMHC）门诊肿瘤诊所。使用临床水平的随机展开 实施试验设计，我们将进一步测试，采用嵌入式患者水平随机对照临床 试验，加强护理（EC）方法的有效性，旨在积极吸引参与者，并 提高自我效能，监测和管理他们的症状。现有的NMPRO将构成 常规护理（UC）以及增加的患者参与和激活功能将构成EC条件。这 创新的混合试验设计允许在研究中心内和研究中心间对实施情况进行评价， 充分把握度的基于组的比较，以证明对个体患者结局的有效性。 我们提出了三个具体的目标，针对患者和系统级的结果。目标1：实施 一个完全集成的肿瘤症状评估和管理计划，横跨六个临床单位， 目标2：通过进行混合评估该计划的影响 I类有效性-实施试验，以评估加强护理计划（EC：症状监测和 自我管理干预），相对于常规护理（UC）对患者和系统水平结局的影响;目标3： 确定执行的促进因素和障碍，并向其他卫生系统传播。我们将招收一名 估计有12，671人参加。为了解决每个临床单元内可能的污染影响，并考虑到 在临床单位内部和之间进行实施前和实施后比较的单位级数据收集，50%将 在项目实施前进行登记和跟踪。另一半将在实施后入组 并将以1：1的比例随机分配（按性别和癌症分期分层;治愈性，非治愈性 意图和生存率）到UC与EC。疲劳、疼痛、焦虑和抑郁将使用 PROMIS®计算机化适应性测试（CAT）。此外，我们还将评估患者的临床结局，包括 医疗保健利用和癌症治疗交付结果。NU IMPACT研究设计允许联合 评估系统级实施和患者级有效性指标。一系列广泛的 纵向EHR数据将使用定制信息学工具进行整合，并用于统计分析 使用混合心理测量和生物统计结果评估模型的计划。成功要素 然后将干预方法推广到广泛采用。

Cancer, and cancer treatment, cause many symptoms that can adversely affect quality of life. Despite the development of improved symptom management interventions, and several evidence- and consensus-based guidelines, their timely delivery remains uneven in the health care system. Our research center, Northwestern University IMPACT (NU IMPACT), builds upon an integrated cancer symptom monitoring and management system (NMPRO), currently deployed by our health care delivery system. We will test the effectiveness of a system-wide symptom management intervention when implemented across the Northwestern Memorial HealthCare Corporation (NMHC) outpatient oncology clinics. Using a clinic-level randomized roll-out implementation trial design, we will further test, using an embedded patient-level randomized controlled clinical trial, the effectiveness of an enhanced care (EC) approach aimed to engage participants actively, and to increase self-efficacy, in the monitoring and managing of their symptoms. The existing NMPRO will constitute usual care (UC), and added patient engagement and activation features will comprise the EC condition. This innovative hybrid trial design allows for a within- and between-site evaluation of implementation along with a sufficiently powered group-based comparison to demonstrate effectiveness on individual patient outcomes. We propose three specific aims that target both patient- and system-level outcomes. Aim 1: Implement a fully-integrated oncology symptom assessment and management program across six clinical units that are part of the NMHC healthcare delivery system; Aim 2: Evaluate the impact of the program by conducting a hybrid Type I effectiveness-implementation trial to evaluate the enhanced care program (EC: symptom monitoring & self-management intervention), relative to usual care (UC) on patient- and system-level outcomes; Aim 3: Identify facilitators and barriers to implementation and disseminate to other health systems. We will enroll an estimated 12,671 participants. To address possible contamination effects within each clinical unit, and allow for unit-level data collection for pre- and post-implementation comparison within and across clinical units, 50% will be enrolled and followed prior to program implementation. The other half will be enrolled post-implementation and will be randomly assigned at a 1:1 ratio (stratified by sex and cancer phase; curative intent, non-curative intent & survivorship) to UC versus EC. Fatigue, pain, anxiety, and depression will be assessed using PROMIS® computerized adaptive tests (CATs). In addition, we will assess patient clinical outcomes, including healthcare utilization, and cancer treatment delivery outcomes. The NU IMPACT study design allows joint evaluation of system-level implementation and patient-level effectiveness metrics. An extensive array of longitudinal EHR data will be integrated using custom informatics tools and deployed for statistical analysis using plans that blend psychometric and biostatistical outcome evaluation models. Successful elements of this intervention approach will then be extended to widespread adoption.