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 改进症状管理干预措施的发展，以及若干基于证据和共识的干预措施 尽管有了这些准则，但在卫生保健系统中，它们的及时交付仍然参差不齐。我们的研究中心，西北大学 大学影响(NU Impact)，建立在综合癌症症状监测和管理的基础上 NMPRO系统，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的全系统症状管理干预 医疗保健公司(NMHC)肿瘤科门诊诊所。使用临床水平的随机推出 实施试验设计后，我们将进一步试验，采用嵌入式患者水平随机对照临床试验 试验，旨在让参与者积极参与的加强护理(EC)方法的有效性，并 在监测和管理他们的症状方面提高自我效能感。现行的《全国人民革命制度改革条例》将构成 常规护理(UC)以及增加的患者参与度和激活功能将构成EC条件。这 创新的混合试验设计允许对实施情况进行现场内和现场间评估，以及 充分支持以群体为基础的比较，以证明对个别患者结果的有效性。 我们提出了三个具体目标，以患者和系统水平的结果为目标。目标1：实施 涵盖六个临床单位的完全集成的肿瘤学症状评估和管理计划 NMHC医疗保健提供系统；目标2：通过实施混合方案来评估该方案的影响 第一类有效性--评估加强护理计划的实施试验(EC：症状监测和 自我管理干预)，相对于常规护理(UC)对患者和系统级结果的影响；目标3： 确定实施的促进者和障碍，并传播到其他卫生系统。我们将招收一名 估计有12,671人参加。以解决每个临床单元内可能的污染影响，并允许 单位一级的数据收集，用于临床单位内部和跨临床单位实施前和实施后的比较，50%将 在计划实施前进行登记和跟踪。另一半将在实施后招收 将按1：1的比例随机分配(按性别和癌症分期分层；治疗意图，非治愈 意向和生存)到UC VS EC。疲劳、疼痛、焦虑和抑郁将使用 PROMIS®计算机化自适应测试(CATS)。此外，我们将评估患者的临床结果，包括 医疗保健利用率和癌症治疗提供结果。NU Impact研究设计允许连接 评估系统级别的实施和患者级别的有效性度量。一系列广泛的 纵向电子病历数据将使用定制的信息学工具进行集成，并部署用于统计分析 使用融合了心理测量学和生物统计学结果评估模型的计划。这其中的成功要素 然后，干预方法将被推广到广泛采用。

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.