VA ASSIST Project

VA 协助项目

• 批准号: 8182131
• 负责人: Karl Lorenz
• 金额: --
• 依托单位: VA GREATER LOS ANGELES HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8182131
• 关键词: Accounting; Address; Advanced Malignant Neoplasm; Adverse effects; Age; Alcoholism; Anorexia; Benchmarking; Bipolar Disorder; Cancer Patient; Capital; Caregivers; Caring; Case Mixes; Characteristics; Chronic; Chronic Disease; Clinical; Clinical Services; Colorectal; Colorectal Cancer; Comorbidity; Complex; Computerized Medical Record; Data; Development; Diagnosis; Diagnostic Neoplasm Staging; Disease; Dyspnea; Effectiveness; Elderly; Eligibility Determination; Ethnic Origin; Etiology; Evaluation; Facility Controls; Family; Fatigue; Funding; Gender; Guidelines; Health Services Research; Healthcare Systems; Hour; Inpatients; Intervention; Investments; Leadership; Life; Linear Regressions; Link; Los Angeles; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Measures; Medical; Medicare; Mental Depression; Methodology; Metric; Modeling; National Cancer Institute; Nausea and Vomiting; Needs Assessment; Non-Small-Cell Lung Carcinoma; Nurses; Nursing Staff; Outcome; Outpatients; Pain; Palliative Care; Patients; Performance; Persons; Physicians; Policies; Process; Process Measure; Protocols documentation; Quality Indicator; Quality of Care; Quality of life; Race; Recording of previous events; Registered nurse; Research; Resources; Sampling; Schizophrenia; Selection for Treatments; Services; Solid Neoplasm; Staging; Substance abuse problem; Supportive care; Symptoms; System; Toxic effect; Training; Translating; Uncertainty; Universities; Variant; Veterans; Workload; advanced disease; austin; base; cancer care; cohort; cost; data modeling; evidence base; experience; health care quality; neoplasm registry; oncology; palliative; satisfaction; screening; severe mental illness; tool

Objectives: Using a national random sample of veterans diagnosed with colorectal and nonsmall cell lung cancer drawn from the VA national cancer registry, we will use the Cancer Quality-ASSIST indicator set to evaluate the overall quality of veterans' supportive cancer care, as well as: 1. Patient-level factors and facility factors that may be associated with overall quality, and 2. Whether palliative care services use is associated with higher quality supportive care. Research Plan: We will use the VA national cancer registry and link it to Austin data in order to identify approximately 750 veterans with advanced lung and colorectal cancer. We will conduct remote chart abstraction using the VA electronic medical record and an Access graphical user data entry tool. Methodology: A cohort of veterans with advanced cancer will be developed by obtaining data about incident cases in 2008 with metastatic disease. We will also obtain data about earlier stage incident cases for the previous 2 years. We will link these files with Austin data (patient treatment files) to determine preliminary eligibility (based on inpatient, outpatient usage and disease status). PTF files will provide patient data to supplement patient characteristics obtained from chart review. We will link patient data to KLFMenu files to characterize facilities. Following revision of the Cancer Quality ASSIST guidelines and quality indicator abstraction tool,oncology and other experienced nurses with a background in chart abstraction will train using the ASSIST guidelines and protocol to achieve an initial kappa for key variables of 0.8 or higher. Nurses will follow an abstraction protocol of approximately 2.75 hours / case to evaluate the quality of symptoms related to cancer and its complications, treatment-related toxicities, and information and care planning needs. Palliative care service use and descriptions will be obtained from chart review. Analyses will evaluate the overall quality of supportive cancer care, and control for age, gender, type of cancer, stage, co-morbidity, as well as the analytic variables of race / ethnicity, serious mental illnesses, and palliative care service use. Complete models will control for patient and facility factors. Given likely selection effects, we will explore the use of treatment selection models including propensity matching and instrumental variable analysis to evaluate the effect of service use. We will attempt multivariable linear regression and explore count data models for main models and consider evaluating facility effects with fixed effects and multi-level approaches. Results: This is a new project and results have not yet been obtained. Significance: Information on the quality of supportive care management, veterans who are particularly vulnerable and may receive lower quality supportive care, and the effectiveness of palliative care services in addressing these issues is urgently needed to information programmatic development, and other research and clinical priorities.

目标：使用诊断为结直肠和非小细胞肺的退伍军人的国家随机样本 癌症是从VA国家癌症注册中心得出的，我们将使用设置的癌症质量辅助指标 评估退伍军人支持性癌症护理的整体质量，以及： 1。可能与总体质量相关的患者级因素和设施因素， 2。姑息治疗服务是否与更高质量的支持护理有关。 研究计划：我们将使用VA国家癌症注册表并将其链接到Austin数据以识别 大约750名退伍军人患有晚期肺癌和结直肠癌。我们将进行远程图表 使用VA电子病历和访问图形用户数据输入工具抽象。 方法论：通过获取有关事件的数据，将开发患有晚期癌症的退伍军人队列 2008年患有转移性疾病的病例。我们还将获得有关早期阶段事件案例的数据 前2年。我们将将这些文件与Austin数据（患者治疗文件）联系起来，以确定初步 资格（基于住院，门诊使用和疾病状况）。 PTF文件将为患者数据提供 从图表审查获得的补充患者特征。我们将将患者数据与KLFMENU文件链接到 特征设施。经过修改癌症质量协助指南和质量指标 抽象工具，肿瘤学和其他具有图表抽象背景的经验丰富的护士将使用 辅助指南和协议，以实现0.8或更高的关键变量的初始KAPPA。护士会 遵循大约2.75小时 /案例的抽象方案，以评估与 癌症及其并发症，与治疗相关的毒性以及信息和护理计划需求。姑息治疗 护理服务的使用和描述将从图表审查中获得。分析将评估整体质量 支持性癌症护理，以及对年龄，性别，癌症类型，阶段，合并症的控制以及 种族 /种族，严重的精神疾病和姑息治疗服务的使用分析变量。完整的型号 将控制患者和设施因素。鉴于可能的选择效果，我们将探索治疗的使用 选择模型，包括倾向匹配和仪器可变分析，以评估 服务使用。我们将尝试多变量线性回归并探索主要模型的计数数据模型 并考虑通过固定效果和多层次方法评估设施效应。 结果：这是一个新项目，尚未获得结果。 意义：有关支持护理管理质量的信息，特别是退伍军人 易受伤害，可能会获得较低的质量支持护理，以及在 迫切需要解决这些问题的信息编程开发以及其他研究以及 临床重点。