Identifying Quality Indicators for Substance Use Disorders

描述（由申请人提供）：拟议的二级数据分析是创新研究议程的第一步，旨在开发相关且可行的QIs，并得到最有力证据的支持。制定相关、可行和有效的质量指标是一项关键的公共政策优先事项，在药物滥用治疗方面最需要质量措施。在国家质量论坛认可的615项标准中，只有一项是针对物质使用的。尽管两种类型的合并症都很普遍，但目前还没有针对MH&SA疾病（MCOD）或身体健康和SA疾病（PCOD）同时发生的措施。我们建议进行二次数据分析，以确定具有最佳指标特征的物质使用障碍（sud）和M/P cod的潜在QIs。我们将使用由我们的研究团队为退伍军人健康管理项目评估开发的独特数据集（包括管理数据、患者调查和服务系统管理员调查的丰富组合）。该数据集包含通过研究人员、临床医生和政策制定者的共同努力创建的60多个质量指标，以及指标可靠性和可行性的数据。虽然这60个候选QIs中的每一个都有精心制定的规范，但在前瞻性队列研究中测试所有这些指标是低效且昂贵的。这种二次分析将使我们能够筛选出一组可能的措施，以获得最有希望和最可靠的候选措施，这些措施可以在随后的研究中在更广泛的人群中进行严格的测试。因此，这一次要数据分析是关键的第一步，它将为在广泛的公共和私人服务系统中进行前瞻性验证研究奠定基础。我们还使用创新的方法来开发复合质量指标，以表征为sud患者提供的所有护理的质量，因为并非所有流程都适用于所有患者

DESCRIPTION (provided by applicant): The proposed secondary data analysis is the first step in an innovative research agenda to develop relevant and feasible QIs supported by the strongest level of evidence. Developing relevant, feasible and valid QIs is a key public policy priority, and nowhere is the need for quality measures greater than in substance abuse treatment. Of the 615 National Quality Forum endorsed standards, only one is specific to substance use. There are no measures for co-occurring MH&SA disorders (MCOD) or co-occurring physical health and SA disorders (PCOD), despite the prevalence of both types of co-morbidity. We propose a secondary data analysis to identify potential QIs for substance use disorders (SUDs) and M/P CODs with the best-performing indicator characteristics. We will use a unique data set (consisting of a rich combination of administrative data, patient surveys, and surveys of service system administrators) developed for the Program Evaluation of the Veterans Health Administration by our research team. This data set contains over 60 QIs created through a collaborative effort of researchers, clinicians, and policy makers, as well as data on indicator reliability and feasibility. While each of these 60 candidate QIs has carefully developed specifications, it would be inefficient and costly to test all of them in a prospective cohort stud. This secondary analysis will allow us to winnow down the set of possible measures to the most promising and robust candidates that can be rigorously tested on a broader population in subsequent studies. Therefore, this secondary data analysis represents a critical first step, which will provide a foundation for a prospective validation study in a broad range of public and private service systems. We also use innovative methods to develop composite QIs to characterize the quality of all care provided patients with SUDs, as not all processes apply to all patients, and high-quality care is best described as the sum of many individual processes. Our specific aims are: Aim 1:. To evaluate the association between individual QIs and outcomes for individuals with SUDs and to identify the QIs with the best-performing indicator characteristics. Aim 2:. To evaluate the association between individual QIs and outcomes for individuals with M/P CODs and to identify QIs with the best- performing indicator characteristics. Aim 3:. To characterize the quality of all MH/SA treatment provided to patients using a composite QI and to evaluate its association with outcomes. To our knowledge, this secondary data analysis is the first to propose a comprehensive analysis of process outcome links for over 60 QIs across a broad range of outcomes in order to identify the most promising candidate QIs with the strongest indicator properties. It is the first step in an innovative research agenda to develop relevant and feasible QIs supported by the strongest level of evidence. PUBLIC HEALTH RELEVANCE: The Institute of Medicine has identified serious problems with the quality of mental health and substance abuse care in the United States and has identified improving quality and treatment outcomes as critical public health and public policy objectives. Developing quality measures is a public policy priority, and nowhere is the need for quality measures greater than in substance abuse treatment. We propose a secondary data analysis to identify potential quality measure with the best-performing characteristics. It represents a criticl first step that will provide a foundation for a future prospective-validation study.