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，目的是开发得到最有力证据支持的相关和可行的量化指标。制定相关的、可行的和有效的QI是一项关键的公共政策优先事项，而在治疗药物滥用方面，对质量措施的需求最大。在615国家质量论坛认可的标准中，只有一项是针对物质使用的。目前还没有针对MH和SA共病(MCOD)或共病(PCOD)的措施，尽管这两种类型的共病普遍存在。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(SUDS)和M/P疾病的潜在QI。我们将使用由我们的研究团队为退伍军人健康管理局的计划评估开发的独特数据集(由管理数据、患者调查和服务系统管理员调查的丰富组合组成)。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QI，以及关于指标可靠性和可行性的数据。虽然这60个候选QI中的每一个都仔细制定了规范，但在未来的队列种马中测试所有这些QI将是低效和昂贵的。这一二次分析将使我们能够筛选出一套可能的措施，以确定最有希望和最强大的候选人，这些候选人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，这一次级数据分析是关键的第一步，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qi来表征所有为sud患者提供的护理的质量，因为并不是所有的流程都适用于所有患者。 高质量的护理最好地描述为许多个体过程的总和。我们的具体目标是：目标1：。评估个体QI与SUDS患者预后之间的关联，并确定具有最佳指标特征的QI。目标2：。评估个人QI与患有M/P疾病的个体的结果之间的关联，并确定具有最佳指标特征的QI。目标3：。使用复合QI来表征所有MH/SA治疗的质量，并评估其与结果的相关性。据我们所知，这项二次数据分析首次提出了对广泛结果范围内60多个QI的过程结果链接的全面分析，以确定具有最强指标属性的最有希望的候选QI。这是创新研究议程的第一步，即开发有最强证据支持的相关和可行的QI。

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.