Identifying Quality Indicators for Substance Use Disorders

确定药物使用障碍的质量指标

• 批准号: 8519401
• 负责人: Teresa Jo Hudson
• 金额: $ 22.09万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8519401
• 关键词: Accountability; Accreditation; Administrator; Alcohol or Other Drugs use; Caring; Characteristics; Cohort Studies; Comorbidity; Consensus; Data; Data Analyses; Data Set; Development; Disease; Effectiveness; Ensure; Face; Foundations; Future; Guidelines; Health Policy; Individual; Institute of Medicine (U.S.); Level of Evidence; Link; Literature; Maps; Measures; Mental Health; Methods; Outcome; Patients; Performance; Policy Maker; Population; Practice Guidelines; Prevalence; Process; Process Measure; Program Evaluation; Property; Provider; Public Health; Public Policy; Quality Indicator; Quality of Care; Research; Research Personnel; Resources; Services; Substance Use Disorder; Substance abuse problem; Sum; Surveys; System; Testing; To specify; Treatment outcome; United States; United States Substance Abuse and Mental Health Services Administration; Veterans; Washington; base; behavioral health; cohort; effective therapy; financial incentive; health administration; improved; innovation; physical conditioning; prospective; public health relevance; substance abuse treatment; validation studies

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.

描述（由申请人提供）：拟议的二次数据分析是创新研究议程的第一步，旨在开发由最强有力的证据支持的相关且可行的质量指标。制定相关、可行和有效的质量标准是一项重要的公共政策优先事项，而在药物滥用治疗方面最需要采取质量措施。在国家质量论坛批准的 615 项标准中，只有一项专门针对物质使用。尽管两种类型的共病都很普遍，但没有针对同时发生的 MH&SA 疾病 (MCOD) 或同时发生的身体健康和 SA 疾病 (PCOD) 的措施。我们建议进行二次数据分析，以识别具有最佳指标特征的物质使用障碍 (SUD) 和 M/P COD 的潜在 QI。我们将使用我们的研究团队为退伍军人健康管理局项目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）。该数据集包含由研究人员、临床医生和政策制定者共同努力创建的 60 多个 QI，以及有关指标可靠性和可行性的数据。虽然这 60 个候选 QI 中的每一个都精心制定了规范，但在前瞻性队列研究中测试所有这些指标效率低下且成本高昂。这种二次分析将使我们能够筛选出最有前途和最稳健的候选人的一系列可能的措施，这些候选人可以在后续研究中在更广泛的人群中进行严格的测试。因此，二次数据分析是关键的第一步，它将为广泛的公共和私人服务系统中的前瞻性验证研究奠定基础。我们还使用创新方法来开发综合 QI，以描述为 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 高质量的护理最好被描述为许多单独过程的总和。我们的具体目标是： 目标1：。评估个体 QI 与 SUD 个体结果之间的关联，并确定具有最佳表现指标特征的 QI。目标2：。评估个体 QI 与 M/P COD 个体结果之间的关联，并确定具有最佳表现指标特征的 QI。目标3：。使用综合 QI 来表征向患者提供的所有 MH/SA 治疗的质量，并评估其与结果的关联。据我们所知，这项二次数据分析首次提出对广泛结果中 60 多个 QI 的过程结果链接进行全面分析，以便识别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，旨在制定由最强有力的证据支持的相关且可行的质量指标。