Using a Computer Adaptive Test to Identify Depressive Disorders in Primary Care

使用计算机自适应测试来识别初级保健中的抑郁症

• 批准号: 7737307
• 负责人: MATTHIAS ROSE
• 金额: $ 32.63万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-18 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7737307
• 关键词: Adverse effects; Algorithms; Attention; Beck depression inventory; Caring; Characteristics; Classification; Clinical; Clinical Research; Communities; Computer software; Computers; Cross-Over Studies; Decision Making; Depressive disorder; Detection; Diagnosis; Diagnostic; Effectiveness; Epidemiologic Studies; Equipment and supply inventories; Floor; Grant; Health; Health Status; Healthcare; Healthcare Systems; Indiana; Individual; Institutes; International; Interview; Length; Life; Measurement; Measures; Medical; Medicine; Mental Health; Mental Health Services; Mental disorders; Meta-Analysis; Methods; Metric; Monitor; Moods; National Institute of Mental Health; New York City; Paper; Patient Monitoring; Patient Self-Report; Patients; Persons; Phase; Physicians; Primary Care Physician; Primary Health Care; Printing; Process; Provider; Psyche structure; Psychometrics; Questionnaires; Randomized; Reporting; Research; Scientist; Screening procedure; Self-Administered; Sensitivity and Specificity; Severities; Small Business Innovation Research Grant; Societies; Solutions; Span 20; System; Technology; Testing; United States; Universities; base; clinical care; clinical practice; college; computerized; depressed; depression; depressive symptoms; follow-up; high risk; improved; instrument; meetings; member; primary care setting; psychologic; public health relevance; response; routine care; success; theories; therapy adherence; tool; treatment adherence

DESCRIPTION (provided by applicant): Mental disorders are among the most prevalent conditions in the United States, and their burden for the individual and society is tremendous. Nevertheless, they are still widely under-diagnosed in community- based health care systems. One way to help primary care physicians identify and monitor mental health problems is to use self-administered patient questionnaires. There are a number of well developed instruments available, but integration into clinical practice has rarely been achieved. Although psychometric characteristics of many tools are good, they still do not meet clinical needs, and a common metric to compare results from different tools is still missing. In addition, paper-pencil questionnaires have to be scored manually, which impose a key barrier for clinical practice, as provider reports for high risk patients must be timely and selective to be effective. Responding to those problems we recently built a Computer Adaptive Test based on the Item Response Theory to assess the mental health status of patients in community based health care settings (MH-CAT). Reports can be printed instantly showing severity of depressive symptoms, self reported treatment and adherence. First evidence has demonstrated that the tool is well accepted, and provides very high measurement precision with almost no floor and ceiling problems assessing the entire continuum from elevated mood to depressive symptoms using only 3-4 items. The underlying item bank allows comparing established depression tools, like the Center for Epidemiologic Studies-Depression Scale and Mental Health Inventory-5. Within a 21/2 -year project we propose to: (1) establish an adaptive algorithm for the MH-CAT to identify depressive disorders with high sensitivity and specificity, (2) demonstrate its feasibility as a routine screening instrument in clinical practice, and (3) assess its impact for case recognition and clinical decision making. To achieve these aims we will evaluate depressed patients from a Primary Care Research Network in Indianapolis. The MH-CAT will be compared to the Patient Health Questionnaire (PHQ-9), and the Beck Depression Inventory (BDI). Two large health centers of the New York City Research and Improvement Networking Group located in underserved communities in the Bronx will introduce the MH-CAT into their routine care. Within a randomized cross-over study we will evaluate the screening success and impact on clinical decision making in comparison with the PHQ-9. All previously not-recognized positive screened patients will be assessed with the Composite International Diagnostic Interview to confirm the diagnostic classification and followed-up for three month to assess which actions have been taken. Approx. 2,500 patients will be included in the study, being carried out together with scientists from the Albert Einstein College of Medicine, Regenstrief Institute, RAND Corporation, Harvard University, and QualityMetric Inc. PUBLIC HEALTH RELEVANCE: The introduction of the Mental Health Computerized Adaptive Test (MH-CAT) will help primary care physicians to identify and monitor patients with mental health problems. The project will demonstrate that an introduction of the MH-CAT is possible with minimal burden for patients and providers. It will have the potential to improve lives of thousands of patients suffering from undiagnosed mental health problems.

描述(申请人提供)：精神障碍是美国最普遍的疾病之一，对个人和社会来说负担是巨大的。然而，在以社区为基础的卫生保健系统中，他们仍然被普遍低估。帮助初级保健医生识别和监测精神健康问题的一种方法是使用自我管理的患者问卷。有许多成熟的仪器可用，但很少能将其整合到临床实践中。虽然许多工具的心理测量学特征都很好，但它们仍然不能满足临床需要，而且还缺乏一个比较不同工具结果的通用度量。此外，纸笔问卷必须手动评分，这对临床实践构成了一个关键障碍，因为为高危患者提供的报告必须及时和有选择性才能有效。针对这些问题，我们最近建立了一个基于项目反应理论的计算机自适应测验来评估社区卫生保健环境中患者的心理健康状况(MH-CAT)。可以立即打印报告，显示抑郁症状的严重程度、自我报告的治疗和坚持。最初的证据表明，该工具被广泛接受，并提供非常高的测量精度，几乎没有地板和天花板的问题，只使用3-4个项目评估从情绪高涨到抑郁症状的整个连续过程。基础题库允许比较已建立的抑郁症工具，如流行病学研究中心抑郁量表和精神健康问卷-5。在一个为期21年半的项目中，我们建议：(1)建立MH-CAT的自适应算法，以识别具有高灵敏度和特异性的抑郁障碍；(2)在临床实践中证明其作为常规筛查工具的可行性；以及(3)评估其对病例识别和临床决策的影响。为了实现这些目标，我们将从印第安纳波利斯的初级保健研究网络对抑郁症患者进行评估。MH-CAT将与患者健康问卷(PHQ-9)和贝克抑郁量表(BDI)进行比较。纽约市研究和改善网络集团位于布朗克斯服务不足社区的两个大型健康中心将把MH-CAT引入他们的日常护理。在随机交叉研究中，我们将与PHQ-9相比较，评估筛查的成功率和对临床决策的影响。所有以前未被确认的阳性筛查患者将通过综合国际诊断访谈进行评估，以确认诊断分类，并进行为期三个月的随访，以评估采取了哪些行动。大约2500名患者将与阿尔伯特·爱因斯坦医学院、雷根斯特里夫研究所、兰德公司、哈佛大学和QualityMetric Inc.的科学家一起进行这项研究。公共卫生相关性：心理健康计算机化适应测试(MH-CAT)的引入将帮助初级保健医生识别和监测有精神健康问题的患者。该项目将证明，引入MH-CAT是可能的，患者和提供者的负担最小。它将有可能改善数千名患有未确诊精神健康问题的患者的生活。