An Expert System to Reduce Depression in Primary Care

减少初级保健中抑郁症的专家系统

• 批准号: 6708226
• 负责人: DEBORAH A LEVESQUE
• 金额: $ 58.19万
• 依托单位: PRO-CHANGE BEHAVIOR SYSTEMS, INC.
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-25 至 2004-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6708226
• 关键词: artificial intelligence; behavioral /social science research tag; cognitive behavior therapy; computer assisted diagnosis; computer assisted patient care; depression; health behavior; health care model; health care service utilization; health services research tag; human subject; human therapy evaluation; mass screening; medical outreach /case finding; mental disorder diagnosis; outcomes research; patient oriented research; primary care physician

A variety of effective interventions exist for people who seek help for depression. However, there is a lack of effective interventions for individuals who do not seek help or follow through with treatment referrals. We propose to fill this gap in services by developing and testing a Transtheoretical Model-based intervention that will be delivered proactively, on a population basis, to primary care patients who are experiencing symptoms of depression but are not currently involved in treatment. It is the first intervention for depression that is appropriate for individuals in all stages of change-not merely the minority who are prepared to take action. In Phase I of this Fast Track Initiative, the aims are to norm TTM measures, develop the TTM interventions, and determine the feasibility of this approach by assessing study recruitment rates and reactions to the intervention materials. Primary care patients who screen positive for mild to moderate depression and are not involved in treatment will complete surveys for measurement norming (n=100) or participate in a pilot-test of the intervention materials (n=50). If feasibility conditions are met, we will conduct a randomized clinical trial in Phase II to assess the efficacy of the expert system intervention for depression. PROPOSED COMMERCIAL APPLICATIONS: Depression is a costly illness for health care organizations, disability insurers, and employers because of increased health service utilization, disability claims, and lost productively among depressed individuals. An effective, low-cost expert system that can reduce the prevalence of depression on a population basis has significant commercial potential.

为寻求抑郁症帮助的人提供了各种有效的干预措施。然而，对于不寻求帮助或坚持治疗转介的个人，缺乏有效的干预措施。我们建议通过开发和测试跨理论的基于模型的干预措施来填补这一服务缺口，该干预措施将在人群的基础上主动提供给正在经历抑郁症状但目前没有参与治疗的初级保健患者。这是对抑郁症的第一次干预，适用于处于所有变化阶段的个人--而不仅仅是准备采取行动的少数人。在这个快车道计划的第一阶段，目标是规范TTM措施，制定TTM干预措施，并通过评估研究人员的招聘率和对干预材料的反应来确定这种方法的可行性。未参与治疗的轻度至中度抑郁筛查阳性的初级保健患者将完成测量常模调查(n=100)或参与干预材料的中试(n=50)。如果符合可行性条件，我们将在第二阶段进行随机临床试验，以评估专家系统干预抑郁症的疗效。建议的商业应用：抑郁症是一种对医疗保健组织、残疾保险公司和雇主来说代价高昂的疾病，因为在抑郁症患者中，医疗服务利用率、残疾索赔和生产性损失都有所增加。一种有效、低成本的专家系统，可以在人群基础上降低抑郁症的患病率，具有巨大的商业潜力。