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.

对于寻求抑郁症帮助的人来说，有多种有效的干预措施。然而，对于不寻求帮助或不遵循治疗转诊的个人，缺乏有效的干预措施。我们建议通过开发和测试基于跨理论模型的干预措施来填补这一服务空白，该干预措施将在人口基础上主动向正在经历抑郁症状但目前未参与治疗的初级保健患者提供。这是第一个针对抑郁症的干预措施，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。在该快速通道计划的第一阶段，目标是规范 TTM 措施、制定 TTM 干预措施，并通过评估研究招募率和对干预材料的反应来确定该方法的可行性。轻度至中度抑郁症筛查呈阳性且未参与治疗的初级保健患者将完成测量标准化调查 (n=100) 或参加干预材料的试点测试 (n=50)。如果满足可行性条件，我们将进行二期随机临床试验，评估专家系统干预抑郁症的疗效。拟议的商业应用：对于医疗保健组织、残​​疾保险公司和雇主来说，抑郁症是一种代价高昂的疾病，因为抑郁症患者的医疗服务利用率、残疾索赔和生产力损失不断增加。一个有效、低成本的专家系统可以降低人群抑郁症的患病率，具有巨大的商业潜力。