Integrating patient-reported outcomes and electronic health record data to improv

描述(由申请者提供)：该项目将利用健康信息技术收集患者的抑郁症严重程度和药物耐受性数据，并将其与电子健康记录(EHR)数据整合为以患者为中心的自动反馈给临床医生。最终目标是在各种初级保健环境中改善以患者为中心的抑郁症护理、用药依从性和抑郁症结果。初级保健医生(PCP)开出了超过一半的抗抑郁药物，但在初级保健环境中，服药依从率和治疗成功率很低。在一个组织内的少量综合初级保健实践中，研究发现，涉及使用病例管理人员收集患者报告的结果并向PCP提供详细反馈的抑郁症护理管理计划在改善患者的用药依从性和抑郁症状方面有效。这些计划的主要缺点包括代表诊所护士和其他工作人员所需的时间，以及实践中的金钱负担。我们的系统以电子方式从患者那里收集数据，并向临床医生提供自动化和可操作的反馈，这一系统有可能减轻实践的负担，并导致与通常的护理相比，有效的抑郁症治疗增加。自动化系统还将可扩展，以在各种实践环境中实施。在这个项目中，将利用一个大型的全国初级保健实践网络(ENQUIRENet)来开发和测试一个自动化系统，以电子方式收集服用抗抑郁药物治疗抑郁症的患者的数据。患者报告的结果将从就诊期间的患者中收集，并将包括与药物相关的副作用耐受性和抑郁严重程度的测量。数据将在六个月内的多个时间点从患者那里收集，并将以电子方式整合到现有的患者建议报告中，在每次临床会诊时提供给临床医生。还将根据患者副作用耐受性和抑郁严重程度的适应症驱动的算法，在临床就诊之间向临床医生提供自动特别更新。数据收集和增强的反馈系统将在eNQUIRENet的八个实践中进行试点测试。在试点试验结束时，临床医生和 参加eNQUIRENet实践的患者将接受采访，以评估数据收集和增强反馈系统的内容、时间、实施、效用和相关的资源负担。本申请中包含的具体目标直接响应了AHRQ在PAR-08-269中指出的感兴趣的研究领域，特别是“支持以患者为中心的护理的卫生IT”和“通过使用集成数据和知识管理改善卫生保健决策的卫生IT”。

DESCRIPTION (provided by applicant): This project will leverage health information technology to collect depression severity and medication tolerability data from patients and integrate with electronic health record (EHR) data into automated patient- centered feedback to clinicians. The ultimate goal is to improve patient-centered depression care, medication adherence, and depression outcomes in a variety of primary care settings. Primary care physicians (PCPs) prescribe more than half of all antidepressant medications, yet rates of medication adherence and treatment success are low in primary care settings. Depression care management programs involving the use of case managers to collect patient-reported outcomes and provide detailed feedback to PCPs were found to be effective in improving medication adherence and depression symptoms for patients in a small number of integrated primary care practices within one organization. Major drawbacks to these programs include the time required on behalf of clinic nurses and other staff and the monetary burdens on the practice. Our system of electronically collecting data from patients and providing automated and actionable feedback to the clinician has the potential to decrease the burdens on the practice and lead to an increase in effective depression treatment compared to care as usual. The automated system will also be scalable to be implemented in a wide variety of practice settings. In this project, a large, nationl network of primary care practices (eNQUIRENet) will be leveraged to develop and test an automated system to electronically collect data from patients taking an antidepressant to treat depression. The patient-reported outcomes will be collected from patients in-between clinic visits and will include measures of medication-related side effect tolerability and depression severity. Data will be collected from patients at multiple time points over a six-month period and will be electronically integrated into an existing Patient Recommendation Report provided to clinicians at each clinical encounter. Automated ad hoc updates will also be provided to clinicians between clinic visits based on algorithms driven by patient indications of side effect tolerability and depression severity. The data collection and enhanced feedback system will be pilot tested in eight eNQUIRENet practices. At the end of the pilot test, samples of clinicians and participating patients from the eight eNQUIRENet practices will be interviewed in order to assess the content, timing, implementation, utility, and associated resource burden of the data collection and enhanced feedback system. The specific aims included in this application directly respond to research areas of interest indicated by AHRQ in PAR-08-269, specifically "Health IT to support patient-centered care," and "Health IT to improve health care decision making through the use of integrated data and knowledge management."