CSI: Assessing Resident Supervision

描述(由申请人提供)： 背景：三分之一的美国住院医师通过退伍军人医疗中心轮换，他们参与了退伍军人管理局37%的医生服务，同时照顾250万退伍军人。尽管退伍军人管理局专业人员在监督病人护理实习生方面发挥了关键作用，但没有科学有效的措施来量化这种监督的强度。因此，对于监督如何影响患者结局、指导受训者提高独立执业能力、或有助于产生临床工作量以满足退伍军人的卫生保健需求，人们了解甚少。目的：本研究的目的是评估临床监督指数(CSI)的表现，CSI是由研究人员通过HSR&D先导研究和VHA/OI&T Greenfield奖设计和开发的VA构思工具，用于定量衡量受训者在住院或门诊遇到预先选定的“活跃”患者时所接受的监督强度。在以下时间完全运行 自2013年5月以来，VA Loma Linda医疗中心在CPRS的II类软件上，CSI的主要功能是：该措施适用于所有健康专业教育实习生(医生、护士、心理学、药剂学等)。并对学员签署的进度说明中描述的临床护理的住院和门诊护理设置和措施进行监督。我们在医疗中心内保持实习生和主管的登记。此外，我们的CSI软件通过CPRS自动管理电子实习生和主管调查，在Vista中搜索负责的监督从业者，在区域服务器上创建合并文件，并以电子方式将这些文件传输到分配资源中心进行处理。目标：我们评估CSI的同时效度(学员和主管在CSI调查中报告的监督时间长度是否与直接观察计算的时间一致？)；结构效度(监督水平是否可预测？)和适用性(调整后的CSI强度分数是否反映学员的临床能力并预测患者护理结果、患者对学员参与的VA护理的满意度、学员教育结果以及学员对其学习经历的满意度？)方法：数据来自VA Loma Linda医疗中心的CSI电子文件、Vista摘录(补充CDW数据)、一年患者随访期间的面对面季度患者调查、两年实习生随访期间基于网络的半年度学员满意度调查、40名实习生组成的小组、在连续性诊所登记的300名II型糖尿病患者小组、90名主管、涉及1,500次会面(索引进度笔记)，以及750次直接观察的会面样本。自2013年5月以来，CSI一直在研究现场全面运行。分析包括组内相关性、平均反应偏差和从广义估计方程计算的效应大小，以解释分级受训人员、主管和患者遭遇水平的数据。

DESCRIPTION (provided by applicant): Background: One-third of all U.S. physician residents rotate through a VA medical center and are involved in 37% of VA's physician services while caring for 2.5 million veterans. Despite the critical role that VA professional staff plays in supervising trainees in patient care, no scientifically valid measure exists to quantify the intensity of that supervision. Thus, little is understood as to how supervision may impact patient outcomes, guide trainees' progress towards competencies to enter independent practice, or contribute to producing clinical workload to meet the health care needs of veterans. Purpose: The purpose of this study is to assess the performance of the Clinical Supervision Index (CSI), a VA conceived instrument designed and developed by the investigators through an HSR&D pilot study and VHA/OI&T Greenfield award to quantitatively measure the intensity of the supervision that trainees receive during inpatient or outpatient encounters with pre-selected "active" patients. Fully functioning at the VA Loma Linda medical center since May 2013 on class II software in CPRS, CSI's key features are: the measure is applicable across all health professions education trainees (physicians, nurses, psychology, pharmacy, etc.) and over inpatient and outpatient care settings and measures supervision for the clinical care described in trainee-signed progress notes. We maintain a registry of trainees and supervisors within the medical center. In addition, our CSI software automatically administers electronic Trainee and Supervisor Surveys through CPRS, searches across VistA for the responsible supervising practitioner, creates merged files on regional servers, and electronically transfers those files to the Allocation Resource Center for processing. Objective: We assess CSI concurrent validity (do the lengths of supervised time that trainees and supervisors report in CSI surveys agree with time computed from direct observations?); construct validity (are supervision levels predictable?); and applicability (do adjusted CSI intensity scores reflect trainee's clinical competencies and predict patient care outcomes, patient satisfaction with trainee- involved VA care, trainee education outcomes, and trainee satisfaction with their learning experiences?) Method: Data are obtained at the VA Loma Linda medical center from CSI electronic files, VistA extracts (supplemented with CDW data), face-to-face quarterly patient surveys during one-year patient follow-ups, web-based semi-annual trainee satisfaction surveys during the two-year trainee follow-ups, panel of 40 trainees, panel of 300 patients with type II diabetes enrolled in the continuity clinic, 90 supervisors, involving 1,500 encounters (indexed progress notes), with 750 encounters sampled for direct observation. The CSI has been fully operational at the study site since May 2013. Analyses include intraclass correlations, mean response biases, and effect sizes computed from Generalized Estimating Equations to account for hierarchical trainee, supervisor, and patient encounter level data.