Automated Point-of-Care Surveillance of Outpatient Delays in Cancer Diagnosis

描述（由申请人提供）： 背景资料：许多错过和延迟的癌症诊断是由于怀疑癌症的异常发现的沟通和协调失败造成的，这些异常发现通常首先出现在初级保健环境中。我们以前在VA的工作表明，尽管通过电子健康记录可靠地提供了测试结果，但异常测试结果的后续延迟仍然存在。检测这些延迟和识别“高风险”患者的方法还不成熟，需要优化以在患者对齐护理团队（PACTs）中使用。我们进行了试点工作，以确定使用电子查询或“触发器”是否可以主动识别有延迟癌症诊断风险的患者。触发器促使审查选定的医疗记录，并提供可能的护理延迟证据（例如，30天后未记录异常胸部X线随访的图表）。在病历审查中，超过一半的触发器识别的病历被确认为错过了随访（阳性预测值[PPV] >50%）。然而，我们的团队确认这些延迟并将其传达给供应商的流程效率低下，而且需要大量资源。目的：在我们的试点工作的基础上，我们建议开发和测试一种创新的自动监测干预措施，以改善初级保健实践中五种常见癌症（结直肠癌、前列腺癌、肺癌、肝细胞癌和乳腺癌）的及时诊断和随访。我们的方法将使用VA信息学和计算基础设施（芬奇）来触发医疗记录，并提供异常检测结果后续可能延迟的证据。为了指导我们的工作，我们将使用基于临床信息学和人为因素原则的8维社会技术模型。我们的具体目标是：1）评估基于芬奇的“实时”自动监测系统的准确性，以识别5种常见癌症的漏诊或延迟诊断风险。2)通过采用信息学和人为因素工程学原则，确定如何将风险患者的“实时”监测和信息交流整合到PACT护理点。3)评价与常规护理相比，自动监测干预对诊断过程及时性和成本效益的影响。方法：研究中心包括VISN 12中的设施。在目标1中，我们将使用迭代方法来开发和测试算法，以“触发”在预定义的癌症诊断线索后缺乏记录的后续行动的记录。操作触发器所需的数据元素已经作为企业数据仓库的一部分存在。我们将对测试队列应用触发算法，将其输出与手动图表审查进行比较，以确认延迟，并使用这些数据修改算法以改善触发PPV。已完成的触发器将应用于验证 通过相同的方法确定最终PPV。在目标2中，我们将使用访谈，任务分析，参与式设计技术和可用性测试，以确保自动化干预将适合现实世界的临床实践的工作流程。我们将确定向PACT传输数据的技术要求，探索向PACT团队传达信息的最佳方式，并进行可用性测试以评估通知设计。在目标3中，我们将进行一项随机分组对照试验，将VISN 12例PACTs随机分配至干预或常规治疗组。干预措施将包括：1）芬奇平台内的每日数据提取，以识别存在诊断延迟风险的患者;以及2）与VISN 12中的PACT团队自动沟通，了解哪些患者正在经历潜在的延迟。我们的结果是从诊断线索到后续行动的中位时间（例如，潜血阳性后至结肠镜检查的时间）以及接受适当和及时随访护理的患者比例。为了确定干预的成本效益，我们将使用每避免一个额外的延迟癌症诊断病例的增量成本的措施。我们的研究结果将提供有关自动干预措施的有效性和价值的重要信息，以识别和减少癌症相关的诊断延迟。

DESCRIPTION (provided by applicant): Background: Many missed and delayed cancer diagnoses result from breakdowns in communication and coordination of abnormal findings suspicious for cancer, which often first emerge in the primary care setting. Our previous work in the VA has shown that delays in the follow-up of abnormal test results persist despite reliable delivery of test results through the electronic health record. Methods to detect these delays and identify "high risk" patients are underdeveloped and need to be optimized for use within Patient Aligned Care Teams (PACTs). We conducted pilot work to determine whether the use of electronic queries, or "triggers," can proactively identify patients at risk of delayed cancer diagnosis. Triggers prompted review of selected medical records with evidence of possible care delays (e.g., a chart with no documented follow-up of an abnormal chest X-ray after 30 days). More than half the charts identified by the triggers were confirmed on chart review to have missed follow-up (positive predictive values [PPVs] >50%). However, the processes by which our team confirmed these delays and communicated them to providers were inefficient and resource intensive. Objectives: Building on our pilot work, we propose to develop and test an innovative automated surveillance intervention to improve timely diagnosis and follow-up of five common cancers in primary care practice (colorectal, prostate, lung, hepatocellular, and breast). Our methodology will use the VA Informatics and Computing Infrastructure (VINCI) to trigger medical records with evidence of potential delays in follow-up of abnormal test results. To guide our work, we will use an 8-dimension, socio-technical model built on principles from clinical informatics and human factors. Our specific aims are to: 1) Evaluate the accuracy of a VINCI- based "real-time" automated surveillance system to identify patients at risk of missed or delayed diagnosis of 5 common cancers. 2) Establish how to integrate "real-time" surveillance and communication of information about at-risk patients into the point of PACT care through adoption of informatics and human factors engineering principles. 3) Evaluate effects of the automated surveillance intervention on timeliness of the diagnostic process and cost-effectiveness as compared with usual care. Methods: Study sites include facilities in VISN 12. In Aim 1, we will use an iterative approach to develop and test algorithms to "trigger" records lacking documented follow-up action after pre-defined diagnostic clues for cancer. Data elements needed to operationalize our triggers already exist as part of the Corporate Data Warehouse. We will apply trigger algorithms to test cohorts, compare their output against manual chart reviews to confirm delays and use these data to modify the algorithms to improve trigger PPVs. The finalized triggers will be applied to validation cohorts to determine the final PPVs through the same methods. In Aim 2 we will use interviews, task analysis, participatory design techniques, and usability testing to ensure that the automated intervention will fit within the workflow of real-world clinical practice. We will determine the technical requirements to transmit data to the PACTs, explore the best ways of communicating the information to the PACT team, and conduct usability testing to evaluate notification designs. In Aim 3 we will conduct a cluster randomized controlled trial with VISN 12 PACTs randomly assigned to intervention or usual care. Intervention will consist of: 1) daily data extraction withn the VINCI platform to identify patients at risk of diagnostic delays; and 2) automated communication to PACT teams in VISN 12 about which of their patients are experiencing potential delays. Our outcomes are the median time in days from diagnostic clue to follow-up action (e.g., time to colonoscopy after a positive hemoccult) and the proportion of patients receiving appropriate and timely follow-up care. To determine cost effectiveness of the intervention, we will use a measure of incremental cost per additional delayed cancer diagnosis case averted. Our findings will provide important information on the effectiveness and value of automated interventions to identify and reduce cancer-related diagnostic delays.