Decision Making and Clinical Work of Test Result Follow-up in Health IT Settings

健康IT环境中检测结果跟踪的决策和临床工作

• 批准号: 8892082
• 负责人: HARDEEP SINGH
• 金额: $ 49.91万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8892082
• 关键词: Decision; Making; Clinical; Work; Test

DESCRIPTION (provided by applicant): Background: Failure to follow up abnormal test results is a significant safety concern in outpatient settings and often leads to patient harm and malpractice claims. Electronic health records (EHRs) can help ensure reliable delivery of abnormal test results, but they do not guarantee that this results in appropriate follow-up action. Our work in the Veterans Health Administration (VA) reveals that almost 8% of abnormal outpatient test results transmitted as EHR-based alerts lacked follow-up at 4 weeks. We subsequently found that follow-up of abnormal tests is influenced by multitude of technological factors (software/hardware) and non-technological factors (user behaviors, workflow, information load, policies and procedures, training and other organizational factors). Improving test result follow-up will require a better understanding of how follow-up processes fit within the complex "socio-technical" context of EHR-enabled health care. It is especially important to clarify how these contextual features influence the cognitive processes that are necessary to perceive, comprehend, and act on abnormal findings in a timely manner. Given that laboratory test result reporting is a component of Stage 2 meaningful use, further exploration of vulnerabilities in EHR-based test result follow-up is imperative. Objectives/Methods: We propose to apply human factors-based frameworks to understand system and cognitive vulnerabilities that affect EHR-based outpatient test result follow-up. To better define the contex of clinical work that affects decision-making in this area, we will use a conceptual model that posits a set of eight socio-technical dimensions that must be considered in the real-world use of IT. Building on our prior work in the VA, our study settings include clinics affiliated with 3 non-A institutions in order to improve generalizability. In Aim 1, we will identify the cognitive factorsthat affect test result follow-up processes in EHR-based health systems. We will conduct record reviews to identify recent abnormal test results with and without timely follow- up and conduct cognitive task analysis interviews with providers who ordered the tests. We will also assess the cognitive load of EHR-based alerts related to test results. In Aim 2, we will characterize the nature of clinical work required for individuals and teams to respond appropriately to abnormal test results in EHR-enabled outpatient settings. To map these processes at each site, we will collect qualitative data using rapid assessment techniques (structured observations, brief surveys, and key informant interviews). Our interpretation of these data will include consideration of how different socio-technical factors (e.g. EHR design, workflow, and organizational factors) interact and affect the cognitive work of test result follow-up. In Aim 3, e will conduct prospective risk assessments to characterize the particular work processes and features of the socio-technical context that are most vulnerable to failure within and across our study sites. This foundational work will lead to better understanding of the "basic science" of missed test results and will clarify targets for future interventions to improve follow-up of abnormal test results in EHR-enabled outpatient settings.

描述（由申请人提供）：背景：未能跟进异常的测试结果是一个重要的安全问题，在门诊设置，往往导致患者伤害和医疗事故索赔。电子健康记录（EHR）可以帮助确保异常检测结果的可靠交付，但它们不能保证这会导致适当的后续行动。 我们在退伍军人健康管理局（VA）的工作表明，近8%的异常门诊测试结果作为基于EHR的警报传输，在4周内缺乏随访。我们随后发现，异常测试的后续工作受到众多技术因素（软件/硬件）和非技术因素（用户行为，工作流程，信息负载，政策和程序，培训和其他组织因素）的影响。改进测试结果后续工作将需要更好地理解后续工作流程如何与 EHR支持的医疗保健的复杂“社会技术”背景。特别重要的是要澄清这些背景特征如何影响认知过程，这些认知过程是及时感知，理解和对异常结果采取行动所必需的。鉴于实验室检测结果报告是第二阶段有意义使用的一个组成部分，进一步探索基于EHR的检测结果后续工作中的漏洞势在必行。目的/方法：我们建议应用人为因素为基础的框架，以了解系统和认知的漏洞，影响电子健康档案为基础的门诊测试结果的后续行动。为了更好地定义影响这一领域决策的临床工作的背景，我们将使用一个概念模型，该模型假定了一组在现实世界中使用IT时必须考虑的8个社会技术维度。基于我们在VA的先前工作，我们的研究设置包括与3个非A机构附属的诊所，以提高普遍性。在目标1中，我们将确定在基于EHR的卫生系统中影响测试结果后续过程的认知因素。我们将进行记录审查，以确定最近的异常测试结果，有和没有及时跟进，并进行认知任务分析采访的供应商谁订购的测试。我们还将评估与测试结果相关的基于EHR的警报的认知负荷。在目标2中，我们将描述个人和团队在启用EHR的门诊环境中对异常检测结果做出适当反应所需的临床工作的性质。为了在每个站点绘制这些过程，我们将使用快速评估技术（结构化观察，简短调查和关键线人访谈）收集定性数据。我们对这些数据的解释将包括考虑不同的社会技术因素（例如EHR设计，工作流程和组织因素）如何相互作用并影响测试结果随访的认知工作。在目标3中，我们将进行前瞻性风险评估，以描述我们研究中心内部和研究中心之间最容易发生故障的特定工作流程和社会技术背景特征。这项基础性工作将使人们更好地了解漏测结果的“基础科学”，并将澄清未来干预措施的目标，以改善电子健康记录门诊环境中异常检测结果的后续工作。