Development of a Clinical Decision Support Tool for Facilitating Naturalistic Decision-Making and Improving Blood Culture Utilization

脓毒症是儿科重症监护病房（PICU）中常见的临床实体， 严重的发病率和死亡率。血培养（BC）作为识别血流感染的唯一方法， 代表脓毒症诊断和治疗的基石。然而，过度使用BC已被证明 导致额外的实验室检查，不必要的抗生素使用，延长住院时间， 医疗费用。用于提高BC利用率的现有临床决策支持（CDS）工具侧重于使用 分析方法，以确定血流感染的预测概率。这些CDS工具不能 解决临床决策的自然和直观性质，因此，可能不会被采用 供应商和提高决策质量。提高业务连续性的利用率，减少其对 医疗质量，安全性和效率，创新的CDS工具，借鉴了分析 和自然的决策是必要的。 在本研究中，我们建议开发一个电子健康记录嵌入CDS工具，以提高BC 利用，这将利用分析决策的力量，综合大量的数据， 促进自然决策。我们将采用社会技术系统方法，即，的系统 患者安全工程倡议2.0模型和以用户为中心的设计方法，即，情境设计， 指导CDS工具的开发，这是一个迭代过程，包括三个步骤： 首先，我们将研究与获得BC（目标1）相关的个人和团队认知工作。我们将 通过背景调查和关键决策方法与供应商进行面谈， 他们的信息需求和决策时使用的策略，获得BC。 其次，根据目标1的结果，我们将与提供者合作开发CDS工具（目标2）。 将与供应商进行焦点小组设计会议，以（1）将目标1的结果转化为设计 需求，（2）设计和评估模型，（3）制定实施计划。成果 焦点小组设计会议将用于开发和迭代评估CDS工具的原型。 最后，我们将在约翰霍普金斯医院的儿科重症监护室实施CDS工具，并评估其使用情况， 对BC利用率和患者结局的影响。虽然定性观察和访谈数据， 将收集计算机生成的定量数据，以评估CDS工具的使用， 将使用系列设计评估CDS工具对BC利用率和患者结局的影响。 这项研究将证明，CDS工具开发使用分析决策，以促进 自然主义决策可以减少PICU中BC的使用，而不会影响患者的结局。的 CDS工具和用于开发CDS工具的方法将推广到更广泛的医疗保健领域 超越BC的问题

Sepsis is a frequently encountered clinical entity in pediatric intensive care units (PICUs) leading to significant morbidity and mortality. Blood cultures (BCs), as the only way to identify blood stream infections, represent a cornerstone of sepsis diagnosis and treatment. The overuse of BCs, however, has been shown to result in additional laboratory tests, unnecessary antibiotic use, prolonged hospitalization, and increased healthcare costs. Existing clinical decision support (CDS) tools for improving BC utilization focus on the use of analytic approaches to determine the pretest probability of bloodstream infections. These CDS tools cannot address the naturalistic and intuitive nature of clinical decision-making and, therefore, may not get adopted by providers and improve decision quality. To improve BC utilization and reduce its negative impacts on healthcare quality, safety, and efficiency, innovative CDS tools drawing upon the strengths of both analytical and naturalistic decision-making are needed. In this study, we propose to develop an electronic health record-embedded CDS tool for improving BC utilization, which will use the strength of analytical decision-making in synthesizing large amounts of data to facilitate naturalistic decision-making. We will apply a sociotechnical systems approach, i.e., the Systems Engineering Initiative for Patient Safety 2.0 model, and a user-centered design method, i.e., contextual design, to guide the development of the CDS tool, which is an iterative process including three steps: First, we will examine individual and team cognitive work associated with obtaining a BC (Aim 1). We will conduct shadowing with contextual inquiry and critical decision method interviews with providers to identify their information needs and strategies used when making decisions regarding obtaining a BC. Second, based on findings from Aim 1, we will develop the CDS tool in collaboration with providers (Aim 2). Focus group design sessions will be conducted with providers to (1) translate findings from Aim 1 into design requirements, (2) design and evaluate mock-ups, and (3) develop an implementation plan. Outcomes of the focus group design sessions will be used to develop and iterative evaluate prototypes of the CDS tool. Finally, we will implement the CDS tool in the PICU at the Johns Hopkins Hospital and assess its use and impacts on BC utilization and patient outcomes. While qualitative observation and interview data and computer-generated quantitative data will be collected to assess the use of the CDS tool, an interrupted time series design will be used to assess the impacts of the CDS tool on BC utilization and patient outcomes. This study will demonstrate that the CDS tool developed to use analytical decision-making to facilitate naturalistic decision-making can reduce BC utilization in the PICU without influencing patient outcomes. The CDS tool and the approach used to develop the CDS tool will be generalizable to a broader set of healthcare issues beyond BCs.