Connected Emergency Care (CEC) Patient Safety Learning Lab

项目总结/摘要 互联紧急护理（CEC）患者安全学习实验室的目标是减少健康 和经济损失的患者下呼吸道感染所造成的支离破碎的紧急护理 系统在这个系统中，临床医生和患者之间缺乏纵向关系，导致护理- 基于背景和实践的学习和基于结果的决策受到缺乏职位的限制， 遇到反馈。这些断开在急诊室（艾德）中由于危险的 决策环境充满了时间压力和过度的认知负荷。电子健康记录- 电子健康记录（EHR）和卫生信息技术（HIT）基础设施具有改善这些问题的潜力。 条件，但目前正在优化对计费和文档，往往加剧断开是- 在临床医生和患者之间。我们打算减少次优诊断造成的健康和经济损失， 使用先进的数据科学方法和EHR集成的临床决策， 离子支持（CDS）。使用这种方法，我们将建立一个连接（闭环）的紧急护理系统- 通过该项目实现以下具体目标： 1.通过以下方式优化疑似LRTI患者的诊断性能：（A）尽量减少过度使用 可避免的成像和实验室测试，和（B）加速检测患有严重LRTI的患者。 2.通过（A）减少不适当的抗生素治疗， 处方，和（B）实现靶向抗生素治疗;适应症、谱和剂量。 3.在艾德就诊完成后，通过减少（A）不必要的 入院患者的长期住院和突然的护理水平变化，以及（B）就诊后30天 出院者的急症护理使用情况。 LRTI患者的临床决策因非特异性和重叠疾病的存在而复杂化， 站和诊断和治疗和处置决定是高度可变的。我们的目标是这三个 在LRTI护理连续性的决策点，以最大限度地提高对健康和成本结果的影响。 这些目标将通过对CDS开发采用系统工程方法并行执行， 将艾德临床医生与患者的就诊前环境、就诊后结果和就诊内环境联系起来， 称之为当前状态和预计状态。先进的数据科学，以用户为中心的设计，人因工程，去 在EHR嵌入式CDS的开发过程中，将采用精密科学和健康信息学原理， 以及由此产生的产品。其设计的核心将是能够适应迪- 与艾德设置，同时有能力利用新推出的EHR传播平台进行扩展 (e.g., Epic App果园）和AHRQ CDS Connect。

PROJECT SUMMARY/ABSTRACT The objective of the Connected Emergency Care (CEC) Patient Safety Learning Lab is to reduce health and financial harm for patients with lower respiratory tract infection caused by a fragmented emergency care system. Within this system, lack of longitudinal relationships between clinicians and patients drives care with- out context and practice-based learning and outcomes-based decision making are limited by absence of post- encounter feedback. These disconnects are compounded in the emergency department (ED) by a hazardous decision making environment fraught with time-pressure and excessive cognitive loading. Electronic health rec- ords (EHR) and health information technology (HIT) infrastructures hold potential for improvement of these conditions, but are currently optimized toward billing and documentation and often exacerbate disconnect be- tween clinicians and patients. We intend to reduce health and financial harms caused by suboptimal diagnosis, treatment, and disposition decisions using advanced data science methods and EHR-integrated clinical deci- sion support (CDS). Using this approach, we will establish a connected (closed-loop) emergency care sys- tem through which the following specific aims are achieved: 1. Optimize diagnostic performance for patients with suspected LRTI by (A) minimizing overuse of avoidable imaging and laboratory testing, and (B) expediting detection of patients with severe LRTI. 2. Increase the specificity of antibiotic treatment for patients with an LRTI by (A) reducing inappropriate prescribing, and (B) enabling targeted antibiotic therapy; indication, spectrum, and dose. 3. Improve transition of care outcomes after the ED encounter is complete by reducing (A) unneces- sary hospitalizations and sudden care-level changes for those admitted, and (B) 30-day post-encounter acute care utilization for those discharged. Clinical decision making for patients with LRTI is complicated by nonspecific and overlapping disease presen- tations and diagnosis and treatment and disposition decisions are highly variable. Our aims target these three decision-points in the LRTI care continuum to maximize impact on health and cost outcomes. These aims will be executed in parallel by taking a systems engineering approach to CDS development that connects ED clinicians to the patient’s pre-encounter context, post-encounter outcomes, and intra-encoun- ter current and projected state. Advanced data-science, user-centered design, human factors engineering, de- cision sciences, and principles of health informatics will be employed during EHR-embedded CDS develop- ment and in the products that result. Central to their design will be the ability to adapt to the uniqueness of di- verse ED settings while having the capacity to scale leveraging newly launched EHR dissemination platforms (e.g., Epic App Orchard) and AHRQ CDS Connect.