TREAT ECARDS: Translating Evidence into Action: Electronic Clinical Decision Support in ARDS

项目摘要 急性呼吸窘迫综合征（ARDS）的相关死亡率为35%， 在许多急性疾病中观察到的发病率和死亡率，例如肺炎、流感、败血症、创伤和 抱负然而，研究一致表明，在高达40%的患者中， 一般低和可变的实践采用表明，提高死亡率的ARDS。这个项目的总体目标是 一个提议的项目是通过促进患者的准确识别来改善对ARDS患者的护理 与ARDS和越来越多的采用证据为基础的干预措施，以改善结果的ARDS。 随着最近出版的多专业机械通气临床实践指南（CPG） 在急性呼吸窘迫综合征中，这项建议的具体目标是： 1)使用自动电子工具（ARDS Sniffer 2.0）增加对ARDS患者的识别， 机器学习算法和自然语言处理的预测分析。 2)翻译最近出版的多专业临床实践指南（CPG）， 将ARDS通气纳入循证、环境适应的电子临床决策支持系统， ARDS（ECARDS）用于ARDS的管理。这一进程将由一个专家小组提供信息， CPG委员会以及一个半定性分析的认知决策过程中，数据的需求 和工作流程的用户在一系列的思考大声研究和焦点小组访谈。ARDS嗅探器2.0， ECARDS和ARDS仪表板显示有关ARDS发生率和比率的近实时数据 最佳护理的使用将被纳入医院EMR，以便与另一个 一系列的有声思考和半定性研究。 3)为了评价ARDS Sniffer 2.0、ARDS Dashboard和ECARDS在阶梯楔中的有效性， Montefiore医疗保健系统内3家医院的随机对照试验，以增加 建议对ARDS进行干预并改善预后。 4)通过我们的专业合作伙伴推广临床决策支持系统 美国胸科学会、重症监护医学会和美国胸科协会的组织 呼吸道护理和应急准备计划。 由于急性呼吸窘迫综合征患者的高血压、高敏度和快速变化的状态，临床上 决策通常默认为自动化的，模式驱动的认知过程， 容易产生无意识的偏见和错误，以及大量不断变化的数据造成的认知过载。因此，在本发明中， 该提案是开发新的临床决策支持工具的理想研究示范项目， 利用大数据和预测分析的力量来识别医院中的ARDS，并提供 基于证据的，专家和用户知情的临床决策支持工具，在正确的时间点， 他们与这些患者的临床互动和工作流程，以促进正确的干预措施， 患者在正确的时间

PROJECT SUMMARY With an associated mortality of 35%, Acute Respiratory Distress Syndrome (ARDS) contributes to the morbidity and mortality seen in many acute conditions such as pneumonia, influenza, sepsis, trauma and aspiration. Yet studies consistently show that ARDS is under-recognized in up to 40% of patients with generally low and variable adoption of practices shown to improve mortality in ARDS. The overall goal of this proposed project is to improve care delivered to ARDS patients by promoting accurate identification of patients with ARDS and increasing adoption of evidence-based interventions shown to improve outcomes in ARDS. With the recent publication of a multi-professional Clinical Practice Guideline (CPG) on mechanical ventilation in ARDS, this proposal specifically aims: 1) To increase identification of ARDS patients with an automated electronic tool (ARDS Sniffer 2.0) powered by the predictive analytics of machine learning algorithms and natural language processing. 2) To translate the recently published multi-professional Clinical Practice Guideline (CPG) on mechanical ventilation in ARDS into an evidence-based, context-appropriate, Electronic Clinical decision support system in ARDS (ECARDS) for the management of ARDS. This process will be informed by an expert panel from the CPG committee as well as a semi-qualitative analysis of the cognitive decision making process, data needs and workflow of users in a series of Think-Aloud studies and focus group interviews. ARDS Sniffer 2.0, ECARDS and an ARDS Dashboard to show near real-time data on the incidence of ARDS and rate of utilization of best care will then be incorporated within the hospital EMR for useability testing with another series of Think-Aloud and semi-qualitative studies. 3) To evaluate the effectiveness of ARDS Sniffer 2.0, ARDS Dashboard, and ECARDS in a stepped-wedge, cluster randomized control trial of 3 hospitals within the Montefiore Healthcare System to increase utilization of recommended interventions in ARDS and improve outcomes. 4) To promote the dissemination of the clinical decision support system through our partner professional organizations of American Thoracic Society, Society of Critical Care Medicine, and American Association of Respiratory Care and Program for Emergency Preparedness. Because of the high pressure, high acuity, and rapidly changing status of the acutely ill ARDS patient, clinical decision making often defaults to the automated, pattern-driven cognitive processes which are particularly prone to unconscious biases and errors and cognitive overload from large volumes of shifting data. As such, this proposal is the ideal research demonstration project to develop a new clinical decision support tool that will harness the power of big data and predictive analytics to identify ARDS in the hospital and deliver an evidenced-based, expert and user informed clinical decision support tool to clinicians at the right point during their clinical interaction and workflow with these patients in order to promote the right interventions to the right patients at the right time.