Transforming Kidney Care in the Emergency Department using Artificial Intelligence Driven Clinical Decision Support

项目总结/摘要 该提案的目的是确定纳入人工智能（AI）的最佳实践方法- 对急救护理的深刻见解本研究将使用迭代开发和评估 AI驱动的临床决策支持（CDS）系统，以预防或减轻急性肾损伤（阿基）作为参考 用例。AHRQ特别强调通知：卫生服务研究优先事项 实现高价值医疗保健系统（NOT-HS-18-015），呼吁开展疾病预防研究 通过将AI纳入医疗保健和干预措施来预防肾脏疾病进展。 急诊科（ED）在危险的决策环境中提供大量患者护理 充满了过度的认知负荷和时间压力。人工智能有可能支持艾德临床医生的决策 通过利用大规模电子健康记录（EHR）数据来帮助预测，从噪声中提取信号， 减少实践中的不利变化。尽管人工智能的热情推广，翻译实践是罕见的， 在艾德中引入可信、透明和可解释的人工智能是未知的。 阿基是AI驱动的预测建模的重要目标。它是普遍存在的，与不良反应密切相关。 结果包括透析和死亡，但认识不足，因此治疗不足。另外很多 艾德治疗无意中促进了阿基和肾脏疾病的进展。阿基预防是可以实现的 在护理点使用循证CDS。我们将使用我们的人工智能驱动的模型，具有早期 和可靠的阿基风险评估，以实现以下具体目标： 目标1：开发一种AI驱动的算法，以促进ED中以AKI为重点的临床决策。 我们将利用以前开发的阿基监测和预测工具， 使艾德临床医生能够预防肾脏疾病进展的算法。 目标2：将AI算法转换为AKI-CDS系统，以实现临床医生-AI的深入研究 我们将建立最终用户的要求，同时创建数据收集工具， 在ED中检查AI。这两项工作都将支持AKI-CDS系统的开发，以进行试点和调查 艾德临床医生对人工智能可信度和可解释性的看法，为多站点实施做准备。 目的3：在ED中对AKI-CDS系统进行多中心有效性实施评价。 我们将在三个艾德研究中心实施人工智能驱动的CDS系统， 同时进行成效和执行情况评价。 拟议的研究将产生新的知识和工具，以推进人工智能在艾德的研究，并将 产生一个可扩展的CDS产品，能够提高提供给更多人的肾脏护理质量， 美国每年有100万患者受到阿基的影响。

PROJECT SUMMARY/ABSTRACT The objective of this proposal is to determine best-practice methods for incorporating artificial intelligence (AI)- derived insights into emergency care. This investigation will use the iterative development and evaluation of an AI-driven clinical decision support (CDS) system to prevent or mitigate acute kidney injury (AKI) as a reference use-case. We are responding to the AHRQ Special Emphasis Notice: Health Services Research Priorities for Achieving a High Value Healthcare System (NOT-HS-18-015), calling for research on prevention of disease through incorporation of AI into healthcare and on interventions to prevent kidney disease progression. Emergency departments (EDs) deliver high-volume patient care in hazardous decision-making environments fraught with excessive cognitive loading and time-pressure. AI has potential to support ED clinician decisions by exploiting large-scale electronic health record (EHR) data to aid prognosis, extract signal from noise, and reduce untoward variability in practice. Despite AI’s fervent promotion, translation to practice is rare and means to incorporate AI that is trustworthy, transparent, and explainable in the ED are unknown. AKI is an important target for AI-driven predictive modeling. It is prevalent and strongly associated with adverse outcomes including dialysis and death, yet is under-recognized and therefore under-treated. In addition, many ED treatments inadvertently promote the progression of AKI and kidney disease. AKI prevention is achievable with evidence-based CDS at the point of care. We will use our AI-driven model, with proven capacity for early and reliable AKI risk estimation, to achieve the following Specific Aims: Aim 1: Develop an AI-driven algorithm for promotion of AKI-focused clinical decision-making in the ED. We will leverage previously developed AKI surveillance and prediction tools to generate a unified EHR-based algorithm that empowers ED clinician prevention of kidney disease progression. Aim 2: Translate the AI algorithm to an AKI-CDS system to enable in-depth study of Clinician-AI interactions in the ED. We will establish end-user requirements while creating data collection instruments to examine AI in the ED. Both efforts will support the development of the AKI-CDS system to pilot and investigate ED clinician perceptions of AI trustworthiness and explainability in preparation for multi-site implementation. Aim 3: Perform a multi-site effectiveness-implementation evaluation of the AKI-CDS system in the ED. We will implement the AI-driven CDS system across three ED study sites using a pragmatic investigational framework to perform effectiveness and implementation evaluations in parallel. The research proposed will generate new knowledge and tools to advance the study of AI in the ED, and will result in a scalable CDS product with the capacity to improve the quality of kidney care delivered to more than 1 million patients affected by AKI in the US each year.