Human Factors and Implementation Evaluation of Pediatric AI Sepsis Model in the Pediatric Emergency Department

儿科急诊科儿科AI脓毒症模型的人为因素及实施评价

基本信息

  • 批准号:
    10648925
  • 负责人:
  • 金额:
    $ 5.17万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-01 至 2025-07-31
  • 项目状态:
    未结题

项目摘要

Sepsis is a leading cause of morbidity and mortality in children with estimated mortality rate of 16.9% and pediatric hospitalization cost of $7Billion. Artificial Intelligence (AI) has been touted as tool to improve sepsis outcomes and many AI models have been developed for improving outcomes related to pediatric. However, there is little high-quality evidence of improved patient outcomes in clinical studies and there are studies with conflicting outcomes related to AI interventions. Current real-world evaluations only measure predictive performance of the models, but do not provide insight into factors contributing to success/ failure of AI interventions. While human centered evaluations were done previously in laboratory settings and in simulations, we are not aware of any real-world evaluations in acute care settings. The goal of this proposal is to measure implementation outcomes and establish the feasibility of measuring the mechanism of impact via human performance aspects such as trust, situational awareness, and workload. In aim 1 of the project, we will evaluate the implementation of an existing pediatric AI sepsis model in the pediatric emergency department. In aim 2 we will demonstrate the feasibility of measuring the influence of sepsis AI on human performance in a real-world acute care setting. This project will use a combination of interview methods and electronic health record data to demonstrate the feasibility of measuring human performance. Demonstrating feasibility will provide preliminary data for a subsequent hybrid implementation trial of sepsis AI interventions in acute care settings. This line of study will ultimately yield insights to harness AI technology for improving acute and critical illness outcomes.
败血症是儿童发病和死亡的主要原因,估计死亡率为16.9%, 儿科住院费用为70亿美元。人工智能(AI)已被吹捧为改善败血症的工具 已经开发了许多AI模型来改善儿科相关的结果。然而,在这方面, 在临床研究中,几乎没有高质量的证据表明患者结局得到改善, 与人工智能干预相关的矛盾结果。目前的现实世界评估只能衡量预测性 模型的性能,但没有提供对AI成功/失败因素的洞察力 干预措施。虽然以人为中心的评价以前是在实验室环境中进行的, 模拟,我们不知道在急性护理环境中的任何真实世界的评价。这项提案的目的是 衡量执行成果,并确定衡量影响机制的可行性, 人员绩效方面,如信任、态势感知和工作负载。在项目目标1中,我们将 评估现有儿科AI脓毒症模型在儿科急诊科的实施情况。在 目的2:我们将证明测量脓毒症AI对人类表现的影响的可行性, 现实世界的急性护理环境。本项目将采用访谈方法和电子健康相结合的方式 记录数据以证明测量人的绩效的可行性。论证可行性将 为随后的脓毒症AI干预急性护理混合实施试验提供初步数据 设置.这一研究领域最终将产生见解,利用人工智能技术改善急性和关键性的 疾病的结果。

项目成果

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Swaminathan Kandaswamy的其他文献

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