Artificial intelligence-based health IT tools to optimize critical care pharmacist resources through adverse drug event prediction

基于人工智能的健康 IT 工具，通过药物不良事件预测来优化重症监护药剂师资源

• 批准号: 10503268
• 负责人: Andrea Sikora
• 金额: $ 38.4万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10503268
• 关键词: Artificial; intelligence; based; health; tools

Critically ill patients are at heightened risk of adverse drug events (ADEs) that worsen outcomes. Critical care pharmacists (CCPs) prevent ADEs, improve patient-centered outcomes, and reduce healthcare costs through performing medication interventions. However, CCPs are an inequitably distributed and non-optimized healthcare resource due to lack of health information technology (IT)-based predictive tools that can identify CCP-driven medication interventions that prevent ADEs. To identify these preventative medication interventions, delineating the medication outcome causal pathway among patient features, medication interventions, ADEs, and patient-centered outcomes is required. Here, novel causal inference methodologies incorporating artificial intelligence (AI) and machine learning (ML) will be applied for the first time to medication safety questions in the ICU. The strategy will focus on developing a novel scoring tool designed for prediction, the medication regimen complexity-intensive care unit (MRC-ICU) Scoring Tool, to predict intervenable ADEs in this causal pathway that are predictable by patient features, preventable by CCPs, and otherwise associated with poor outcomes. The central hypothesis of this AHRQ Health Services Research Project (R01), based on preliminary data, is that an AI-informed dashboard visualizing the medication outcome causal pathway can optimize CCP care to improve patient-centered outcomes. The objective of this work is to apply AI and ML methodology to multi-center data to create prediction tools for integration into visualization dashboards that answer vital questions including (1) what is the best metric for predicting ICU intervenable events and CCP workload?; (2) what causal factors of intervenable events can be prevented by CCPs?; (3) how can CCPs efficiently use AI-based predictions at the bedside? The long-term goal of the proposed work is to establish validated prediction models that can be embedded into dashboards in the electronic health record (EHR) to help guide CCP delivered care. The rationale for this work is that it will establish the MRC-ICU Scoring Tool as a means of predicting medication safety events and CCP interventions. The central hypothesis will be tested using large, multi-center datasets of ICU patients via these specific aims: (1) Create robust prediction models of intervenable events to guide CCP medication interventions; (2) Explore causal relationships among intervenable events, CCP interventions, and outcomes; (3) Design an EHR-integrated platform (ICView) to visualize predictions to guide CCP care. Applying user centered design methods to create a health IT product will result in a visualization dashboard (ICView) that houses MRC-ICU based, AI-informed prediction models for CCP interventions that can improve patient outcomes. This innovative approach applies state-of-the-art ML methodology to causal outcome predictions using the novel MRC-ICU Scoring Tool. The proposed work is significant because any advances in the understanding of how CCPs improve outcomes would have a profound public health impact due to their established role on the interprofessional healthcare team.

重症患者发生药物不良事件（ADE）的风险更高，导致结局恶化。重症监护 药剂师（CCP）预防ADE，改善以患者为中心的结果，并通过以下措施降低医疗成本 进行药物干预。然而，CCP是一个不公平分布和非优化的 由于缺乏基于健康信息技术（IT）预测工具， CCP驱动的预防ADE的药物干预。识别这些预防性药物 干预，描绘患者特征，药物治疗 干预措施，ADE和以患者为中心的结果是必需的。在这里，新颖的因果推理方法 人工智能（AI）和机器学习（ML）将首次应用于药物治疗。 ICU的安全问题该战略将侧重于开发一种为预测而设计的新型评分工具， 药物治疗方案复杂性-重症监护室（MRC-ICU）评分工具，用于预测可干预的ADE 在这一因果途径中，患者特征可预测，CCP可预防， 结果很差。本AHRQ卫生服务研究项目（R 01）的中心假设，基于 初步数据显示，一个可视化药物结果因果路径的AI信息仪表板可以 优化CCP护理，以改善以患者为中心的结果。这项工作的目标是应用AI和ML 方法来创建预测工具，以便集成到可视化仪表板中， 回答重要问题，包括（1）预测ICU可干预事件和CCP的最佳指标是什么 工作量？(2)CCP可以预防哪些可干预事件的因果因素？(3)CCP如何 在床边有效地使用基于人工智能的预测？拟议工作的长期目标是建立 经验证的预测模型，可以嵌入到电子健康记录（EHR）的仪表板中， 帮助指导CCP提供护理。这项工作的基本原理是，它将建立MRC-ICU评分工具， 一种预测药物安全事件和CCP干预的方法。中心假设将被检验 通过这些特定目标使用ICU患者的大型多中心数据集：（1）创建强大的预测模型 可干预事件，以指导CCP药物干预;（2）探索因果关系， 可干预事件、CCP干预措施和结果;（3）设计EHR集成平台（ICView）， 可视化预测以指导CCP护理。应用以用户为中心的设计方法创造健康IT产品 将产生一个可视化仪表板（ICView），其中包含基于MRC-ICU的AI预测模型 用于CCP干预，可以改善患者的结果。这种创新的方法应用了最先进的ML 使用新的MRC-ICU评分工具进行因果结果预测的方法。拟议的工作是 重要的是，在理解CCP如何改善结果方面的任何进展都将产生 由于他们在跨专业医疗团队中的既定角色，他们对公共卫生产生了深远的影响。