Machine learning validation of medication regimen complexity for critical care pharmacist resource prediction

重症监护室（ICU）患者发生药物不良事件（ADE）和不良结局的风险较高。 重症监护药剂师（CCP）预防ADE，改善患者结局，降低医疗成本 通过药物干预。然而，CCP是一种未充分利用的医疗保健资源， 缺乏基于健康信息技术（IT）的预测工具来分配他们为ICU提供的护理 患者目前，没有经过验证的医疗IT工具可用于CCP优化以患者为中心的护理。 这一R21健康信息技术的中心假设，以提高医疗保健质量和 结果奖，基于初步数据，是数据驱动的方法应用于MRC-ICU评分 该工具在预测CCP干预方面的表现将优于基于规则的模型的预测， 患者的结果，并可作为开发新的健康IT工具的基础， 由CCP提供的以患者为中心的护理。MRC-ICU评分工具是第一个旨在测量 ICU患者的用药方案复杂性。为了扩大规模，这一工具需要彻底的验证和IT 基于自动化。本文的目的是将机器学习方法应用于多中心 数据来创建预测工具，以便集成到可视化仪表板中，这些仪表板可回答重要问题， (1)每个患者的CCP干预的预测数量是多少;（2）实时可修改的风险是什么 结果（例如，流体超负荷）;（3）预测的结果是什么（例如，死亡率、住院时间）。很长的- 拟议工作的长期目标是建立有效的预测模型，可以嵌入到 电子健康记录（EHR）中的仪表板，以帮助指导CCP资源部署。的理由 这项工作的目的是建立MRC-ICU评分工具，作为综合患者数据的一种手段， 整合EHR系统。中心假设将使用ICU的大型多中心数据进行检验 患者通过这些特定的目标：（1）应用基于ML的预测方法，以开发一种新的药物模型 方案复杂性作为预测CCP干预和患者结局的指标;（2）比较 不同模型预测CCP干预和患者结局的性能;（3）设计基于网络的 仪表板（ICView），以可视化基于药物治疗方案复杂性的预测。健康IT产品将 产生一个基于Web的仪表板（ICView），其中包含实时、自动化的MRC-ICU评分工具， 除了预测模型的CCP干预，可以改善病人的结果。这一创新 方法将最先进的ML方法应用于新型MRC-ICU评分工具。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。健康 IT产品为未来R18应用于多中心前瞻性试验提供了必要的基础， 评估基于MRC-ICU的CCP资源分配策略。

Intensive care unit (ICU) patients are at heightened risk of adverse drug events (ADEs) and poor outcomes. Critical care pharmacists (CCPs) prevent ADEs, improve patient outcomes, and reduce healthcare costs through performing medication interventions. However, CCPs are an underused healthcare resource due to lack of health information technology (IT)-based predictive tools to allocate the care they provide to ICU patients. Currently, there are no validated health IT tools for CCPs available to optimize patient-centered care. The central hypothesis of this R21 Health Information Technology to Improve Health Care Quality and Outcomes Award, based on preliminary data, is that data-driven methods applied to the MRC-ICU Scoring Tool will out-perform predictions of a rules-based model in predicting CCP interventions that can improve patient outcomes and may serve as the foundation for development of novel health IT tools that optimize the patient-centered care provided by CCPs. The MRC-ICU Scoring Tool is the first tool designed to measure medication regimen complexity in ICU patients. To be scaled-up, this tool requires thorough validation and IT based automation. The objective of this work is to apply machine learning (ML) methodology to multi-center data to create prediction tools for integration into visualization dashboards that answer vital questions including (1) what are the predicted number of CCP interventions per patient; (2) what is the risk of real-time modifiable outcomes (e.g., fluid overload); (3) what are the predicted outcomes (e.g., mortality, length of stay). 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 resource deployment. The rationale for this work is that it will establish the MRC-ICU Scoring Tool as a means of synthesizing patient data for integration across EHR systems. The central hypothesis will be tested using large, multi-center data of ICU patients via these specific aims: (1) Apply ML-based prediction methods to develop a new model of medication regimen complexity as a metric for predicting CCP interventions and patient outcomes; (2) Compare the performance of different models to predict CCP interventions and patient outcomes; (3) Design a web-based dashboard (ICView) to visualize medication regimen complexity-based predictions. The health IT product will result in a Web-based dashboard (ICView) that houses a real-time, automated MRC-ICU Scoring Tool in addition to prediction models for CCP interventions that can improve patient outcomes. This innovative approach applies state-of-the-art ML methodology to 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. The health IT products provide the necessary foundation for a future R18 application for a multi-center, prospective trial to evaluate MRC-ICU based CCP resource allocation strategies.