CRII: SCH: Towards Smart Patient Flow Management: Real-time Inpatient Length of Stay Modeling and Prediction

CRII：SCH：迈向智能患者流程管理：实时住院患者住院时间建模和预测

基本信息

批准号：
2246158
负责人：
Yuxin Wen
金额：
$ 17.44万
依托单位：
Chapman University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246158&HistoricalAwards=false
关键词：
CRII SCH Towards Smart Patient

项目摘要

Patient length of stay has been used as an essential criterion for the effective planning and management of hospital resources. Prolonged stay increases patients’ risk of hospital-acquired infections and disrupts patient flow and access to high-quality healthcare services. As such, a model that can reliably predict the length of stay for a specific patient is desirable to mitigate the prolonged stay and guide personalized decision-making. However, the length of stay can be affected by a multitude of factors and can vary based on different patients’ conditions and disease progression. The complex and dynamic nature of massive clinical data, not to mention the presence of a large portion of missing and censored values in the healthcare data, poses significant challenges for efficient modeling and dynamic prediction. This project aims to offer an integrated solution by establishing a pipeline consisting of advanced statistical modeling, monitoring, and deep learning techniques based on patient information collected from heterogeneous medical systems over time. The success of the project will catalyze a transition from a traditional standard-driven discharge scheduling service to a data-driven proactive scheduling paradigm. The success of the project will alleviate the hospital’s pressure on resource allocation and improve patient flow and, more importantly, pandemic preparedness. The project can provide opportunities for research-based interdisciplinary training of undergraduate and graduate students in health informatics, statistics, and artificial intelligence from diverse backgrounds, including women and underrepresented minorities.This project will address the critical challenges of healthcare data analysis, i.e., heterogeneity, multi-modality, and data sparsity. Conventional data-driven methods have been predominantly focused on identifying the factors that strongly influence the length of stay as opposed to predicting the length of stay itself. Moreover, the existing approaches failed to address the inherent uncertainty and were incapable of incorporating different data modalities and dynamic prediction. The project proposes a personalized framework by integration of advanced tensor fusion and time-to-event modeling techniques towards smart patient flow management, which ultimately allows for faster achievement of health outcomes and reduction of hospitalized costs. The proposed intelligent framework will advance the state-of-art research of real-time data fusion and personalized prognosis in the following aspects: (1) brings the data fusion and length of stay prediction into a unified framework; (2) facilitates personalized length of stay prediction in a real-time manner; (3) naturally has the capability to incorporate uncertainties in the decision-making process to provide a confident and intelligent scheduling service. Although the methodology is proposed for the patient length of stay prediction, it does not depend on any restrictive assumptions of domain knowledge and specific disease and thus can potentially be applied to a broad range of events predictions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

患者的住院时间已被用作有效计划和管理医院资源的基本标准。长时间的住宿增加了患者出现医院获得感染的风险，并破坏了患者的流量并获得高质量的医疗服务。因此，可以可靠地预测特定患者住院时间的模型，以减轻长期停留和指导个性化的决策。但是，住院时间可能会受到多种因素的影响，并且会根据不同患者的状况和疾病进展而有所不同。大规模临床数据的复杂而动态的性质，更不用说医疗保健数据中存在很大一部分缺失和公民值的存在对有效的建模和动态预测面临着重大挑战。该项目旨在通过建立一个管道来提供一个集成的解决方案，该管道基于随着时间的推移从异质医疗系统收集的患者信息而包括先进的统计建模，监视和深度学习技术。该项目的成功将催化从传统的标准驱动排放计划服务到数据驱动的主动调度范式的过渡。该项目的成功将减轻医院对资源分配的压力，并改善患者流动，更重要的是大流行准备。该项目可以为来自潜水员背景（包括妇女和代表性不足的少数族裔）的健康信息，统计和人工智能的本科和研究生的基于研究的跨学科培训提供机会。该项目将解决医疗数据分析的关键挑战，即，即异性，多模态性，多模态性和数据稀释性。常规数据驱动的方法主要集中在确定强烈影响住院时间的因素，而不是预测住院时间的时间。此外，现有方法未能解决继承的不确定性，并且无法纳入不同的数据方式和动态预测。该项目通过将高级张量融合和活动时间建模技术集成到智能患者流量管理中，提出一个个性化的框架，这最终可以更快地实现健康结果并降低住院费用。拟议的智能框架将在以下方面推进对实时数据融合和个性化预后的最新研究：（1）将数据融合和停留预测的长度带入统一的框架；（2）以实时的方式促进个性化的住宿预测；（3）自然有能力将不确定性纳入决策过程中，以提供自信而聪明的调度服务。尽管该方法是针对患者的住院时间预测提出的，但并不取决于对领域知识和特定疾病的任何限制性假设，该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来审查标准，被认为是通过评估来获得的支持。