PFI:AIR - TT: A Clinical Predictive Model Based Smart Decision Support System for Congestive Obstructive Pulmonary Disease (COPD) related Re-hospitalization

这个PFI: AIR技术翻译项目的重点是翻译智能预测建模技术，以开发临床决策支持系统（CDSS），以满足减少充血性阻塞性肺疾病（COPD）患者再次住院的需求。在美国，慢性阻塞性肺病影响了近2400万人，是第三大死因。CDSS很重要，因为它为患者提供了更好的护理质量，同时大大节省了与再入院有关的费用。该项目将产生一个智能CDSS的原型，这将使从业人员更好地了解慢性阻塞性肺病患者是否有望在出院后30天内再次住院。这种基于云的CDSS将具有以下独特功能：能够利用电子或纯文本格式的患者信息，源自大数据分析、自然语言处理和机器学习的新算法，并能够向医疗保健专业人员提供关于高再住院风险患者的警报。这些特点提供了以下优势：通过系统可访问性提高护理过程的效率，有效利用和整合各种来源的患者相关信息，通过早期和预防性干预降低护理总成本，提高患者的护理质量和生活质量。此外，这种CDSS将有助于减少护理的可变性，从而使历来绩效较低的医院能够从最佳实践中受益。该项目解决了以下技术差距，因为它从研究发现转化为临床决策支持系统领域的商业应用。主要的技术差距是缺乏用于开发预测模型的结构化和非结构化数据的融合，自然语言处理的有限使用，以及严重缺乏本地和全球临床信息的整合。该项目提供了一个全面的平台，通过整合以CCD、CCR或CCDA格式提供的结构化本地和全球临床数据与非结构化临床数据（如医生笔记和实验室报告）来解决这些差距。此外，该系统还利用基于UIMA的ctake自然语言处理子系统对非结构化数据进行分析，该子系统是为COPD开发的。CDSS的预测能力基于多种前沿技术，包括数据挖掘、机器学习、自然语言处理和数据可视化技术。此外，参与该项目的研究生将有机会学习医学信息系统的跨学科领域及其在现实世界中的应用。此外，这些学生将在技术转让和商业化活动方面得到指导。拉金社区医院（美国最大的D.O.项目）和Humana（佛罗里达州最大的健康计划）将为该项目提供临床数据和专业知识。该项目可在医院、健康计划、负责任的保健组织和管理的保健组织中有效部署，以避免与慢性阻塞性肺病有关的住院再入院，从而在提高护理质量的同时大幅节省与住院再入院有关的费用。

This PFI: AIR Technology Translation project focuses on translating smart predictive modeling technologies to develop a Clinical Decision Support System (CDSS) to fill the need for reducing re-hospitalization for patients with Congestive Obstructive Pulmonary Disease (COPD). COPD affects almost 24 million people in the U.S. and is the 3rd leading cause of death. The CDSS is important because it provides better quality of care for patients while providing significant costs savings related to hospital readmissions. The project will result in a prototype of a smart CDSS, which will enable practitioners to better understand if a COPD patient is expected to be a candidate for hospital readmission within 30 days of discharge. This cloud-based CDSS will have the following unique features: ability to utilize patient information available either in electronic or plain text format, novel algorithms derived from big data analytics, natural language processing and machine learning and ability to provide alerts to healthcare professionals about high readmission risk patients. These features provide the following advantages: improving the efficiency of the care process through system accessibility, effectively utilizing and integrating relevant patient information from various sources, reducing total cost of care through early and preventative intervention, and improving the quality of care and quality of life of the patients. Further, this CDSS will help reduce the variability in care so that hospitals with historically lower performance can benefit from best practices.This project addresses the following technology gaps as it translates from research discovery toward commercial application in the area of clinical decision support systems. The predominant technical gaps are a lack of fusion of structured and unstructured data for the development of predictive models, a limited use of natural language processing, and a significant lack of integration of local and global clinical information. This project provides a comprehensive platform to address these gaps by integration the structured local and global clinical data available in either CCD, CCR or CCDA format with the unstructured clinical data such as physician notes and laboratory reports. Further the system utilizes UIMA based cTAKES natural language processing sub-system that is developed for COPD to analyze this unstructured data. The predictive capabilities of CDSS are based on multiple leading-edge technologies including data mining, machine learning, natural language processing and data visualization techniques. In addition, graduate students involved in this project will have opportunities to learn the interdisciplinary domain of medical information systems and their real-world applications. In addition, these students will be mentored in technology transfer and commercialization activities. Larkin Community Hospital (largest D.O. program in the U.S.) and Humana (the largest health plan in Florida) will provide clinical data and expertise to the project. This project could be effectively deployed at hospitals, healthplans, accountable care organizations and managed care organizations for avoiding hospital readmissions related to COPD thereby, providing significant savings related to hospital readmissions while improving quality of care.