A personalized preventive care recommendation system by integrating guidelines with the EHR data

项目摘要 全球大多数卫生系统的设计都是反应性的。美国疾病控制和预防中心 (CDC)美国的一份报告称，美国每年3.3万亿美元的医疗保健支出中有90%用于 患有慢性病和精神疾病的人。因此，预防疾病是提高人们生活质量的关键。 健康和控制不断上升的医疗费用。预防保健临床决策支持中的标准 (CDS)大多数EHR系统中的模块仅限于年龄、性别和筛查间隔。这个“一个尺寸” 适合所有人”的预防性护理CDS不提供任何考虑风险因素的个性化建议 涉及患者的家族史、社会行为史、种族和各种慢性病史。 社会历史，包括行为和环境决定因素，越来越多地被认为是关键风险 在美国，许多疾病，残疾和死亡的原因。很少有研究已经 应用自然语言处理（NLP）技术和人工智能技术， 从预防保健指南和EHR数据中提取信息，以生成个性化预防保健 建议考虑风险因素。由于大多数风险因素，如社会行为， 很少从临床记录中系统地提取，将这些信息与预防保健联系起来仍然非常困难。 少见.本研究的主要目标是开发一个系统，以生成个性化的 通过使用从预防保健指南和 信息，包括从EHR数据中提取的风险因素。个性化的预防建议 将根据EHR数据和预防保健指南提供建议和理由。 我们的长期目标是将各种预防保健指南与EHR数据自动集成， 生成个性化的预防护理建议，让更多的患者参与预防护理， 降低医疗成本，提高人口健康水平。创新的NLP方法和深度学习- 可以使用基于算法从其他叙述性指南中提取信息， EHR数据。我们将（1）使用一个建议的EHR组件为基础的数据交换结构，分析 一致地提取信息;（2）自动从临床指南中提取信息;（3）提取 危险因素，如社会行为，症状和其他危险因素，从结构化和非结构化 使用创新的NLP处理EHR数据;（4）评估 个性化的预防保健系统，包括医疗保健提供者和患者。我们将利用 印第安纳州患者护理网络（INPC）-全州范围的临床数据仓库。我们严格的方法和 EHR数据的可用性使未来有可能探索（1）个性化医疗保健， 从EHR中提取的风险因素，以及（2）改善患者对疾病预防的参与， 利用EHR数据进行管理。

PROJECT SUMMARY Most health systems globally were designed to be reactive. The Centers for Disease Control and Prevention (CDC) of the United States reported that 90% of the nation's $3.3 trillion annual healthcare expenditures are for people with chronic and mental health conditions. Therefore, preventing diseases is key to improving people's health and keeping rising health costs under control. The criteria in the preventive care clinical decision support (CDS) modules in most of the EHR systems are limited to age, gender, and screening intervals. This "one size fits all" preventive care CDS does not provide any personalized recommendations by considering the risk factors that relate to a patient's family history, social behavior history, ethnicity, and various chronic disease history. Social history, including behavioral and environmental determinants, are increasingly recognized as critical risk factors for many causes of disease, disability, and mortality in the United States. Very little research has been conducted on applying Natural Language Processing (NLP) techniques and artificial intelligence techniques to extract information from the preventive care guidelines and EHR data to generate personalized preventive care recommendations by considering the risk factors. Since most of the risk factors, such as social behaviors are rarely systematically extracted from the clinical notes, linking this information to preventive care is still very uncommon. The main objective of this research proposal is to develop a system to generate personalized preventive recommendations by using information extracted from the preventive care guidelines and the information, including risk factors extracted from the EHR data. The personalized preventive recommendations will provide the recommendations as well as rationales based on the EHR data and preventive care guidelines. Our long-term goal is to automate the integration of various preventive care guidelines with the EHR data to generate personalized preventive care recommendations, to engage more patients in preventive care, and to reduce the healthcare cost and improve population health. The innovative NLP methods and deep learning- based algorithms can be used to extract information from other narrative guidelines so that they to be analyzed with the EHR data. We will (1) use a proposed EHR component-based data interchange structure to analyze the extracted information consistently; (2) extract information from the clinical guidelines automatically; (3) extract the risk factors, such as social behaviors, symptoms and other risk factors from the structured and unstructured EHR data using innovative NLP processing; (4) evaluate the efficiency, accuracy and usability of the personalized preventive care system through involving both healthcare providers and patients. We will utilize the Indiana Network for Patient Care (INPC) - a statewide clinical data warehouse. Our rigorous methods and the availability of the EHR data make it possible in the future to explore (1) personalized healthcare by considering risk factors extracted from the EHR, and (2) improved patient engagement in disease prevention and management by utilizing EHR data.