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

将指南与 EHR 数据相结合的个性化预防保健推荐系统

• 批准号: 10202935
• 负责人: Xiao Luo
• 金额: $ 15.79万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-23 至 2023-08-09
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10202935
• 关键词: Age; Algorithms; Artificial Intelligence; Behavioral; Centers for Disease Control and Prevention (U.S.); Chronic; Chronic Disease; Clinical; Clinical Data; Data; Data Analyses; Data Element; Diabetes Mellitus; Diagnosis; Disease; Disease Management; Drug usage; Electronic Health Record; Ethnic Origin; Family; Family history of; Future; Gender; Goals; Guidelines; Health; Health Care Costs; Health Expenditures; Health Personnel; Health system; Healthcare; Indiana; Information Retrieval; Intervention; Knowledge; Link; Maps; Medical; Mental Health; Methods; Natural Language Processing; Ontology; Outcome; Patient Care; Patients; Physical Exercise; Prevention; Preventive; Preventive care; Process; Publishing; Recommendation; Recording of previous events; Records; Reporting; Research; Research Proposals; Risk Factors; Social Behavior; Structure; Suggestion; Symptoms; System; Techniques; Text; Tobacco use; United States; base; care systems; clinical data warehouse; clinical decision support; deep learning; design; disability; disorder prevention; electronic data; health data; improved; innovation; learning strategy; mortality; patient engagement; patient portal; patient-clinician communication; personalized care; personalized health care; population health; prevent; problem drinker; screening; social; social determinants; structured data; unstructured data; usability

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.

项目总结 全球大多数卫生系统的设计都是反应性的。疾病控制和预防中心 美国疾病控制与预防中心(CDC)报告称，全国3.3万亿美元的年度医疗支出中有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健康水平的关键 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 大多数电子病历系统中的(CDS)模块仅限于年龄、性别和筛查间隔。这个“一码” 预防保健CDS没有通过考虑风险因素来提供任何个性化的建议 这与患者的家族史、社会行为史、种族和各种慢性病史有关。 社会历史，包括行为和环境决定因素，越来越被认为是关键风险 在美国，导致疾病、残疾和死亡的因素很多。几乎没有什么研究是 将自然语言处理(NLP)技术和人工智能技术应用于 从预防护理指南和EHR数据中提取信息以生成个性化的预防护理 通过考虑风险因素提出建议。因为大多数风险因素，如社会行为，都是 很少系统地从临床记录中摘录，将这些信息与预防护理联系起来仍然是非常困难的 不同寻常。本研究方案的主要目标是开发一个系统，以生成个性化的 通过使用从预防护理指南和 信息，包括从电子健康记录数据中提取的风险因素。个性化的预防建议 将根据EHR数据和预防护理指南提供建议和理由。 我们的长期目标是自动将各种预防护理指南与EHR数据相结合，以 生成个性化的预防护理建议，让更多的患者参与预防护理，并 降低医疗成本，改善人口健康。创新的NLP方法和深度学习- 基于算法可以用来从其他叙事指南中提取信息，以便对它们进行分析 使用电子病历数据。我们将(1)使用建议的基于EHR组件的数据交换结构来分析 信息提取的一致性；(2)从临床指南中自动提取信息；(3)提取 风险因素，如社会行为、症状等风险因素来自结构化和非结构化 使用创新的NLP处理方法处理电子病历数据；(4)评估 通过让医疗服务提供者和患者共同参与的个性化预防护理体系。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。我们严谨的方法和 EHR数据的可用性使未来有可能通过考虑以下因素来探索(1)个性化医疗保健 从电子病历中提取的风险因素，以及(2)改善患者对疾病预防和 利用电子病历数据进行管理。