Sunday, December 22, 2024
12/22/2024
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.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).