Tailored preventative care recommendations using electronic health records

Abstract

According to the U.S. Centers for Disease Control and Prevention (CDC), 90% of the nation’s $3.3 trillion annual healthcare expenditures are attributed to individuals with chronic and mental health conditions. Thus, preventing diseases is essential to improving public health and managing escalating healthcare costs. However, the preventive care clinical decision support (CDS) modules in most electronic health record (EHR) systems primarily rely on basic criteria such as age, gender, and screening intervals. This “one-size-fits-all” approach fails to provide personalized recommendations that consider patient-specific risk factors, such as family history, social history, ethnicity, and chronic conditions. This research develops an information system that analyze patient-specific data against the information extracted from preventive care guidelines to generate tailored recommendations along with justifications grounded in both the EHR data and preventive care guidelines. Our system empowers patients by providing personalized insights into their potential health risks before issues arise. By analyzing factors such as family history, social background, ethnicity, chronic conditions, age, gender, and past medical history, it delivers tailored recommendations based on established guidelines. With this information, individuals can take a proactive approach to their health, engaging in informed discussions with their physicians to explore preventive measures. By promoting early intervention and personalized care, this system has the potential to reduce the burden of preventable diseases and improve long-term health outcomes.Management Science and Information System