Intelligent Healthcare Data Management Using AI-Enabled Electronic Health Records

Abstract

Modern healthcare organizations often suffer from problems associated with fragmented patient data, inefficient medical treatment management, and insufficient use of artificial intelligence for clinical decision-making. Electronic Health Record (EHR) solutions represent a promising way to address these challenges by providing a digital alternative for health records management. Still, many existing EHR platforms fail to provide advanced analysis and intelligence for effective health management. Therefore, this research offers an EHR system powered by artificial intelligence that supports advanced treatment management features. The presented platform provides the ability for patients to store their digital health profiles, as well as for doctors and hospitals to document treatments, prescriptions, diagnostic reports, and medical expenses. Moreover, the system utilizes several intelligent modules, which include risk of diseases estimation based on the patient's medical history, automatic creation of report summaries, alerts about prescribed medications, and anomaly detection for suspicious hospital billings. Machine learning techniques, such as Logistic Regression and Random Forest, are used to analyze EHRs' datasets for prediction of potential risks for specific diseases, and data security is provided with the help of controlled access to patient's records. The developed EHR prototype was created using contemporary web technologies combined with AI techniques and evaluated on publicly available healthcare data. Experimental results have shown that a Random Forest model predicted potential risks with an average accuracy of 89%, and AI modules made health data management more convenient.