An Innovative Framework for AI & ML based Smart EMR Systems in Healthcare Sector

Abstract

Healthcare technology has advanced significantly since the introduction of Electronic Medical Record (EMR) systems in 1972. Nevertheless, many medical professionals still find it difficult to incorporate EMR systems into their routines with ease, even with these developments. The traditional pen and paper methods of Medical Prescriptions are still preferred in busy outpatient settings as they are quick and easy. Despite providing templates and pre-filled formats, current EMR systems still need clinicians to manually enter a great deal of information for every patient contact, including symptoms, clinical findings, investigations, and prescriptions. In addition to adding to physician burnout and workload, this laborious data input process takes time away from providing direct patient care. Drawing on recent research, this overview of the literature highlights a number of current EMR systems' shortcomings. The main problem that is brought to light is how time-consuming data entry is and how this interferes with doctors' capacity to provide patient care. This research suggests a novel solution to these problems: an Artificial Intelligence (AI) and machine learning-powered smart electronic medical record system that is integrated with a mobile application. By utilizing machine learning algorithms that learn from every encounter and provide physicians with real-time support, this cutting-edge solution seeks to expedite the data entry process. The goal of the Smart EMR system is to make the doctor-EMR interaction more effective and collaborative by automating repetitive processes and providing intelligent suggestions. The voice-to-text capabilities for quick data entry, adaptive templates that dynamically modify based on patient information, and predictive analytics to foresee clinical needs are some of the key characteristics of the proposed system. The Smart EMR system seeks to reduce physician burnout and boost overall efficiency in healthcare delivery by eliminating manual input and improving user experience. Future topics for study include enhancing machine learning algorithms for tailored patient interactions, guaranteeing privacy and data security, and evaluating the long-term effects on clinical results and physician satisfaction. In order to improve physician productivity and patient care quality in contemporary healthcare settings, AI integration with EMR systems constitutes a paradigm change.