ENHANCING ELECTRONIC HEALTH RECORDS SYSTEMS AND DIAGNOSTIC DECISION SUPPORT SYSTEMS WITH LARGE LANGUAGE MODELS

Within Electronic Health Record (EHR) Systems, physicians face extensive documentation, leading to alarming mental burnout. The disproportionate focus on data entry over direct patient care underscores a critical concern. Integration of Natural Language Processing (NLP) powered EHR systems offers relief by reducing time and effort in record maintenance.

Our research introduces the Automated Electronic Health Record System, which not only transcribes dialogues but also employs advanced clinical text classification. With an accuracy exceeding 98.97%, it saves over 90% of time compared to manual entry, as validated on MIMIC III and MIMIC IV datasets.

In addition to our system's advancements, we explore integration of Diagnostic Decision Support System (DDSS) leveraging Large Language Models (LLMs) and transformers, aiming to refine healthcare documentation and improve clinical decision-making. We explore the advantages, like enhanced accuracy and contextual understanding, as well as the challenges, including computational demands and biases, of using various LLMs.