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Within the Electronic Health Record (EHR) Systems, physicians spend extensive time on patient documentation, leading to an alarming increase in mental burnout. The disproportionate focus on data entry, eclipsing time spent on direct patient care, highlights a critical concern. To address this pervasive burnout, a unified effort is imperative. The urgency of the matter is accentuated by the integration of Natural Language Processing (NLP) powered EHR systems, poised to alleviate the substantial time and effort required for health record maintenance. Our research unveils a cutting-edge solution-the Automated Electronic Health Record System, a transformative innovation that not only transcribes dialogues but also employs advanced clinical text classification. This System empowers physicians by pre-filling EHR sections, enhancing accuracy and facilitating comprehensive review before integration. With an outstanding accuracy exceeding 98.97%, our System represents a break-through, saving over 90% of time compared to manual data entry. Rigorous testing on MIMIC III and MIMIC IV datasets underscores the reliability and effectiveness of our classification model, marking a pivotal stride in the evolution of healthcare documentation.
Khan et al. (Fri,) studied this question.