Authors : Saritha Kondapally

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/29x9wvcw

Scribd : https://tinyurl.com/5xheuzva

DOI : https://doi.org/10.5281/zenodo.14854514

Abstract : Aim to emphasize the transformation and efficiency improvements AI Transcription can bring to EHR systems while making the article engaging for readers. In the evolving landscape of healthcare, the integration of Artificial Intelligence (AI) into Electronic Health Records (EHR) systems has the potential to significantly transform the way healthcare providers manage patient documentation. "Automating Healthcare Documentation: The Role of AI Transcription in EHR Evolution" explores how AI-driven scribing tools can streamline the documentation process, reduce clinician burnout, and improve the accuracy and efficiency of EHR systems. This white paper examines the challenges faced by healthcare professionals in manual EHR data entry, including time constraints, errors, and the impact on patient care. It introduces AI Transcription as a solution that leverages natural language processing (NLP) to transcribe and organize clinical notes in real-time, allowing healthcare providers to focus more on patient interaction. By embedding AI Transcription directly into EHR platforms, organizations can enhance workflow efficiency, reduce administrative overhead, and ensure more accurate documentation. The paper further delves into the benefits of AI scribing, such as improved documentation accuracy, real-time data entry, and better clinician-patient interactions. It also highlights the potential hurdles to successful AI integration, such as system compatibility, training, and data security. Through case studies and evidence of impact, this paper demonstrates how AI-powered scribing is revolutionizing healthcare documentation, offering a path toward more efficient, patient-centered care.

Keywords : Artificial Intelligence (AI), AI Scribing, Electronic Health Records (EHR), Healthcare Documentation, Clinical Workflow Automation, Natural Language Processing (NLP), Speech Recognition, Clinician Burnout. Electronic Health Records (EHR).

References :

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