Enhancing Healthcare Communication: A Study on Automated Speech-to-Text Conversion and Analysis of Doctor-Patient Dialogues for Improved Clinical Documentation and Patient Care

While breakthroughs in speech technology–above all those based on DL technology–open unprecedented options for the transformation of this industry, its potentiality hasn't been studied adequately to date. The tremendous potential that voice technology offers to transform the healthcare industry has been the main focus of this research. More specifically, we examine current advancements in speech signals for health monitoring and detection, voice synthesis, automated speech recognition (ASR), and text-to-speech (TTS). We also provide a broad summary of some obstacles to the growth of healthcare services based on speech. We outline outstanding issues and suggest some possible research directions to maximize the advantages of other technologies and boost the effectiveness of speech-based healthcare solutions. As a result, proper communication between physicians and patients is essential for accurate diagnosis, effective treatment, and patient satisfaction. This creates errors, inefficiency, and stress among the medical providers from the written clinical records. Through this project, therefore, a case study is developed to investigate whether automated speech-to-text (STT) systems will be adopted in altering doctor-patient conversations to structured, analyzable clinical records in health communication. The proposed solution reduces human burdens of note-taking by the employment of NLP techniques applied to real-time recording talks for extracting important medical information that creates reliable medical records, besides examining the system's ability to identify communication patterns that contribute to enhancing patient participation in the treatment process, further offering more personalized care.