This project is funded by CATalyst Gap fund, Fall 2019. The audio recordings are also available in Oxiago Int. Alternative repository for audio recordings This is a pickle file (protocol version 4) containing all the transcribed transcripts and the casenotes for easy and quick access to the data using python. Transcripts generated from the audio recordings using Google Cloud Speech-To-Text API.Ĭasenotes written by the students, i.e. Source transcripts that are used to generate the audio recordings.Īudio recordings of the enacted doctor-patient conversations. Authorsĭescription of the dataset Case note categories: Index The students had past experience of writing case notes and we let the students write case notes as they practiced without any training or instructions from us. The students worked independently using a software that we developed earlier for this purpose. We provided the generated transcripts back to the students and asked them to write case notes. These newly generated transcripts are auto-generated entirely using AI powered automatic speech recognition whereas the source transcripts are either hand-written or fine-tuned by human transcribers (transcripts from Alexander Street). We used Google Cloud Speech-To-Text API to transcribe the enacted recordings. Our study requires recording the doctor and the patient(s) in seperate channels which is the primary reason behind generating our own audio recordings of the conversations. Six of the transcripts that we used to produce this recordings were hand-written by Cheryl Bristow and rest of the transcripts were adapted from Alexander Street which were generated from real doctor-patient conversations. We employed eight students who worked in pairs to generate these recordings. Since, we didn't have access to real doctor-patient conversations, we used transcripts from two different sources to generate audio recordings of enacted conversations between a doctor and a patient. Amazon Transcribe Medical provides transcription expertise for primary care and specialty care areas such as cardiology, neurology, obstetrics-gynecology, pediatrics, oncology, radiology and urology.We generated this dataset to train a machine learning model for automatically generating psychiatric case notes from doctor-patient conversations. The service is HIPAA-eligible and prioritizes patient data privacy and security. Amazon Transcribe Medical can serve a diverse range of use cases such as transcribing physician-patient conversations for clinical documentation, capturing phone calls in pharmacovigilance, or subtitling telehealth consultations.Īmazon Transcribe Medical is available as a set of public APIs that can address both batch workloads and real-time speech-to-text applications. Some organizations use existing medical transcription software, but find them inefficient and low in quality.ĭriven by state-of-the-art machine learning, Amazon Transcribe Medical accurately transcribes medical terminologies such as medicine names, procedures, and even conditions or diseases. However, accurate medical transcriptions such as dictation recorders and scribes are expensive, time consuming, and disruptive to the patient experience. It’s critically important that this information is accurate. Conversations between health care providers and patients provide the foundation of a patient’s diagnosis and treatment plan and clinical documentation workflow. Amazon Transcribe Medical is an automatic speech recognition (ASR) service that makes it easy for you to add medical speech-to-text capabilities to your voice-enabled applications.
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