Saykara Launches First Fully Ambient AI Healthcare Voice Assistant

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Saykara today announced the release of Kara 2.0, an AI-powered healthcare assistant that further simplifies the documentation process for physicians. Now featuring Ambient Mode, Kara 2.0 is a breakthrough AI-powered voice application for healthcare, allowing physicians and patients to interact as they normally do, all while Saykara listens, transcribes to text, parses text into structured data, and intelligently completes each form in a patient's electronic health record (EHR or chart). Saykara then automatically generates a clinic note including patient history, physical, assessment, plan, orders and referrals. With the release of Ambient Mode, Saykara is the only virtual healthcare assistant that can be used passively'in the room' during physician-patient appointments with no voice commands. Ambient Mode builds on Saykara's versatility and agnosticity, allowing it to better serve up to 18 disparate healthcare specialties, including primary care, pediatrics, internal medicine, orthopedics, urology and more.


Amazon Launches EHR-Integrated, Machine Learning-Powered Transcription Service

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As part of re:Invent, today AWS announced Amazon Transcribe Medical, a new HIPAA-eligible, machine learning automatic speech recognition (ASR) service that allows developers to add medical speech-to-text capabilities to their applications. Transcribe Medical provides accurate and affordable medical transcription, enabling healthcare providers, IT vendors, insurers, and pharmaceutical companies to build services that help physicians, nurses, researchers, and claims agents improve the efficiency of medical note-taking. Today, clinicians can spend up to an average of six additional hours per day, on top of existing medical tasks, just writing notes for electronic health record (EHR) data entry. Not only is the processing time consuming and exhausting for physicians, but it is also a leading factor of workplace burnout and stress that distracts physicians from engaging patients attentively, resulting in poorer patient care and rushed visits. While medical scribes have been employed to assist with manual note-taking, the solution is expensive, difficult to scale across thousands of medical facilities, and some patients find the presence of a scribe uncomfortable, leading to less candid discussions about symptoms.


Amazon's machine learning transcription service aims to ease docs' tasks

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Amazon Web Services is rolling out an electronic health record-supported machine learning transcription service that uses speech recognition applications to ease physician documentation. The product is Amazon Transcribe Medical, which automatically translates audio streams into medical speech, enabling affordable, secure and accurate note taking for clinical staff, researchers and other stakeholders. Cerner, for example is using the product in an initial development of a digital voice scribe that automatically listens to clinician and patient interactions and captures the conversation in text form. The service enables developers to add medical speech-to-text capability to their applications. Amazon is positioning Transcribe Medical as a tool to ease physician and researcher burnout.


Crowdsourced Continuous Improvement of Medical Speech Recognition

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We describe a method for continuously improving the accuracy of a large-scale medical automatic speech recognizer (ASR) using a multi-step cycle involving several groups of workers. The paper will address the unique challenges of the medical domain, and discuss how automatically created and crowdsourced input data is combined to refine the ASR language models. The improvement cycle helped to decrease the original system's word error rate from 34.1% to 10.4%, which approaches the accuracy of human transcribers trained in medical transcription.


Amazon Transcribe Medical – Real-Time Automatic Speech Recognition for Healthcare Customers Amazon Web Services

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In 2017, we launched Amazon Transcribe, an automatic speech recognition service that makes it easy for developers to add speech-to-text capability to their applications: today, we're extremely happy to extend it to medical speech with Amazon Transcribe Medical. When I was a child, my parents – both medical doctors – often spent evenings recording letters and exam reports with a microcassette recorder, so that their secretary could later type them and archive them. That was a long time ago, but according to a 2017 study by the University of Wisconsin and the American Medical Association, primary care physicians in the US spend a staggering 6 hours per day entering their medical reports in electronic health record (EHR) systems, now a standard requirement at healthcare providers. I don't think that anyone would argue that doctors should go back to paper reports: working with digital data is so much more efficient. Still, could they be spared these long hours of administrative work?