AWS Voice-Enabled Transcription Tool to Address Clinician Burnout

By iCare

Amazon Transcribe Medical can limit physician burnout and improve care with the automatic transcription service.

 – AWS has announced the deployment of Amazon Transcribe Medical, a speech recognition service that transcribes clinician and patient speech into text.

The tool helps physicians document more efficiently, integrating medical transcriptions into EHRs or applications. The service also includes transcribing conversations with patients into text and uses proper capitalization and punctuation during conversation.

“Extreme accuracy in clinical documentation is critical to workflows and overall caregiver satisfaction,” said Jacob Geers, solutions strategist at Cerner Corporation, an EHR vendor in using an early version of the tool.

“By leveraging Amazon Transcribe Medical’s transcription API, Cerner is in initial development of a digital voice scribe that automatically listens to clinician-patient interactions and unobtrusively captures the dialogue in text form,” continued Geers. “From there, our solution will intelligently translate the concepts for entry into the codified component in the Cerner EHR system.”

Voice solutions that are implemented into the tool will produce accurate medical transcripts of dictation and conversational speech in the medical domain, AWS said. It is typical for physicians in the workplace to take extensive notes. With Amazon Transcribe Medical, the clinicians do not have to worry about taking notes that are distracting to the patient or the care of the patient, AWS suggested.

Data entry into the EHR is also simplified using this tool. This product will ease the burden that physicians face when it comes to EHR use. It will save the clinician time and mouse clicks, thus limiting physician burnout due to cognitive overload.

Right away, the product is HIPAA eligible and it offers an API that can integrate with voice-enabled applications and any device with a microphone, described the company.

Once the transcribing is completed, it is time-stamped, and the transcript is given a confidence score for accuracy. Physicians can use the API to open a secure connection over WebSocket protocol and start passing a stream of audio to the service. Then, the user receives the text in real-time.

The transcribed text can also be quickly implemented into a text analytics service.

In late 2018, Amazon developed Amazon Comprehend Medical, a machine learning service that enables developers to analyze patient EHRs to identify information including symptoms, patient diagnoses, dosages, treatments, and signs.

The tool allows users to process unstructured medical text and is designed to improve clinical decision support for healthcare providers. It can also aid providers, insurers, researchers, clinical trial investigators, and biotech and pharmaceutical companies to streamline revenue cycle management and clinical trials management.

Developers can use the tool to identify common types of medical data including medical conditions, anatomic terms, medications, details of medical tests, treatments, and procedures.

“Amazon Comprehend Medical provides the functionality to help us with quickly extracting and structuring information from medical documents, so that we can build a comprehensive, longitudinal view of patients, and enable both decision support and population analytics,” said Anish Kejariwal, Roche Diagnostics information solutions director of software engineering.

Amazon aimed to create the tool to allow patients to manage their own health, schedule visits, and make informed medical decisions.

As noted above, it can work side-by-side to also analyze unstructured text in clinical trial reports and doctors’ notes.

“Amazon Comprehend Medical will reduce this time burden from hours per record to seconds,” said Matthew Trunnell, CIO at Fred Hutchinson Cancer Research Center, at the time. “This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”


This article originated from and was written by Christopher Jason