How Speech to Text Transformed Healthcare and Medical Transcription

Imagine a doctor typing away at her computer while her patient speaks. She feels trapped between recording vital information and engaging with the person in front of her with each typed word. This picture, repeated in many exam rooms, represents a major issue in modern healthcare: balancing documentation and patient interaction.

The pressure to maintain comprehensive digital records has created barriers between healthcare providers and patients. Although effective healthcare management and continuity of treatment depend on thorough documentation, it sometimes comes at the expense of meaningful patient connection. 

However, there is a chance to bridge this gap. Speech-to-text technology presents a viable way to revive personalized healthcare while maintaining the accuracy needed in digital record-keeping.

In this article, you will learn:

• How speech-to-text is revolutionizing medical documentation
• The numerous benefits of implementing speech-to-text solutions in clinical settings
• Real-world applications and use cases of speech-to-text in various healthcare scenarios
• The unique advantages offered by Deepgram's advanced AI-driven speech recognition technology.

Healthcare needs accurate and timely documentation, but traditional methods present numerous challenges that affect patient care, provider productivity, and overall healthcare outcomes. 

The burden of extensive documentation requirements often comes at the expense of quality patient interaction. Healthcare providers frequently divide their attention between the patient and their documentation duties, potentially compromising the patient-provider relationship and the quality of care delivered. Challenges here include the following:

• Providers may rush through patient encounters to allocate time for documentation
• Reduced eye contact and engagement during visits due to note-taking
• Patients may feel neglected or undervalued when providers focus on paperwork

One of the most pressing issues with traditional medical documentation is the sheer amount of time it consumes. Healthcare providers often spend hours each day manually entering patient data, writing clinical notes, and updating medical records, taking valuable attention away from direct patient care. Challenges here include the following:

• Clinicians spend up to 50% of their workday on documentation tasks
• Manual data entry is slow and prone to delays
• Paperwork often extends beyond regular work hours, affecting work-life balance

Manual documentation is inherently susceptible to errors, which can seriously affect patient safety and treatment efficacy due to time constraints, fatigue, or simple human oversight. Challenges here include the following: 

• Transcription errors can lead to misdiagnosis or improper treatment
• Illegible handwriting may result in misinterpretation of orders or prescriptions
• Incomplete or inconsistent documentation can compromise continuity of care

Medical transcription is the process of converting voice-recorded reports into written text. Traditional transcription processes introduce additional layers of complexity and the potential for delay. 

The typical workflow entails dictation by the healthcare provider, then transcription by a medical transcriptionist, and finally review and sign-off by the provider. Challenges here include the following: 

• Turnaround time for transcribed documents can range from hours to days
• Backlogs in transcription can delay critical decision-making and care coordination
• The cost of employing or outsourcing transcription services can be substantial

STT models convert spoken language into written text. At its core, STT uses artificial intelligence (AI), specifically machine learning (ML) and natural language processing (NLP), to interpret and transcribe human speech.

Modern STT systems rely on complex algorithms and models for high accuracy and performance. Speech recognition has changed a lot, thanks to deep learning. 

Neural networks, particularly deep learning models, are trained on massive amounts of data to recognize patterns in speech and accurately transcribe audio input into text output.

End-to-end models, which combine acoustic and language modeling into a single neural network, are becoming increasingly prevalent due to their superior performance. These models can learn directly from raw audio data and text transcripts, which simplifies the training process and improves overall accuracy.

One of the key advantages of modern STT systems is their ability to be trained on domain-specific data. 

Since models can be fine-tuned to recognize medical terminology, jargon, language, and accents, they are more accurate than general-purpose models in healthcare.

As you saw earlier, classical medical transcription is slow, error-prone, and delays critical documentation. Deepgram's Nova 2 Medical Speech-to-Text model addresses the unique challenges of medical transcription and voice-based healthcare applications. 

Deepgram's Nova 2 Medical is built on the industry-leading Nova 2 speech-to-text model—a Transformer-based STT model. The Transformer-based architecture is divided into two components:

• Acoustic Transformer encodes audio into embeddings
• Language Transformer decodes embeddings into text using input prompts

We have trained the model on large medical conversations and documentation datasets to understand complex medical terminology, clinical jargon, and diverse accents.

Here are the key features and capabilities of Deepgram’s Nova 2 Medical STT Model:

Nova 2 Medical has a 16% relative improvement in word recall rates (WRR) for medical terminology compared to the previous model, with an average relative WRR improvement of 20.5% vs. leading competitors.

WRR measures the percentage of words in the ground truth text that were correctly predicted or matched (i.e., true positives). The higher the WRR, the better the transcription is in terms of word accuracy. 

This means the model makes fewer errors and has a more accurate representation of critical patient care details.

The Nova 2 Medical Model gets an 11% improvement in the overall word error rate (WER) for pre-recorded (batch) transcription, which is 42.8% better than the best alternatives in the medical field. 

Typically, the lower the WER, the better the transcription is in terms of word accuracy. This translates to fewer manual corrections and more reliable documentation.

Nova-2's innovative architecture gives it a big speed advantage over other speech recognition solutions. Transcription speeds are 5 to 40 times faster than similar vendors, with a median inference time of just 29.8 seconds per hour of audio. It is one of the few options on the market that can handle real-time application performance.

Deepgram offers various deployment options, including on-premises and securely self-hosting on a VPC (virtual private cloud). This allows healthcare organizations to meet their specific security and compliance requirements.

Nova-2 Medical Model generates results that adhere to stringent medical documentation standards and ensure HIPAA compliance by ensuring patient information remains confidential and secure.

Healthcare organizations can improve the accuracy of uncommon keywords (e.g., new drug names) with rapid custom model training services that boost the Nova-2 medical model’s already impressive, out-of-the-box performance.

This walkthrough shows you how to transcribe an audio file using Deepgram's Nova-2 Medical Model. 

We'll use Python and the Deepgram SDK to make the API call. This example will help you understand how to set up the client, send the audio file for transcription, and handle the response.

Step 1: Setup Environment:

Ensure you have Python installed and install the Deepgram SDK and dotenv for managing environment variables:

pip install deepgram-sdk python-dotenv

Step 2: Create a .env File:

Store your Deepgram API key securely in a .env file:

DEEPGRAM_API_KEY=your_deepgram_api_key

Step 3: Imports and Setup

This section imports the necessary modules and sets up the environment:

• os and dotenv are used for environment variable management.
• datetime is used to time the transcription process.
• Deepgram’s API interaction is facilitated through four main classes: DeepgramClient for API connection and operations, DeepgramClientOptions for client configuration, PrerecordedOptions for specifying transcription parameters, and FileSource for handling audio file input
• AUDIO_FILE is defined as a constant for the input audio file

import os
from dotenv import load_dotenv
import logging
from datetime import datetime, timedelta

from deepgram import (
    DeepgramClient,
    DeepgramClientOptions,
    PrerecordedOptions,
    FileSource,
)

load_dotenv()

AUDIO_FILE = "VOXTAB_Medical_audio.mp3"

Step 4: Main function

Define the main() function with a try-expect block to catch and print any exceptions during execution:

def main():
    try:
    # Function body (explained below)
    except Exception as e:
        print(f"Exception: {e}")

Step 5: Initialize Deepgram Client

Call a new Deepgram client instance: (The API key is loaded from the environment variables.)

deepgram: DeepgramClient = DeepgramClient(os.getenv(“DEEPGRAM_API_KEY”))

Step 6: Load Audio File

Open the audio file in binary read mode, and read the content into buffer_data:

with open(AUDIO_FILE, "rb") as file:
    buffer_data = file.read()

Step 7: Prepare Payload

Create a payload dictionary with the audio data. This will be sent to Deepgram for transcription:

payload: FileSource = {
    "buffer": buffer_data,
}

Step 8: Set Transcription Options

Configure a `PrerecordedOptions` object for Deepgram's transcription service, specifying the "nova-2" model with enhanced features like smart formatting, utterance detection, punctuation, and summarization (version 2):

PrerecordedOptions = PrerecordedOptions(
    model="nova-2",
    smart_format=True,
    utterances=True,
    punctuate=True,
    summarize="v2",
)

Step 9: Transcribe and Time the Process

Initiate the transcription process and time its execution. Then, call the transcribe_file method with the prepared payload and options and capture the end time:

before = datetime.now() response = deepgram.listen.prerecorded.v("1").transcribe_file(payload, options) after = datetime.now()

print(response.to_json(indent=4))
print("")
difference = after - before
print(f"time: {difference.seconds}")

This outputs the transcription results as formatted JSON, along with the duration of the transcription process in seconds:

For a hands-on demonstration, you can use Deepgram's API Playground to test the Nova-2 Medical model without writing any code. 

Visit the Deepgram API Playground and follow these steps:

Step 1: Select the Model

• Choose `nova-2-medical` from the model options.

Step 2: Upload an Audio File

• Upload your audio file or provide a URL to an audio file.

Step 3: Configure Options

• Set options like smart formatting, punctuation, and summarization as needed.

Step 4: Run the Transcription

• Click the "Run" button to see the transcription results in real-time.

Following these steps, you can quickly start with Deepgram's Nova-2 Medical model and experience its powerful speech-to-text capabilities tailored to healthcare applications.

The integration of speech-to-text technology in healthcare has opened up many applications that improve clinical workflows and patient care. 

From streamlining documentation to enhancing decision-making processes, speech-to-text is finding its place in various aspects of healthcare delivery. Let's explore some of the critical applications of this technology in clinical settings.

Electronic Health Records (EHR) Integration

Speech-to-text models allow clinicians to dictate patient notes directly into the EHR in real-time, which eliminates manual data entry and reduces the risk of errors. 

For instance, Deepgram's Nova 2 Medical Model powers TORTUS and Phonely AI, which integrate with existing EHR systems to keep accurate records of conversations with patients and streamline the documentation process.

Dictation of Patient Notes, Reports, and Summaries

Healthcare providers can efficiently create detailed clinical documentation by dictating progress notes, discharge summaries, operative reports, and other medical terminology. 

Deepgram's specialized medical vocabulary and customizable models ensure accurate transcription of complex medical terminology. 

Subtitle Generation

Real-time speech-to-text can create subtitles for video consultations, making them more accessible to patients with hearing impairments. Deepgram's low-latency transcription ensures accurate and synchronized subtitles. 

For instance, BIGVU uses Deepgram’s API to provide transcription service for its customers, who rely on automated captions to increase engagement with their viewers. By implementing Deepgram’s solution, BIGVU reduced its prior transcription latency by 58%.

Virtual Visit Documentation

Speech-to-text automatically transcribes telemedicine consultations, generating a comprehensive interaction record. This can improve documentation accuracy, save providers time, and improve virtual care quality.

Hands-Free Documentation in Procedures

In surgical or procedural settings, where sterility and hands-free operation are paramount, speech-to-text allows for real-time dictation of operative notes, procedure logs, and equipment requests. The model’s accurate transcription can capture critical details without disrupting the workflow.

This is also the case with medical consultations. Instead of ferociously noting every detail from the patient during consultation, doctors can focus on the consultation and leave notetaking to STT. 

For instance, Deepgram's Nova 2 Medical Model also powers Lyrebird Health's medical documentation platform, helping practitioners like Dr. Carr interact better with patients.

Voice-Enabled Clinical Decision Support

Clinicians can verbally query clinical databases or guidelines, accessing critical information hands-free. Deepgram's API integrates with decision-support tools to provide verbal or visual responses to queries and enhance clinical decision-making.

Voice-Activated Alerts

Speech-to-text can power alert systems that notify clinicians of potential drug interactions, allergies, or critical lab values based on dictated information.

Voice-Activated Patient Portals

Speech-to-text technology can enable voice-activated navigation and data entry in patient portals, improving accessibility for individuals with limited dexterity or visual impairments.

Multilingual Communication

Deepgram's STT API also supports real-time speech-to-text translation, which makes it easier to talk to people who do not speak English as their first language and gives more people access to care.

Other ways STT transforms healthcare and medical transcription are in medical research and clinical trials. Researchers can dictate real-time observations and notes during field studies or clinical trials to capture valuable data accurately and efficiently.

Deepgram's STT API can automatically transcribe research interviews and focus groups, saving valuable time in qualitative data analysis.

After looking at these different applications, it's become clear that speech-to-text technology isn't just changing some parts of healthcare; it's truly changing how patients are cared for, how efficiently healthcare is run, and how medical progress is made.

Voice-to-text technology is changing the way healthcare documentation is done by fixing problems that have been around for a long time with the old ways, like the fact that it takes a long time to enter data by hand, there are a lot of mistakes, and doctors have less time to spend with patients in person. 

Deepgram's Nova-2 Medical Model helps healthcare professionals in many ways, such as by letting them spend more time with patients, making medical records more accurate, reducing provider fatigue, improving patient safety, and working more efficiently overall. 

Nova-2 Medical Model meets the demanding needs of healthcare environments with its 30% reduction in word error rate, 5-40x faster-pre-recorded inference time, and competitive pricing starting at $0.0043 per minute. 

The model's architecture is robust, safe, and scalable, so it can be used for many important healthcare tasks, like EHR documentation, patient session transcription, and patient intake.

Speech-to-text systems can protect patient privacy by reducing the need for outside transcriptionists and implementing strong encryption and access control. However, it is very important to pick HIPAA-compliant solutions and follow best practices for keeping data safe.

Tracking metrics like less time spent on paperwork, lower transcription costs, faster medical record turnaround times, and happier patients because providers are more involved can help you determine the return on investment. Long-term benefits may also include reduced burnout rates among healthcare providers.

Nova-2 uses special medical dictionaries and machine-learning algorithms to correctly transcribe medical terms that are difficult to understand. It was trained on large datasets with many different accents and dialects, which lets it recognize and accurately transcribe a wide range of speech patterns.

Nova-2 offers competitive pricing starting at $0.0043 per minute for pre-recorded audio, making it 3-5 times more affordable than other comprehensive providers. This cost-effectiveness and its advanced features provide significant savings for businesses and organizations.

We offer custom model training for Nova-2 so organizations can tailor speech recognition features to their specific use cases and industries. This customization ensures optimal performance and accuracy for diverse applications.