How Speech Recognition Technology is Transforming the Healthcare

Voice recognition is a technology that analyzes speech patterns to not only understand the human voice but also link that voice to specific speakers. If you’ve ever set up a new smartphone, smart speaker, or voice assistant, you’ve probably used voice recognition technology. During the setup, you are asked to repeat certain phrases to help the device recognize your voice in the future. This is what makes voice recognition different, more effective, and more advanced compared to speech recognition software. Speech recognition focuses only on the words being said (the “speech” itself), while voice recognition identifies both what is being said and who is saying it. This ability to distinguish the speaker is why not all iPhones in a room respond when one person says “Hey, Alexa.” However, voice recognition technology is now expanding beyond just home devices and moving into more specialized fields like healthcare. 

Understanding How Voice Recognition Technology Functions

To understand voice recognition technology, we first need to look at speech recognition. Speech recognition takes raw, analog sound and changes it into a digital format. This audio file is then broken down into smaller, manageable pieces. These segments are compared to existing data sets, and the program tries to make the most accurate interpretation based on the information it has.

Voice recognition uses the same process but adds something called a "voiceprint" or "voice fingerprint" — essentially a separate internal database that allows the program to match the voice and identify the speaker. The different variations of “Hey, Assistant” you say while setting up a new device are an example of creating a voiceprint.

Mainstream Applications of Voice Recognition

Voice recognition technology is commonly used in virtual assistants that help with various tasks. For instance, “Hey, Siri, update an alarm for 7:45 p.m.” is a typical example of how we use voice recognition in our daily lives. Another example could be, “Ok, Alexa, set a reminder for mother’s birthday tomorrow at 10:00 p.m.”

The Role of Voice Recognition Technology in Healthcare

Voice recognition technology is becoming increasingly important in healthcare, especially when it comes to handling administrative tasks like medical documentation. Voice assistants can simplify tasks such as searching for patient data, making recordings, entering details into the electronic health record (EHR), scheduling appointments and follow-ups, placing test orders, and more. By using voice recognition technology, many time-consuming administrative duties faced by healthcare providers can be completed faster.

Currently, several products are available that offer these capabilities. Nabla is one example of how voice recognition and dictation can be used to create notes and place orders. These advanced dictation tools can use voice recognition to enter information into specific EHR fields based on voice commands and existing data. In this way, voice recognition technology in healthcare acts as a link between medical professionals and computers, helping them interact efficiently with their programs and EHRs.

Looking ahead, the future of voice recognition technology in healthcare lies in fully automating these documentation tasks. The goal is not just to enhance or speed up the process with voice commands, but to eventually have these tasks completed automatically, without the need for manual input.

Voice Recognition, Artificial Intelligence, and NLP in Healthcare

Voice recognition technology involves the process of identifying and organizing structured audio data, while natural language processing (NLP) is a branch of AI that focuses on labeling and organizing unstructured data, often referred to as “natural language.” Unlike voice recognition, which only identifies words or speech, NLP can interpret grammar, meaning, and context, allowing for deeper insights. To better understand its uses in healthcare, it's helpful to have a basic idea of how it functions.

The general process of traditional NLP is as follows: first, it breaks down a natural conversation recording into smaller speech segments known as tokens. These tokens are then categorized into different parts of speech, such as nouns, verbs, adjectives, and so on. Afterward, these words are stripped of any conjugations and reduced to their base form or "stem." The next step is to remove filler words, or "stop words," such as “a,” “to,” or “like,” which don't add much value to the analysis. The result is a detailed, yet manageable set of language data that can be processed and organized by an algorithm. Using this method, NLP can take long spoken conversations and pull out relevant information. With the help of other AI techniques like machine learning, these systems become more efficient and accurate over time.

In healthcare, this advanced technology proves to be highly beneficial for providers who interact directly with patients. By utilizing voice recognition and NLP, medical information can be extracted from natural conversations. This allows healthcare providers to use the technology in the background while they conduct their regular exams. A common use of this technology in healthcare is the AI Medical Scribe — a tool that silently works in the background, captures crucial information being discussed, and organizes it into comprehensive medical notes.

AI Medical Scribes Powered by Voice Recognition Technology and NLP

At the moment, SOAPsuds is among the best applications in healthcare tech that utilize voice recognition and NLP models. It has the capability to listen to a patient interaction and pull out the essential medical details. What sets SOAPsuds apart even more is its ability to organize the encounter into the separate sections of a standard medical note and then automatically upload that note into the provider’s system — similar to how a human scribe would do. SOAPsuds operates as an ambient technology — meaning it doesn’t need prompts, dictation, or wake-words — and it automates the process of medical documentation for healthcare professionals.