Medical speech recognition is extremely intuitive for healthcare practitioners to use, but you might be wondering how exactly it works. Although each type of software may be slightly different depending on what it has been programmed to do, medical speech recognition generally follows these four steps:
- Initially, an analog-to-digital converter translates analog waves that are produced by speech into data that computers are able to understand.
- The data is broken down into smaller sound bites that match phonemes in the given language.
- The software analyzes and compares these phonemes with known words, phrases and sentences.
- After this comparison has been made, the software makes an inference from the speech and translates it into text.
For a healthcare practice using this software, the words, phrases, and sentences within the database are specific to the medical industry. The first few times a practitioner uses the system, they will most likely have to check for errors within the produced note and fix any mistakes. The algorithm is designed to adjust to these mistakes, meaning that as time goes on, the final result will have fewer errors.
As we mentioned previously, all healthcare practitioners are required to maintain clinical documentation and this process can take quite a bit of time. Sessions with patients need to be recorded in progress notes, and any updates or modifications to a treatment process need to be uploaded into the EHR as soon as possible. Whilst most doctors manage to keep up with documentation, it usually takes up so much time that they end up not being able to see and treat as many patients. Using medical speech recognition software will cut down this time significantly, all whilst ensuring that the documentation remains accurate.
There are two main types of speech recognition software that are currently being used in healthcare practices. The first utilizes dictation tools that transcribe what the practitioner verbalizes word-for-word. Dictation software requires the doctor to use the exact language that they want their notes to contain. The second is AI scribes, which apply natural language processing (NLP) to the word-for-word transcription. NLP identifies the relevant medical language and removes filler words and even small talk. AI scribes allow practitioners to speak more freely into their voice recognition software, as the algorithm does a lot of the formatting and editing for them.