Medical Voice Recognition Software: Types, Benefits - EffectiveSoft
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Medical Voice Recognition Software

Today, doctors face the problem of spending their time manually creating and maintaining medical records rather than providing medical care to patients. Speech recognition (SR) systems greatly facilitate and speed up the maintenance of information as they allow creating and editing medical records using voice input only.
4 min read
voice recognition technology in healthcare

    Why use medical voice recognition systems? The answer to this question is simple. Most people speak faster than they can type. An experienced operator can type a 100-word message in about 2 minutes. A speech recognition system is able to transcribe 150 words per minute and has already achieved 99% accuracy, which is critical for healthcare providers since this saved time may be spent in many useful ways. For instance, saving time on paperwork allows medical staff to focus on carrying out their direct responsibilities. In addition, speech recognition software is being constantly improved, which results in spending less time per patient admitted. With speech recognition systems hospitals trim their costs because doctors can enter data directly into an EHR system without having a nurse or an assistant to carry out this task.

    In this article, we will look into the use of medical voice recognition software, as well as its types, and top benefits.

    How speech recognition is applied in healthcare

    A lot of healthcare institutions have a position of a transcriptionist or outsource transcription services in order to make records of everything a doctor says to patients. Nevertheless, outsourcing or hiring a transcriptionist and providing enough specialists to cover the needs of a medical facility is a real challenge.

    With applications for voice recognition, doctors do not need to transcribe audio dictation, and medical facilities do not have to hire a lot of medical transcriptionists to accompany every doctor. The text recognized by a SR system goes directly to the electronic health records. There is no need to worry about difficult medical terminology — medical SR systems are trained to recognize the majority of terms.

    Speech recognition types

    Types of voice recognition software in healthcare

    Types of voice recognition software in healthcare

    Back-end. These systems convert speech into text only after the speaker has dictated it. The system records the file, processes it and then converts the voice into a text document. Afterwards, the document is ready for editing or direct use.

    Front-end. Unlike back-end SR systems, front-end ones are capable of recognizing and converting voice to text in real time. The system can make some mistakes in recognition, so a medical professional has to edit the text, in other words, ‘teach’ the system to work with their speech patterns.

    Speaker-dependent. Such software learns the unique characteristics of a person’s voice. For correct operation, the system should be trained by any new user via talking to it. This often means that new users should read several pages of text so that a speech recognition system could analyze the peculiarities of the voice and intonation.

    Speaker-independent. Such systems recognize any user’s voice, so no training is required. The main drawback of speaker-independent software is lower accuracy as compared to speaker-dependent solutions. To deal with the issue, the system uses limited grammar and small vocabulary.

    Control interface. SR systems with the control interface functionality make it possible to interact with software via various voice commands. In healthcare, such systems, for instance, allow entering data into various fields of an EMR solution, aid in performing order and inventory management, and help to carry out other tasks.

    Benefits of voice recognition software in healthcare

    Speech recognition advantages

    Speech recognition advantages

    Time savings and financial benefits. With SR software, the need for transcription is completely eliminated and saves up to $ 50,000 annually per physician. By implementing EHR with trained voice recognition, healthcare providers typically achieve a 25% increase in patient throughput and subsequent paid income.

    Flexibility. Most SR systems used in healthcare allow the user to add new words to the dictionary and thus adapt the system to work in a particular medical department.

    Improved quality of care. With the help of the speech recognition technology in healthcare, the doctor can be truly present with the patient without having to interrupt the conversation flow to make some notes. As a result, the doctor is more connected and provides more qualitative care.

    Conclusion

    It has been traditionally believed that in hospitals speech recognition systems can be used only by doctors who dictate reports to a computer. Apparently, modern SR systems can provide significant assistance to any employee in a healthcare institution. Such solutions reduce the time spent on compiling and transcribing medical records, speed up the flow of information, as well as help healthcare staff handle additional workload.

    As an advanced medical application development services provider, EffectiveSoft is ready to reveal the potential of voice recognition in healthcare. Contact us to get a quote.

    FAQs

    • Medical voice recognition software is a technology that converts spoken language into text in healthcare settings. It allows healthcare professionals to dictate patient information, medical notes, and other documentation verbally, making documentation faster and more accurate.

    • Medical transcription involves human transcribers listening to recorded dictations and converting them into text. Voice recognition, on the other hand, uses software to automatically convert spoken language into text without human intervention. While transcription can be more accurate but time-consuming, voice recognition is faster but may require post-processing for accuracy.

    • Voice recognition technologies use Automatic Speech Recognition (ASR) systems. These systems utilize complex algorithms, neural networks, and deep learning techniques, including Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs).

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