Speech Recognition Software in Healthcare

The medical speech recognition software market size is estimated to grow at a CAGR of more than 11% over the next five years, according to various software market trend research reports, providing an opportunity for medical technology startups, especially cloud-based ones focused on North America. The potential of advanced AI, machine learning, and NLP to decrease the manual effort required for healthcare documentation, reduce associated costs and eliminate barriers to making more profit for health organizations are key factors driving market growth. Today, it is hard to distinguish medical transcription software from healthcare speech recognition software because they have essentially become the same, especially for tasks where real-time AI-based speech recognition and transcription are employed, with direct inputs into EHRs and other systems, as well as automatic coding. However, in general, transcription is often considered a subset of speech recognition.

What is Healthcare Voice Recognition Software?

This type of medical software converts voice inputs into traditional written documents.

It automates tiresome and mistake-prone operational tasks, providing clinicians with a tool to create and submit medical records in seconds.

To reduce the clinical documentation burden, different solutions are used in clinical practice:

• Medical scribes (traditional, in-person staff)
• Virtual scribes (remote workers, often located offshore)
• Medical speech recognition software (physicians must actively dictate notes)
• Ambient speech recognition software(which passively listens to doctor-patient interactions and extracts relevant medical details, focusing solely on automatic note generation)
• Artificial intelligence (AI) assistants (voice-controlled EHR helpers that doctors speak to directly, allowing the AI to perform commands such as documentation, EHR navigation, order entry, communication, and workflow automation).

Medical Documentation Challenges

Challenges without Modern Voice Recognition Software

A huge administrative burden forces physicians to spend additional hours on documentation. The problem leads to increased rates of errors, higher possibility of psychological issues among staff, and worsened productivity. 

Doctors perform multiple activities during their interactions with patients. They take symptoms, assess health factors, review previous reports and health history, as well as schedule additional tests and appointments with other specialists. Clinicians need a tool that would be able to perform several tasks: transcribe voice in real-time, comprehend subtleties of the medical context to take notes correctly, and understand doctors’ commands.

Challenges with Legacy Voice Recognition Software

The healthtech market offers various solutions. However, their functionality differs, limiting some of the clients’ expectations. Here are the challenges that prevent efficient clinical workflow while utilizing voice recognition software.

• Finding the balance between the speed and accuracy of the tool might be difficult. Some voice-based digital assistants offer excellent accuracy while being slow in functioning. Others perform faster, but they require manual editing. Both options are frustrating for doctors and may add to aggregating stress
• Manual editing demands time, forcing users to examine, edit, and submit the notes to the electronic health record (EHR), therefore preventing instant availability of the data
• Difficulties with managing the system might occur, as doctors’ voices vary in tone, pitch, and accent. Those variations affect the accuracy of transcription
• Hampered transition between commands and dictations makes the functionality of an integrated voice assistant inconvenient
• When manual editing is required, privacy issues might occur. Those staff members who check the accuracy of the records need access to sensitive patient data, creating additional risk. 

AI-assisted Voice Recognition Software Functionality

Modern voice recognition tools are meant to become “invisible” doctors’ digital assistants. Such assistants provide clinicians with necessary patient data, accurately make notes, and answer special queries. The software usually performs the following features:

Workflow integration and usability features

• Fit into the workflows of each individual clinician and understand different medical specialties, from behavioral health to surgery
• Combine voice dictations and commands in one tool with a seamless transition from the prior to the latter to enable clinicians to deal with documentation, coding, and looking through the data in the EHR 
• Analyze user’s intentions, navigation events, or cursor movements, allowing doctors to naturally dictate the data and then request details to fill in documentation sections if necessary 
• Comprehend the nuances of the country-specific healthcare environment 
• Provide additional assistance with answering inbox messages, compiling referral letters, and other administrative tasks.

Data integration and accuracy features

• Integrate a large language model (LLM) to learn patient data, health history, and other relevant contexts to help doctors create notes
• Integrate the data with key systems, such as EHR, in two directions, i.e., using the EHR data to generate the note and leveraging the actual note to complete the fields in the EHR.
• Combine the information about medical context, i.e., patient details, doctor’s specialty, type of appointment, etc., with medically customized automatic speech recognition to select relevant terms and generate highly accurate notes.
• Keep a record of the notes, track and understand the text dictated at various sections, including situations involving issues like slow wifi or poor-quality microphones 
• Compile patient summaries using automated LLM-based summarization workflows to enable practitioners to prepare for patient visits and analyze data from previous emergency department visits, inpatient visits, and appointments with specialists  

Benefits of modern AI-assisted Voice Recognition Software

Healthcare systems that have already utilized AI and medical speech recognition products report the following improvements:

• 79% of users reported better documentation quality, 70% saw reduced burnout and fatigue, 81% of patients saw greater physician focus, 72% reduction in documentation time, 40% decrease in after-hours work, including weekend work, 20% increase in practice satisfaction, according to the research
• Single solution instead of applying several ones
• Supporting a broad number of clinicians of various specialties
• Allowing doctors to have a natural interaction with their colleagues and patients in the decision-making process
• Easy switching between dictation, request, command, or query options and relevant accuracy while performing those actions
• Elimination of the necessity to type and click while compiling documentation, resulting in high doctors’ speed

How Belitsoft Can Help

Healthcare software development companies like Belitsoft help healthtech startups create, customize and support speech recognition software solutions with the following capabilities:

• Real-time voice transcription for doctors to dictate notes
• Seamless switching between taking notes and processing voice commands
• Automatic synchronization of doctors’ notes with internal systems (EHRs, etc.)
• Customized note sections for doctors to easily navigate through medication history, health maintenance, etc.
• Embedded personalized templates for doctors
• Integrated ICD-10 diagnosis codes to enable automatic coding and billing
• Compliance with health systems’ requirements
• Multi-language interface
• Augmentation with a Google Chrome extension which helps to create notes conveniently
• and more.

If you're looking for expert assistance in data infrastructure, data platforms, workflow engineering, and cloud development (including AWS, Azure, and Google Cloud), as well as hybrid or on-premises environments, Belitsoft healthcare software development company offers outsourced expertise to meet your needs.