Speech Recognition for Healthcare Software in 2024

The Essence of Voice Recognition in Healthcare

Speech recognition in healthcare is used to convert spoken appointment summaries and health information into consistent health records or to execute voice commands. Speech recognition technology increases medical staff’s productivity by nearly 10%, facilitates better medical data consistency, and improves patient engagement.

Healthcare Speech Recognition Market

In 2024, the global voice recognition market is expected to reach $8.53 billion. By 2030, it will reach $19.57 billion, growing at a CAGR of 14.8%. The healthcare segment holds the largest share of the market and is expected to contribute the most to the growth. Among the medical fields actively adopting voice recognition are radiology, pathology, and emergency medicine.

How Speech Recognition in Healthcare Works

Use cases

Health records management

Speech recognition software transforms physicians’ or patients’ voice input into consistent written reports, appointment summaries, treatment plans, mood journal entries, symptom summaries, etc. Then, this data is uploaded to EHR, a patient or RPM app, or other target software.

Appointment transcription

During an in-person or online appointment, voice recognition software can differentiate between the physician’s and the patient’s voices and create an accurate visit record. Later, doctors can use this data to create appointment summaries, and patients can revisit the received medical recommendations.

Virtual assistants

Doctors use speech recognition-powered virtual assistants to schedule appointments, tests, and diagnostic procedures, create and retrieve health records on the go. Virtual assistance also helps people with motor and visual impairments to use patient-facing software like telehealth solutions, mental health apps, etc.

Architecture

Below, we present a high-level architecture of speech recognition software that can be adapted to fit the needs of your specific project.

An automatic speech recognition (ASR) engine transforms voice input into text. Then, a natural language processing (NLP) module helps interpret the voice data by using:

Semantic analysis that helps adjust the ASR-generated text based on the context and make it cohesive.
Named entity recognition (NER) technology that detects certain entities within the text (e.g., a person, a health organization, a condition) and checks the text against publicly available knowledge bases (e.g., Unified Medical Language System) to generate a health record.
Intention detection that identifies voice commands and sends them to the software business logic for execution.

The NLP module is connected to a terminology service featuring various medical terms, popular abbreviations, etc. The voice recognition software may be powered by an additional machine learning module to improve speech recognition quality or adjust to specific speech patterns and accents.

ScienceSoft’s hint: If you want to transform your speech recognition system into a full-fledged AI medical assistant, we suggest implementing a text-to-speech module. The solution will convert the textual response into spoken words and will improve user convenience (especially for those with visual/motor impairments).

Alena Madden

Senior Business Analyst and Healthcare IT Consultant

Back-end or front-end speech recognition?

There are two types of speech recognition: back-end and front-end. If you opt for back-end speech recognition, spoken words are recorded digitally, transformed into text, and should be proofread by a medical transcriptionist or a doctor before being entered into the system. In the diagram above, we presented the architecture of front-end speech recognition software. It converts spoken words into text in real time and eliminates the need for medical transcriptionists. At first, there may be slight errors, so I recommend medical staff to correct transcription errors immediately after input. With time, ML-powered front-end speech recognition software learns its users’ speech patterns and becomes more accurate.

Features

Dictation

Patients and clinicians can dictate notes; the software transforms audio data into text.

Automated appointment summaries generation

After turning the audio input into text, the software uses natural language processing to identify the relevant medical information and create appointment summaries.

Voice-enabled commands

Patients and clinicians can control the software by giving voice commands (e.g., to schedule an appointment or create a treatment plan).

Voice patterns recognition

After a short training period, AI-based speech recognition software adapts and recognizes physicians’ or patients’ unique voice patterns to create accurate records.

Data encryption

All speech-related data is encrypted in transit and at rest to ensure end-to-end security.

ScienceSoft’s Speech Recognition Projects

Security Assessment for a Major Healthcare Speech Recognition Software Provider

ScienceSoft verified the IT infrastructure of a speech recognition software provider reconice against data security vulnerabilities. To ensure ePHIs remain uncompromised, we conducted black box pentesting of the voice recognition app used by 500+ healthcare organizations.

Development of an Intelligent Voice Command Interface for the Automotive Industry

ScienceSoft developed an intelligent voice command interface for a complex solution that allows vehicle owners to communicate with their vehicles and perform remote car electronics control.

Accessibility Features Implementation for a Desktop Communication Application

ScienceSoft augmented a cloud-based business collaboration tool with new accessibility functionality, including text-to-speech and voice recognition. Now, the application is accessible to people with dyslexia or visual and motor impairments and can be easily used on the go.

Entrust Your Speech Recognition Project to AI Pros

Working with AI since 1989, ScienceSoft knows how to build an accurate and secure medical voice recognition app. We are ready to assist you at every project stage: from software design, development, and QA to support and evolution.

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Technology Elements

Alena Madden

Senior Business Analyst and Healthcare IT Consultant

If you require exceptional speech recognition software accuracy, we recommend using GPT-4-like LLMs (Large Language Models). We implement such solutions using OpenAI API or similar open-source alternatives for semantic analysis.

How to Tackle the Challenges of Speech Recognition for Healthcare

Challenge: Medical language specificity, speech accents, and the variety of language patterns may cause transcription errors.

To prevent errors, ScienceSoft recommends adding dictionaries for medical specializations to the software terminology service. You can also use autocorrection suggestions to help users fix word identification errors (e.g., claustrum vs. colostrum). Also, when a speech recognition solution detects a critical amount of speech disruptions, it can send instant reminders to physicians and patients, asking them to reduce the noise level, move closer to the microphone, etc.

Challenge: Speech recognition solutions are often costly and take long to implement.

To reduce the time and costs of implementation, ScienceSoft suggests using open-source speech recognition engines like Google Cloud Speech-to-Text, Azure Speech-to-Text, Dragon APIs, and IBM Watson. Even though you will have to pay subscription fees for API use, it will help you launch the medical software and start getting benefits from it much faster. Plus, if you are building a medical speech recognition product, you can include the voice recognition tool cost in the subscription price of your product.

Challenge: It’s hard to reliably protect the data that goes through speech recognition engines, especially third-party ones.

We recommend implementing security best practices when designing your voice recognition software. For example, in our projects, ScienceSoft does not store raw voice files after the transcription, uses secure API communication protocols, and keeps the encrypted transcriptions in a secure database. We also provide continuous system monitoring and run regular security tests after the software launch to ensure the confidentiality and integrity of healthcare data.

How Much Does It Cost to Build Healthcare Software for Speech Recognition?

Based on ScienceSoft’s experience, the cost of a speech recognition solution ranges from $80,000 to $250,000+ for a system powered by open-source tools and an advanced product with multiple integrations respectively.

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Key cost factors we consider:

	The complexity of the medical speech recognition software.
	Subscription fees for the ready-made speech recognition tools.
	The number and complexity of required software integrations (with one or several EHRs, a patient app, etc.).
	UI and UX requirements, accessibility features.
	Security requirements and compliance-associated costs.
	Support and maintenance costs.

Launch Healthcare Speech Recognition with a Reliable Partner

With over 100+ successful projects for the healthcare industry, ScienceSoft helps software companies and healthcare providers implement state-of-the-art speech recognition technology and steer clear of potential risks.

Speech recognition consulting

Need professional assistance to implement speech recognition? ScienceSoft’s healthcare IT experts can suggest the optimal technology and architecture, guide you through project planning, and assist with risk management.

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Speech recognition implementation

ScienceSoft’s team is ready to implement speech recognition for your healthcare initiative. We can take charge of the entire project – from design to development, testing, support, and maintenance.