Editor’s note: Relying on ScienceSoft’s 15-year experience in software development for healthcare organizations, SaaS companies, and medical device manufacturers, Alena describes the possibilities of speech recognition technology in the healthcare industry and shows how to avoid its main implementation risks. If you are interested in opportunities voice recognition functionality can bring to your medical software product, you are welcome to turn to our medical IT experts.
The accuracy of modern voice recognition technology has reached 99%. Being so technically mature, it becomes increasingly popular among care providers as it yields impressive results. For example, the majority of the respondents noticed an improvement in documentation quality and completeness as well as a decrease in time spent on documenting a patient encounter after implementing a speech recognition solution.
Before these numbers make you rush into creating your own speech recognition solution or adding speech recognition to your healthcare product, I’d like to walk you through the essentials of this technology and make sure you understand its specifics and are ready to face possible (but completely manageable) implementation difficulties it can pose.
There are 2 types of speech recognition (SR), which can be used separately or together in your mobile or web application.
Back-end speech recognition
With back-end speech recognition technology, spoken words are recorded into a digital form and then translated into text. The system sends a generated draft document with a voice record for a proofread to a medical transcriptionist or a doctor. This type of SR technology is popular among healthcare organizations because generated texts are edited by a person, which makes them more reliable for drawing up healthcare documents and EHR records.
Front-end speech recognition
Front-end speech recognition software converts spoken words into text in real time. With this type of voice recognition technology, you can eliminate the need for medical transcriptionists. However, front-end speech recognition technology can first deliver results with slight errors, so medical staff needs to correct transcription errors immediately after input. With time, front-end speech recognition software learns speech patterns and further avoids similar errors. Therefore, a front-end SR solution is better suited for taking personal notes (errors are not as critical here as in EHR records) or short medical reports with typical formalized phrases (for example, “normal findings”, where the possibility of error is minimal).
SR increases medical staff’s productivity
ScienceSoft’s customers from the healthcare industry note that the majority of their doctors spend too much time on typing patient notes, reports, etc., which reduces their productivity. With voice recognition technology, medical staff can free up time spent on data entry by voice-typing their documentation on the go and focus on patients more. And increasing the productivity of medical staff, SR helps provide medical care to more patients, thereby speeding up the cash flow to healthcare organizations.
SR facilitates the completeness of medical documentation
When creating a medical record with the help of a voice recognition solution, the possibility to omit some important information during a visit is minimized. This is especially important when numerous medical services need to be documented within one appointment. This, among other benefits, facilitates cooperation with insurance providers.
SR inspires patient engagement
Speech recognition can be a crucial part of voice-powered assistant solutions for patients. Within such solutions, speech recognition technology can increase the level of patient engagement in their treatment process outside the care facility.
For example, patients can ask a voice-powered nurse assistant (which can be connected to medical devices) to provide information about their disease, record their symptoms, or consult on general issues. Thus, speech recognition technology makes such activities more comfortable and time-efficient. As a result, patients are likely to be engaged eagerly in their treatment process.
High cost and long duration of implementation
I suggest using open-source SR engines like Google Cloud Speech-to-Text, Azure Speech to Text, Dragon APIs, and IBM Watson to reduce the implementation time and costs for voice recognition technology. Also, to get value early and with minimal risks, you should carry out thorough product road-mapping and feature prioritization.
As the information transmitted to EHR is confidential and should be protected, the issue of HIPAA compliance is acute. Based on ScienceSoft’s experience, I recommend designing your SR-powered product with security in mind adding security features (for example, a single voice profile encrypted in the cloud) that help minimize risks of data leakage. Also, I suggest providing continuous system monitoring and running regular security tests after product launch to ensure the confidentiality and integrity of healthcare data.
Medical language specificity, misspelled or missing words, user accents, and a variety of language patterns may cause errors in healthcare documents, reports, and notes generated with speech recognition technology. To prevent them, you should extend product functionality through:
- Adding dictionaries for medical specializations.
- Using reminders for physicians to speak slower or clearer when a speech recognition solution detects a critical amount of disfluencies during speaking to minimize the possibility of misinterpretation.
- Using semi-autocorrection, which allows users to quickly and easily correct spelling errors.
I believe that speech recognition technology offers great opportunities for care providers and patients. SaaS companies and medical device manufacturers should not ignore this profit opportunity despite possible implementation challenges on the way. So, if you consider enriching your product with speech recognition technology, ScienceSoft’s team will be glad to help you.
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