Does speech recognition have a future in healthcare?

Speech recognition is everywhere. It’s iour smart phones, voice assistants and automobiles, helping man and machine communicate in a natural way, by essentially, capturing any spoken words and converting them to text.  

But does speech recognition have a future in healthcare? 

Yes. Speech recognition technology has improved vastly over the last few years in its ability to understand medical terminology and regional accents. 

recent report from the NHS states: “With recent advances in SR (speech recognition) algorithm design and system performance, this technology now presents a valuable tool for clinical documentation – the benefit for the healthcare workforce to focus on patient interaction and care rather than the computer screen and keyboard is clear. This is likely to have a major impact in primary care, as well as outpatients and emergency departments in hospitals.”  

We’ve also seen how speech recognition can help provide early diagnoses for Parkinson’s Disease, hinting at the vast range of clinical use cases it could apply to. 

While such use cases show great potential, next-gen speech recognition software is already helping medical institutions improve their operational efficiencies and reduce costs. Let’s examine the future of speech recognition for healthcare in a little more detail now. 

What is speech recognition for healthcare? 

Speech recognition for healthcare is broadly divided into two areas. Firstly, we have front-end speech recognition. This effectively happens in real-time, where clinicians can create and review documents with their voice. 

For example, when creating clinical documents, clinicians can efficiently produce correspondence and navigate or fill forms in by simply using their voice. Clinicians can also dictate and store a patient’s data directly into a computer system, saving them precious time. 

When reviewing clinical documents, clinicians can review and sign a document using their voice too. For example, a clinician could electronically sign and distribute patient documents in real time or send them to clerical team for editing. This can be done across multiple platformsdevices and use cases – making each record highly accessible. The instant creation of computer-based media files also ensures that medical staff can follow standardised procedures with ease. 

Secondly, we have back-end speech recognition. This is primarily used as a transcription aid, when a clinician is recording their speech. The recordings are then transcoded and can be sent to the appropriate platform. 

Here, transcribers receive pre-processed text that includes command-based events, standard text and patient-based information. Thanks to out-of-the-box transcription workflows, the turnaround time to transcribe these documents is reduced, and the clinician’s administrative workload is also reduced thanks to voice enabling your EHRs.  

For example, clinicians can utilise T-Pro Speech to dictate directly into any text field within your her, or leverage the T-Pro Speech SDK to embed the speech interface in any platform. Clinicians can also configure their commands to speech-enable their existing workflows. 

A speech recognition example 

As we can see, speech recognition can be applied to a wide range of clinical use cases. But let’s look at a common mobile and digital dictation example in more detail. 

Let’s assume that for every patient interaction in your hospitala detailed medical record must be produced. Many institutions rely on analogue tapes and Dictaphones for this. But these are resource-intensive and highly inefficient processes with many limitations, including tape breakages, missing tapes, lack of backup options and diminishing tape quality due to increased wear and tear.  

From an operational perspective, it is also difficult to prioritise and share workloads between stakeholders when you rely on these analogue solutions, making it difficult to get information on the status of the document. 

Speech recognition for healthcare

As a result, there are often significant delays in patient discharges due to delayed documentation, leading to an increase in the Average Length of Stay (AVLOS), a metric against which many hospitals are measured for effectiveness, causing significant bed blockages, delayed admission for sick patients and loss of reputation. 

Speech recognition solutions can address all of these challenges, and more. For example, T-Pro recently worked with University Hospital Kerry to help their clinical teams improve their correspondence and document turnaround. We replaced their analogue tapes and Dictaphones with the T-Pro solution. 

As a result, the hospital massively streamlined its administrative processes, eliminating overtime secretarial costs and completely removing the need for temporary cover when its secretaries are off sick or on annual leave. The hospital also reduced its turnaround times for reports and patient notes, allowing it to increase the number of clinicians without increasing its secretarial resources. 

Security and accuracy challenges 

However, many challenges remain for speech recognition in healthcare, mainly with regards to the security and accuracy of such solutions.  

Security is a primary concern for any technical solution in a clinical setting – and speech recognition is no exception where patient records and other sensitive information must be protected. 

T-Pro Dictate comes with a comprehensive range of appropriate technical safeguards to ensure confidentiality, integrity and security of patient information/medical records. It also uses a secure cloud-based application, providing accessibility and solutions such as encryption to protect patient information. 

Accuracy is another concern as the right information must be recorded to prevent potential errors. Plus, clinicians do not want to waste their precious time repeating themselves.  

For T-Pro Dictate, we incorporate next-gen speech recognition technologies to guarantee the accuracy of our speech recognition software. For example, our models are constantly adapting and improving based on dictation from over 50,000 clinicians, reducing errors.  

Plus, our Natural Language Processing technology provides clinicians with real-time feedback. To further streamline the process, dynamic model assignment also reduces the need for profile training. 

So, does speech recognition have a future in healthcare? 

Speech recognition is breaking into the mainstream medical industry, thanks to the diverse range of use cases and benefits it brings, especially when compared to inadequate analogue solutions.  

The prognosis is certainly looking good for next-gen technologies like T-Pro Dictate, which provides the security, accuracy and cost saving benefits your organisation requires. If you’d like to find out more about T-Pro Dictate and how it can transform your institution, contact us today. 
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