Just Say It, a solution to your EMR data entry dilemma
For as long as I can remember (and that’s a very long time), clinicians have been complaining about the burden of entering data into the EMR. For most of us who started practice when medical records were primarily on paper there were basically two modalities for documenting our work. One was the pen. It worked pretty well for SOAP notes and brief encounters. I even recall documenting full physical exams on paper. The problem with writing things down on paper was that my hand would get tired, and my handwriting was awful. Sometimes after a long day of patient care, even I couldn’t read my notes.
For longer or complicated patient exams we had the luxury of dictation. In fact, I worked in several organizations where all of my notes were dictated and later transcribed by professional transcriptionists. Then along came the EMR, and the expectation that our clinical notes would be entered using keyboard and computer. Thank goodness my mother made me take touch typing in high school. None-the-less, compared to the speed of dictation, typing patient data into the computer was a burden and a significant time sink to my productivity.
Of course, for many years there have been software solutions for voice recognition and today they are substantially better and more accurate than they used to be. They work best when the user “trains” the software to understand his or her own voice, and even the best software will miss words now and then. Also, because medicine has its own language you generally cant’ use just any old software for voice recognition. You must buy speech recognition software written specifically for medical professionals, and it was usually quite expensive.
When I went to work for Microsoft, it always bugged me that our own very robust speech recognition engine, the one that comes free with Windows, couldn’t easily be used for medical dictation. Therefore, clinicians had to install expensive software that used an entirely different speech engine. Because Windows didn’t have a lexicon for medical terminology that worked with our Windows speech engine, that’s just the way it was. That is not the case anymore. Thanks to some innovative partners and the advent of cloud computing, there are now some excellent, cost-effective solutions for clinicians who are ready to ditch the keyboard and start using their voice to enter EMR data. One of those solutions is SayIt, by nVOQ.
This year at HIMSS we demonstrated the nVOQ SayIt solution on our Surface Pro 3 tablets. This marriage of a great, clinical grade device connected to a robust, cloud-based medical speech recognition solution is guaranteed to delight clinical end-users.
I’ve gotten to know the crew at nVOQ over the past few years. In fact, one of their executives is a former colleague of mine here at Microsoft. nVOQ has been working tirelessly to bring forward and continuously improve a medical, speech recognition solution that is suddenly gaining a lot of traction in the market.
SayIt is a cloud-based speech recognition solution that converts spoken words into text within seconds. It can be used for free-form dictation, front-end transcription and for navigating EMRs using voice commands or shortcuts. SayIt supports 35+ medical specialties and works with virtually any EMR (PC and Mac-based). The solution enables healthcare providers to:
Spend less time on clinical documentation, more time with patients
Take more detailed notes for clearer assessments
Document care in their own words for improved patient outcomes
Simplify clinical workflow for time and cost savings
If it has been awhile since you tried speech recognition for data entry into your EMR, and particularly if you haven’t tried a cloud-based solution, I’d urge you to take another look. I think you’ll be surprised by how much the technology has improved. I also think if you treat yourself to a “clinical grade” device like our Surface Pro 3 that you just might begin to love, rather than loathe, your EMR.
Bill Crounse, MD Senior Director, Worldwide Health Microsoft
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