The development of speech-enabled commands and searches have been a budding trend in the electronic medical records sector. Health IT company M*Modal has developed an algorithm that responds to voice commands to search unstructured data in electronic health records that offers a less cumbersome way for physicians to access the unstructured data to find the information that they would have to painstakingly look for manually.
In recent years , the company has rolled out the technology in pilot programs with electronic medical records vendors. This year at HIMSS13, in a collaboration with Intermountain Healthcare, it has wrapped the cloud-based natural language system software into a computerized, physician-order entry mobile application for the health system that includes 22 hospitals and 185 physician clinics.
Here’s how it works: Users select the patient, describe information on the prescription — including dosage, frequency and qualifiers — and then send the request to an electronic health records order entry system. In addition, it can be used to order CT scans, requests for nurses to check catheters and vital signs as well as testing.
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“It’s interesting to see how even Intermountain and Mayo Clinic, and MD Anderson and Geisinger, who have put a lot of effort into building their own solutions, are now adding on these other services because they cant’ do it all [themselves],” said Juergen Fritsch, M*Modal chief scientist.
In an interview with MedCity News, Fritsch said there are a few different areas where natural language-processing technology could improve efficiency and accuracy in patient care, particularly to prevent overbilling.
- Identifying care gaps: By looking at the patient’s history and digging through unstructured data, it can be used to reveal where there has been insufficient care and where physician follow-ups did not happen.
- Improve work flow: By having the natural language app embedded in the work flow, it can avoid additional steps and reduce disruption.
- Help comply with meaningful use requirements: It can do this by creating a faster way to do e-prescribing and exchange data.
Other companies that have developed natural language systems to access unstructured data in EHRs are QPID and Reveal.
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