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Continue LogoutNatural language processing (NLP) techniques allow digital systems to streamline user interactions, allowing machines to read text, understand meaning, and generate narratives from existing information. Recent advances in artificial intelligence (AI) technologies have accelerated progress in a broad range of NLP applications for health care, including digital assistants for clinical staff, concierge services for patients, and digital scribes to streamline documentation processes.
How natural language processing will help achieve a payback on EHRs
For example, last fall, Nuance Communications released its Dragon Medical Virtual Assistant to help health care providers interact with clinical workflows using NLP and other conversational AI functionality. Nuance announced at HIMSS18 that it will integrate its virtual assistant technology into Epic's EHR.
According to the press release, the new partnership allows physicians to use the virtual assistant to ask for information from a patient's chart, retrieve labs, medication lists, and visit summaries using Epic Haiku. Nurses using Epic Rover can use the assistant to conversationally interact with flowsheets to enter and confirm patient info and vitals. Finally, scheduling staff using Epic Cadence can converse with the assistant to check physician schedules and create or modify patient appointments. And Vanderbilt University Medical Center recently announced it is leveraging Nuance's technology to build a prototype voice assistant called "V-EVA" (Vanderbilt EHR Voice Assistant) to help caregivers navigate the hospital's Epic EHR using natural dialogue.
A number of other health care providers have started piloting voice assistants. For instance, Northwell Health is testing Amazon's Alexa across multiple use cases, including one that helps users determine the wait time at nearby EDs and urgent care centers in Northwell's system. People can ask their Alexa-enabled home devices to either search for the shortest wait time based on their zip code, or can ask for the wait time for a specific location. Once the user asks for this information, Alexa queries Northwell's database of wait times (which analyzes check-in data every 15 minutes) for the best option. The Alexa feature can respond back with the location's name, address, and wait time.
In another example, the University of Pittsburgh Medical Center (UPMC) is collaborating with Microsoft to create an intelligent scribe called EmpowerMD. The project is part of Microsoft's Healthcare NExT initiative, which aims to use AI to accelerate health care innovation. The virtual scribe listens to conversations doctors have with patients, analyzing speech for clinically relevant concepts to make suggestions in the medical record. The goal is to allow doctors or other staff to engage with patients face to face, without the need to divert their attention to a computer screen. The scribe can make suggestions or take notes for follow up, which the doctor accepts or modifies after the encounter. Staff can also view a transcript of the conversation for greater context on the assistant's suggestions. Using machine learning, the virtual assistant improves its performance as suggestions are accepted, rejected, or modified by the user.
Patients are also interested in AI-powered virtual assistants. Accenture recently released the findings of its 2018 Consumer Survey on Digital Health, which polled 2,301 U.S. consumers on topics such as wearables, virtual care, and AI. Among the findings, the survey showed that roughly one-in-five consumers has experienced health-related AI, and, in particular, showed an openness to using intelligent virtual assistants. Specifically, the survey found that:
Is your team interested? Here are three key considerations to get you started:
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