NEW YORK, Feb. 23, 2016 /PRNewswire/ -- Arria NLG (LSE: NLG) Arria NLG's Professor Ehud Reiter, Chief Scientist at Arria NLG, will be discussing ways in which Artificial Intelligence can impact the healthcare ecosystem at the IBM Watson Health event on Thursday, March 3.
Leading organizations around the world are collaborating to address the intersection of healthcare and how artificial intelligence, and emerging technologies such as Natural Language Generation (NLG) have the power to improve the existing healthcare ecosystem.
NLG is a form of artificial intelligence software, specialized in extracting information from complex data sources and communicating that information in natural language (i.e. as if written by a human). This technology provides healthcare with intelligent reporting assistants to communicate what the data is telling us in a quicker, more efficient and user-friendly way. NLG technology can empower healthcare professionals and patients with new insights about the patient's health and treatment.
Three key NLG uses Professor Reiter believes could transform the current system include:
- Empowering patients
Every healthcare system in the world knows that the best way to improve health is to encourage patients to do more for themselves, including following healthier lifestyles, better self-management of chronic conditions, and good compliance with treatment regimes. NLG is a key tool in empowering patients, as it can explain complex information in an understandable and sensitive manner. For example, many diabetics have sensors which measure blood sugar levels, but they struggle to use this information to manage their diabetes because they often don't understand it, and can overreact and indeed panic when they see their blood sugar change. An NLG system can explain and contextualize any changes in blood sugar and help diabetics respond appropriately.
- Empowering clinicians
Currently clinicians spend a great deal of their time writing routine clinical documents such as referral letters, radiology reports, shift handovers, and discharge summaries. This is inefficient (we want doctors to be looking after patients, not writing reports). Furthermore, human-written reports may contain mistakes and be written in different ways by different clinicians; poorly-written reports can also increase legal liability and regulatory risks. It's much more efficient to get an NLG system to generate draft reports, and ask clinicians to review these reports and add any key medical insights.
- Improved decision-making
NLG can also be used to support clinical decision-making. In a research project called Babytalk, NLG software generated summaries of electronic patient record data for doctors and nurses in a neonatal intensive care unit (NICU). Clinicians in the NICU could see graphs of patient data, but the hospital was concerned that some clinicians were not effectively using these graphs. Experimental work suggested that NLG-generated written summaries of data could be a useful addition to standard data visualizations, for both real-time decision-making and longer-term care planning. In particular, the NLG summary texts could prevent mistakes by highlighting important information that was not obvious from the visualization.
Insights about the individual, developed through the analysis of data, can help identify risk factors, promote health and drive more effective, early engagement from the entire community of care. But there is a growing shortage of analytical talent.
With the use of NLG, clinicians can make more effective usage of data, reduce the amount of time they spend on writing up reports, and reduce the risks of mistakes due to information not being passed from one clinician to another. Patients can now have a personalized summary of their medical condition, in a language they can understand, which is oriented to support key decision-making. With the understanding that NLG provides, patients will feel empowered and in control, and ultimately more satisfied with care.
Professor Reiter will be speaking about the best use cases for NLG, and what it could mean for the healthcare ecosystem, as part of his lecture.
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SOURCE Arria NLG